Classic Google: Clicks the "Try in Gemini" button from the Gemini 4 page, gets a page that defaults to Gemini 3.6, with no option to try Gemini 4 without upgrading.
If they don't want to make it available even for small tests for free, that's fine, but then don't put a button suggesting I can try it without qualifying it...
EDIT: It's actually worse: The page then shows "Look what you can do with Gemini 4" and a "Let's go" button that leads to a bunch of links to examples, all with enticing pictures. Except if I click on them I get a box stat strongly suggests it'll run with Gemini 4, except the dropdown shows "Flash" and if you click on it, it shows as Gemini 3.6 again, and if you run it, the results unsurprisingly end up looking nothing like their pictures.
I don't know if that page was built by humans or AI, but clearly nobody tested it properly.
Ha, same experience here. It opened my Gemini and gave me images like “Design a terrarium with Gemini 4” - proceeds to pick 3.1 Pro from the picker because 4 just doesn’t exist in the dropdown. Typical Google.
I really can’t believe how bad the Gemini experience is. I have personal paid workspace account, and meant to have some better AI Google features I think.
So, I connected my Gmail to Gemini. 3.6 Flash kept telling me it doesn’t have any Gmail connnection, and then proceeded to hallucinate instructions for me to enable it, all bogus. At least 3.1 Pro realized that my Gmail is connected and then searched.
I have $6 free credits from Google. The hoops you have to jump through are insane. $200 free credits from Anthropic are stupid simple to use. I know which one I’m using…
I haven't tried it for coding yet, but I ran a couple of my recent curiosity driven queries from Claude (mostly used Fable and Opus 5.5) through it.
This is a clear step change, I'm genuinely amazed.
Its answers are to-the-point, logically structured, clearly written. Like a really well made mini essay on your question.
It also really likes to build little simulations / models that you can tweak the parameters on. Amazing to explore how different assumptions affect economic scenarios, for example.
It's also great at picking (sourced) illustrations.
I'll be using this for everything that isn't coding from now on.
It’s wild how good it is at explaining nuanced and complex health topics.
As an example, I recently had an OCT scan of my retina because I'm a “glaucoma suspect.” I'm a glaucoma suspect because I have an unusually high cup-to-disc ratio, which is a pattern typically seen in glaucomatous patients.
As part of this OCT scan, you get a report back on your RNFL, which is basically a report on the health and thickness of your optic nerve(s) and retina(s). (Dramatically oversimplifying, of course.)
My ophthalmologist struggled to explain my OCT scan results to me and instead sort of shrugged and said, "It's probably because you're a high myope, but we'll keep you coming in once a year.”
But my scans look horrifically bright red — meaning I'm in less than the first percentile of retinal thickness for my age. Obviously concerning enough to do my own research…
ChatGPT 6 Pro and Opus 5.5 have struggled to make any sense of the clinical implications of these scans. In contrast, Argon was the first model to:
1. explain how the OCT machine works
2. detail the physiological mechanism that causes the cupping and thinner RNFL layers in my scenario
3. sum it up into a clinical recommendation (similar to what my ophthalmologist recommended, with the addition of basic supplementation and lifestyle modification changes)
I'm not the poster you responded to, but I can tell you that historically the Gemini models have lagged behind Anthropic (the clear leader) and OpenAI's best models where coding in particular is concerned. Opus 5.5 is, right now, the consensus best coding model.
That said, I'm extremely impressed with Argon thus far. I take back everything bad I've ever said about their AI strategy. Argon is very good and very fast. It might be competitive at coding, but it's too early to say.
I completely agree;
I was really surprised to find that Claude Code Opus 5.5 is significantly better. I had spent quite a bit of time using Gemini Pro for coding tasks, but when I revisited the code analysis using Opus 5.5, it revealed quite a few shortcomings in Gemini's understanding...
I code using Python, which is considered the best language for writing code via LLMs (I am referring to linguistic entropy).
I am using it via OpenRouter for going through some Vulkan code. It's a beast - it has no hesitation putting in instrumentation within the code (having a number of render passes) to dump the framebuffer to PNG files and doing pixel-by-pixel comparisons, putting in false colors in shaders to identify bugs etc. Very impressive.
Out of curiosity, which harness do you use for this kind of task, and how do you sandbox it? I used Aider before, and consider switching to a more recent tool.
I started out with agy CLI but it's horrible because of its very indeterminate context compaction. But I live with it because it's the only way to use Google's subscription. I was and still use Opencode in parallel (accessing Openrouter ZDR endpoints) when I run out of rate limits / weekly limits.
Over the past two weeks, I have started using a locally modified version of deepseek-harness (modifications include disabling telemetry and querying a provider's model list dynamically through Openrouter, instead of relying on the pre-packaged pi-ai manifest that comes with the base code). So far, that has been the best experience - I really like the caching, tokens/sec stats that it provides. (EDIT: I don't know a lick of TypeScript but AI-driven modification of the harness is good enough for me to make these tweaks).
Sandboxing is a definite issue for me. As you perhaps know already, headless operation is often not an option in graphics/game programming which requires actual render-surface instantiations from windowing systems and then input-context handling (keyboard/mouse/gamepad) as well. So I run most development on a VM. But part of my test-suite and smoke-tests rely on runninng it directly on the host OS (which is not ideal). I'd love to hear suggestions to avoid this.
With 3.8 Flash on High I noticed a huge upgrade in intricate discussions related to physics, material properties, real-world application, and so on. Things that I've had a hard time getting LLMs to be able to discuss accurately. They often discuss textbook knowledge fine but can't apply it properly or can't think outside the box at all. 3.8 High understands applied material sciences in a way that GPT Astra flunks at often. I asked Gemini to explain that result, and it said basically that GPT was merely book smart in a classical sense and that it was likely undertrained on more obscure material.
We have this same point made every release. I wonder if models actually are getting better or if these companies are just secretly hamstringing the previous models before releasing a similarly performing model
I think it takes a while to start finding the weak points in a model. It seems fantastic until you use it enough to find out that it hallucinates APIs, or can’t count letters, or uses too many em-dashes, etc. you don’t really know someone (or something) until you’ve lived with them (or it).
I think there's definitely some quantization or nerfing happening post-release -- I definitely noticed that with Gemini Flash 3.7, it was super fast and accurate at the start and a few weeks later seemed to run in circles and overthink even a "what are you doing" message, all while doing almost identical work. I find it weird that we're so far into the LLM era and the post-launch degradation is still something of a conspiracy theory.
I'm using this model via API. I am most excited about its ability to write the english language (or any language). My least favorite trend on twitter has been people posting 2D illustrations/3D renders as a proxy for model capability. Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I've been trying it out via the API and the model has been a delight so far. Clear in the head, not RL-fried, distinct writing style, and big model smell. That said, the em-dash is back w/ this model.
That said, I would be so frustrated with Sundar Pichai if I was on the Google team. The botched rollout of this model seems to be entirely a matter of capacity, and Pichai has been more than happy to give it to anybody but his own team.
I've been using it for a while internally at Google. I prefer Argon over Opus 5.5 because of the clear thinking and directness. It phrases things in an organized, straightforward way.
Interesting. My favorite LLM output ever was the reasoning traces in gemini 2.5 pro. I found them much easier to understand than the "final output", because they were very structured and concise. Of course, they ended up hiding the reasoning, not long after that, like everyone else... sad!
Opus 5.5 has the personality of a proud righteous nerd, definitely incomprehensible at times, unless you explicitly tells it to slow it down, it crams information far too dense.
Great model to architect and resolve eng. problems in a harness loop tho. Poor handoff.
Yes, exactly! Maybe not sarcastic but definitely using jargon and being opaque like we all know it, it’s just hinting. Feels like talking to someone who’s constantly trying to prove to me they are smart(er).
Agree I do not like the way Opus 5.5 communicates with me. It feels like it is trying to act superior in a condescending way. DO NOT LIKE. I have not used Argon yet but Gemini 3.8 Flash has been a joy to use in comparison (and so fast!) as a daily driver.
I have used it as well. It is very dense, but it cites with evidence and has a direct and straightforward approach and manner. It doesn't really have a "tone" or mannerisms the way Opus 5 did. It follows instructions and sticks with pretty clean language by default. Argon is solid and handles long horizon well.
Em dashes are commonly used in most countries that speak English. I get the feeling that people from the USA don’t use them often and now have tarnished the use of them.
Do you remember when the AIs were all writing with bullet points all the time? I miss that! I'd prefer that to the current more-information-density-than-any-human-would-ever-muster approach where sentences are packed so deep you can't get any meaning by skimming.
I have a pet theory that there's some overlap of writing patterns that A) indicate quality, clear communication and B) those that the LLMs have been converging on. As a result, we're ruining the reputation of some great writing patterns because we've associated it with AI slop.
My pet theory is that this was happening, because labs naturally worked to source highest quality data sources they could get. Even if they initially slurped everything indiscriminately, the obvious next step is to sort them by "quality", and bias towards the high end while trimming from low end.
So obviously, government documents, top publications from liberal arts scholars and STEM > Wikipedia > Reddit (and within, r/AskHistorians > r/all, etc.). The first group is the one most likely to (over)use correct / fancy typography.
I have a related pet theory: the telltale sign of AI writing is being too good for the context it's found in, in terms of vocabulary and style as well as structure.
What is jarring about this is precisely the quality - we associate certain styles with important (or self-important) venues, so it's uncanny to suddenly see random shitposts, copywriting spam, or regular chat replies look like work of NYT bestselling authors or legal scholars - it feels wrong in places we expect basic language, grug-brain reasoning, and lots of typos.
Also, what makes the above much more noticeable, is that AI systems don't have cross-session stylistic memory. From the POV of an LLM, that perfectly fitting, masterfully crafted starter is the first and only time it used it. From the user's POV, it's the tenth time they're seeing it in the span of an hour.
There's also no voice. If I get a text from a friend, I can hear them. Their tones, their mannerisms. I can't hear Claude because it's not a person that I've met and interacted with.
I didn't say it's copying anything. It's learning styles - all of them - in pre-training. Post-training is just about giving it the right defaults.
Doesn't change my point anyway - even if post-training, labs naturally want the LLM to come off as smart (well, except for Grok). Post-training is where they "untrained" em-dashes and such, due to complaints of users.
I did at the beginning, but there is only so much you can take, and no one cares if you have read books and seen it before. There were a bunch of programmers whom I thought would understand em dashes, but they did not and said no one uses such dashes only normal hyphens.
Em dashes ended up as one of the first victims of llm age
Em dashes were already "doomed" at least half a century ago, because they didn't make it into ASCII character set - which arguably is just because they were seen as redundant on ancient typewriters; that's also why they aren't there on normal keyboards, which was a double whammy: inconvenient for programmers to support, inconvenient for users to type.
In other words, since the very start of computer era, the use of em dashes was always a signal.
They were in models in the first place because they are used ubiquitously published formal writing. As, of course, are semicolons. There is nothing wrong with using punctuation for nuance and structure.
I think they just used them too much. I suspect that as they naturally form a thematic break point in writing, their probability was unnaturally high at the end of phrases and this led to overuse.
I don't ever recall seeing an LLM using them incorrectly, as opposed to simply at high frequency but correctly (although I'm sure some low param or early models must have done so at some point).
can confirm this. I didn't have high expectations for AI writing good long-form content in Indonesian. However, I recently tested it on some of my Indonesian writings and asked Argo to rewrite them while maintaining the original tone—and it absolutely nailed it.
> Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I'm not entirely sure you're correct. Your claim boils down to "Writing natural human prose" vs "Broad use of complex software and spatial awareness". My gut feeling is that there's more untapped and fruitful applications in the second group than the first - at least in the short term.
this happens to me as well, meanwhile Pangram claims their false positive rate is 1 in 24000 as if that should be practically negligeable. I bet it's closer to 1 in 10 for gen-x'ers
Announced the model two weeks ago to a ton of hype, model was unavailable for anyone to try. On twitter, Google folks went on a great run taking shots at Muse ("Muse who?"). The hype died down bc nobody could touch the models and meta/muse came out with a similarly capable model in the meanwhile.
At the same time, OpenAI had a disastrous dev day that led a lot of users to jump ship to Claude.
Couple weeks later, Gemini-4-argon is still unavailable on anything less than the ultra tier, which means there's no natural funnel to get ppl to pay for the more expensive ultra plan other than "trust us the model is good".
If Google had argon broadly available earlier this month they would have picked up a ton of new users.
As someone internal at Google I don't understand why there's so much criticism of Google models... but right now I'm using the next model beyond Argon, and Flash 3.8. In my personal Gemini app I only have access to 3.5.
Models move so fast now and Google is very cautious about releasing them.
I think google makes great models, my favorite models have been google models. My critisisms are with the infrastructure around the models (or lack of them),things like billing, access, changes to terms and limits, pricing etc. All of these have shown either inconsistency, lack of care, sometimes confusing offerings, and things that felt like rug pulls.
Please google, just make it easy for me to give you money and get what I paid for.
My complaint, and the complaint of many is not the models - they can be good to very good depending on use case.
What sucks, hard, is googles ability to deploy direct to devs and customers at scale when the deployment is not a longstanding Google project.
Case in point: I’m an ultra subscriber and see in my app right now an ad to try Gemini 4; meanwhile the only models I have available are 3.1 pro,3.6 flash, and 3.8 thinking.
Clicking the try gemini 4 link takes me to a demo prompt that loads 3.6 flash. Srsly
I won’t even start telling you what getting api key based access together was like a year ago.
I agree that reply was unnecessarily rude, but... idk man. I'm a huge cheerleader for Google [1], use 3.1 Pro via AI studio, I'm on a Pro plan... and I can't access this model? Not even a "4 chats per hour" type thing?? I mean, I have video generation access for some reason (no good for me), but Google's policies are baffling to say the least.
Ofc I know this isn't your fault in person, but I really wish Google would get its shit together.
...As a long time tech enthusiast I should probably stop being so delusional. Heh (cries internally)
1: lol I know who cheers for a trillion dollar company but I'd rather not cheer for OpenAI
There's nothing unnecessarily rude about my comment, they are literally unable to get their act together on product.
The top level comment blamed cheerleading but let's apply critical thinking for a second as product people and look at the funnel:
1. Potential users sees new release
2. Potential user takes time to install and set up Google harness (you already lost 90% of people here, the funnel better close quick!)
3. Potential user cannot use new model.
Then what? You hope they developed good will towards your model through this experience?
You stuck a two week wait in your funnel? Is that the definition of not having your act together or what?
We're on HN, how many of us have heard from Google staff this kind of stuff is a result of them growing so big that AI Cloud has to compete with Vertex Cloud, and AI cloud exists because Vertex Cloud needed 2 years to get OAI/Ant style API keys working and were losing dev mindshare in the meantime?
Because Google models are crap. They are on par with local shit models. Last time I tried they were unable to even hold a longer conversation, because they would enter loop and reiterate last answer regardless of the prompt etc.
Then basic mistakes (inferring wrong information from the given text, hallucinations...). Gemini is okay for some absolute basics and the rest of the work goes to actual frontier models.
How long ago did you try it? Gemini models seem extremely capable to me. I've been using them in ag for over a year and to me the results are great. switching to opus in ag doesn't produce any better results for me
All the power users are always jumping around by now and many hold the smallest subscription with all the models or at least two.
Narrating this as if it’s the game of thrones is a fun exercise, but it’s just becoming commonplace now that models jump ahead of each other on a weekly to Monthly basis.
> and many hold the smallest subscription with all the models or at least two
This was part of the issue, as stated in their comment. We couldn't jump around to try it for the reason that is wasn't made available to, as you say, "all the power users".
I'm still a codex man myself but lots of people feel like opus 5.5 just gets it for some projects in a way that astra doesn't. Especially given the fact that astra and sol 6.1 are often at capacity late at night during US hours, when lots of people want to run longer unsupervised projects while they sleep. There's a general sense I've felt reaching some comment threads that openai failed to react with something better than opus 5.5 and are rushing to release small tweaks to make people feel like they're still getting something new.
Sol is just hilariously slow. Even Luna is slow. It is unusable compared to Claude. I was pretty unhappy with Opus 5 and really gave OpenAI a shot for personal projects, but their quotas are just too small and their capacity issues in the last few weeks have just been untenable. No good. Opus 5.5 is just a very good model, very efficient with usage, gets things done at a reasonable speed. Sonnet 5.5 is also a very nice worker improvement. It is probably on par with Opus 4.6-4.8.
The sense I get from your comment and the GP comment is that OpenAI’s pricing changes alienated individual users for the sake of the enterprise market. I live in the enterprise market, and was quite pleased with the changes. We don’t really use Sol 6.1, just Astra.
$500/month for 40+ hours a week of Astra usage, with ultrafast mode available when you’re near the weekly reset has made our jobs so much easier. Our product has some sciencey bits as well, ands Astra kills it as a rubber duck in this area.
As a consumer whose 20x account got downgraded, I am a bit sad. I can’t justify $500 a month on personal AI spend. As an engineering manager, I could not be happier with OpenAI’s services. Also, Codex App Server is amazing.
Hey, account created 39 days ago. Did you really have to tag that on after all the other stuff? Could you make it any more obvious?
That said this thread is hilarious because it's just an open ai sales manual debating with an anthropic sales manual. It's funny, but it's not, because the truth is that you people are just shitting all over the commons. Someone needs to kick you out but that's not going to happen here.
:shrug: I spend a lot of those token make local models better, resurrecting old hardware to make it more accessible and usable. Frontier models and AI is cool. This is my lived experience doing real systems engineering projects. My real sales manual is: https://stealthgoat.com/deepswe-flash-next.html local models are kind of awesome now and everyone should be using them in addition to frontier models. I will build the stuff I want which includes optimizing local inference engines until they are at my hardware ceiling. I use this capacity in my house to do reversing, security research, and internal OpenClaw type stuff I find useful.
They are all very slow at the moment. Some tasks are taking hours to even days. These are DevOps related tasks in which I create hundreds of servers in an agentic way. It works. But it's slow. That's still annoying.
Gemini 4 is announced be available to Pro users in the Gemini app.
I mean, I heard rumors too that it would be ultra only, but that doesn't seem to be the case, unless you are specifically talking about antigravity?
Realistically, market adoption isn't going to be the result of terminally online people (like me, I confess) following things way too closely on social.
There weren't "rumors" that it would be Ultra only, that's what Google has announced two weeks ago. Happy to see they've went back on that, but I haven't seen Gemini 4 on any apps till now, and I'm a Google AI Pro subscriber.
edit: just a few hours after this comment I got access to Gemini 4 on the Gemini app. Yayy! :)
That's correct, I've misquoted it. Although, if I understood correctly, it got released to all plans together today right? Or did AI Ultra subscribers have access to it since the first announcement?
I went from using GPT exclusively to cancelling all my subscriptions with OpenAI.
'backlash' is underwhelming. They single handedly murdered 5.3 codex which was great by making it slooow and duuumb, and pushed people over to their new ones. 5.6 was a decent candidate but it was so slooooow.
And then double or triple the cost for Plus and Pro by nerfing their effective power? I never looked for an alternative faster. They really screwed the pooch.
Been quite happy with deepseek v4.1 flash, muse 1.3 and haiku 5.5.
"Disastrous" is obviously a loaded term, and it can be debated, but from what I collect, the reasons are
- Dots. People don't understand it. Their presentation didn't explain it properly at all. Additionally, many people thought it's stupid, and that the cutesy mascots are dystopian.
- Voice mode didn't work in their presentation. The demo failed.
- 200$ plan goes 20x -> 10x usage. 500$ plan introduced, 25x usage. The contrast is stark.
- "Ultrafast": 8x pricing on subscription plan (6x API). That's 80$ input, 400$ output, 100$ cache write. Hello?! Additionally, since it generates 8x faster, it consumes your quota 8x faster. Per wall-clock minute, it's 64x more usage spend per minute of generating. People report that on the 500$ plan, their weekly usage is gone in 40-100 minutes.
- People believe gpt-6.1-sol was planned to be released as astra-minor, but OpenAI renamed it at the last possible time, because gpt-6-sol is a disaster (this I can get behind)
Pulled astra 6.1 at the last moment (allegedly) and only released a dirty looper sol 6.1. Also released dots which are unavailable in EU. There may have been more but no one cared.
I just created a Google account, which was a hassle. I tried to find an app and found AI Studio, but I’m not sure what that is. Then I found Antigravity, which apparently is Google’s alternative to Codex, and I installed it. Then I tried to buy the small Gemini AI Ultra package, but it didn’t let me pay. It accepted neither my credit card nor my PayPal account. It’s crazy how bad the Google experience is nowadays. You don’t know what to use or where to find it, and the security measures seem totally overkill. Am I really the only one having these experiences? Is Google aware of that?
I bought a google play card (or whatever it's called) with cash due to these issues. Result: Instantly rejected + locked after entering the code, no credit, no dispute, no customer service, no refund.
It is mind boggling how difficult it is. I should be able to go to some well known URL, e.g. gemini.com and have the same experience I have on claude.ai or chatgpt.com. I can't wrap my head around why they are over complicating this. And it should largely feel this same as when I install 'Anti Gravity' which is just a crazy bad name. Why isn't it called Gemini Desktop?
Same thing, I've always paid my bills, I have had some sort of google subscription for the last 10 years. Now they're requesting me to switch to pre-paid billing. They won't accept my card (that has worked every time this year so far).
Is there anybody in Google having a look at how bad and complicated their payment system is? In all the other AI providers you just sign in, pay, get a key, done. In other clouds like AWS, you add a card, pay little to verify the card the first time, monthly bill, done. In Google it looks like you have to fight them every time for the very thing they should make as easy as possible: opening your wallet.
i live in French Polynesia, a French territory, i'm a Google AI subscriber and i can't use Google AI Studio or antigravity cli because i'm in a geo restricted area ...
It seems Google has no issue laying out cables across the Pacific Ocean from/to Tahiti, they don't have an issue selling their services and plans there but i can't use AI Studio or antigravity cli because of geo restrictions ...
This is the first time I'm hearing about this email (thanks!) and I'm wondering: If it was already known in 2003 how bad Windows was, how did we end up with a Windows that's so much worse? Bill said it was his job to send such emails but apparently he didn't do his job very well…
That said, asking Gemini.google.com to walk you through the steps seems to suffice. In fact, one of AIs big perks for me is walking my through kafkaesque systems like Google’s to cut the shit and get to the result
In the same boat, I have a single seat Google workspace account, and can't figure out how to add Argon to my workspace. Asking Gemini seems to make it hallucinate pages that aren't there.
Also single seat workspace user. Half the time my Gemini forgets I connected Gmail to it and hallucinates false instructions to enable it.
Truly crazy how much Gemini still hallucinates, especially things which its own Google documentation or just a basic web search away.
I feel it’s mostly solved problem with chatgpt/claude , but for me, with Gemini it’s almost guaranteed it will just make stuff up
I recently tried to create a new Gmail account for a small app I've made. I wanted a way for users to get hold of me other than my personal email address.
They now ask for your mobile number during signup. And it wouldn't let me register any more accounts with my number. So frustrating.
I think it’s a typical strategy for scammers to create a new account and then immediately try to buy an optional service with a stolen credit card.
What you did isn’t wrong, but is unfortunately very similar to what real scammers do. The heuristics behind these anti-abuse efforts are always going to have false positives.
First install Antigravity and see if it works on Free with your account at all. You can buy Ultra and then find out you don't have "Antigravity eligibility" on a given account or it will ask you to send SMS to some nonexistent number.
I would also advise against buying Gemini if you have any other Google services, because you get banned across board if they don't like something. To use Google it's best to first migrate off of Google.
Otherwise great product, I really like and would recommend Gemini. The usage is also very generous, at least Flash models.
I recently had to verify my YouTube account with a phone number recently because it requires this in order to set a thumbnail.
Questions right off the bat:
1. Why do I need to verify to post a thumbnail that will take 1MB on their servers but not to post a video that will take 100MB on their servers and be public??
2. Why do I need to verify my phone number on YouTube when it's linked to my Google account which has a verified phone number and 2FA etc.?
In any case, it takes you to a CSS-less page that asks you to choose between getting an SMS or being called to get a code. I choose SMS and it doesn't work. It shows an error message with no description just "something went wrong". I try a few times and it keeps not working.
I then switch to the call, and guess what! I have tried "too many times" and will have to try again later. Call worked the next day, but geez, what a mess.
Using google services has always been miserable. It used to be way worse. Now you can at least get an api key from AI studio without going through 20 esoteric steps to configure everything.
Yeah, a month ago I wanted to test out Gemini-3.5-Transcribe, but had to wait a completely indeterminate time, got the offensive message "Your Google Cloud billing account is being processed. Processing time varies from a few moments to a few weeks.", like I'm just worthless scum. Took over a week for no reason.
Ended up with Soniox instead, took minutes to get started.
I hate everything with Google and consumer facing services, they dont give a shit because 99.9% are monetized through spyware or are buisness customers.
They had gemini cli, what happened to that? Was fine
> The model launches today at an introductory price of $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off).
Couldn't you just say what the price will be after the introductory period and when it'll take effect? Do they have some SKILL.md for these blog posts that requires the most important information to always be buried ten levels deep and hidden behind a login page?
With the pace they're delivering models, there's gonna be 1-2 new Gemini 4 versions by December 1st, presumably with their own introductory price windows. We just got Gemini 3.8 on 2 September and Gemini 3.7 on 13 August.
This reminds me of telephone commercials that ran back around 1987, saying they were offering long distance calls for 10 cents a minute (a pretty good price back then) and -- this was emphasized, a huge selling point -- they guaranteed your rate would never go up. In retrospect, all the lols :-)
I wonder if that’s more to price in the cost of keeping old models around on expensive inference hardware. It’s not like old models get cheaper to run, and a more heterogeneous fleet is less efficient to manage.
> Couldn't you just say what the price will be after the introductory period
+1. FWIW, I didn't even get far enough to wonder about the price.
The lead (and only) sentence states "Our next era of frontier intelligence, built to solve your hardest problems" a statement equally true of any new Google or competitor model, hence completely devoid of useful meaning. I then kept scrolling down... and down... and down, giving up after a half dozen screens of big images, huge videos and some customer stories. The first sentence of every text block was equally vague or meaningless so... tv;dr (Too vague, didn't read).
Me, watching the AI world from the sidelines while using DeepSeek at $0.15($0.003 cached)/$0.60 - still bitter about the pricing changes.
It might not be as smart, though my employer gives me access to Claude, so I tried giving the same task to Opus 5.5. Both DS4.1 Flash and Opus5.5 produced nearly identical outputs.
After that, my company switched the entire engineering department to Azure hosted DeepSeek4.1 and costs were cut by 3/4. So far, no one has really noticed the change.
> They don’t really. Most normies which are the vast majority of people just use ChatGPT. All tech folk just use Claude.
It was the case a year ago, but it's less clear cut nowadays, I've seen non-tech people using claude on their phone in the train, and many tech people seem to have switched from Claude Code + Opus to Codex + Sol over the past few month.
they are target with 6 month lag. Say, if current Gemini will be demonstrating obviously superior perf compared to Opus, many normies devs will switch within 6 months.
Why do these benchmarks often show models running on Max i.e. Opus 5.5 (max) is on the frontier? I would think it is never really economical to run max
What jonas21 is stating is correct. If you have the absolute best performance, you are automatically on the Pareto frontier because you’re the most cost effective one (or the only model) on that performance tier.
Huh, I guess you're right, but I interpreted jonas21's comment in relation to the above comment, which was about all models running on Max (which implies this would be a comment relating to all points on the pareto curve).
In particular, where cost is the x axis, a well-funded company with a good but not best model can discount their new model until it reaches the frontier. In some sense this is as much a statement about the efficiency of markets in the presence of public measurement-based comparison and public list pricing as it is about research pace.
Muse reminded me we should have a time based chart as well - this year I hope we will begin to see time to completion stats at various intelligence tiers. The waiting makes a lot of things more tiring.
I’m curious at the enterprise level does Anthropic have staying power. My sense is when you use it with your own custom harness, it doesn’t, I can switch between Opus 5.5 or Gemini Argon and not really care as long as capabilities are similar. This is good for the consumer, and bad for the model developers.
People should appreciate Google more for how all of their models have:
- Natively multi-modality
- Enormous context windows
- Reliably cheap prices
It's like they don't release a model until it passes the above checklist.
Most providers have some mixture of these three occasionally, but with Google, every model is consistent. This kind of consistency is really nice to have.
Good point. On that note, it may be a bug but I noticed Argon compacting well under the 1M max in Copilot (around 400k) -- when set to 1M context. That may be a Copilot thing but I've never seen that with other models running 1M context. Could be a day-1 bug.
Outside of that, behavior is quite good, a bit slow, and definitely different than both GPTs and Claudes -- which is appreciated.
Nice research, you are right! Seems that low power mode was on. I have been using the web, Netflix, Apple TV app, and anything else just fine without ever noticing a thing though.
So that all being said, a normal website should work totally fine on low power mode, and this should definitely be a negative on the Opus implementation.
> and this should definitely be a negative on the Opus implementation.
I would disagree with this based on two things:
1. the prompt, which explicitly states to render the planets in the background in 3js (thus the gpu implementation).
2. the purpose of this, which is a test of its ability to convert an image design into a html/js/css implementation that matches it as closely as possible. I've talked about this before when it's come up[†]. The purpose is to be a good test, not a good design or website.
For reference, here's the full prompt used:
> Create a web page implementation from the following instructions: https://diffui.ai/build/Futuristic_Interior_UI_build.md?auth... dont reference any other folders that have similar builds. Focus on the flowing SVGs. Animate elements that make sense to be animated. For the starships that have planets in the background, ask for high quality images with the windows as a transparent background, and recreate the planets behind them in 3js. It should be one single page application that I can just open the index.html file to view.
Interesting! Do you have webgl disabled for incognito or for chrome in general? I wonder if it's trying to do software rendering.
FWIW, I have literally the exact same configuration (M3 air, google chrome). Here's a recording on mine where I'm seeing 60fps: https://non.io/video/video-3587018401.mp4
Mine doesn't stutter on on Firefox on M1 Max, but it pins 7 cores the entire time the page is open, not just while loading. It also uses 50% of GPU in addition to that.
Edit: The argon built site pins 1.3 cpu cores. The Sol 6.1 site uses 0.18 cores.
Truly S-tier. It's sharp, creative, intuitive. Can't believe how much better it is than Opus 5, then they went ahead and made it cheaper and faster too.
I'm constantly blown away by it. I do think Astra is slightly more intelligent (although worse at this particular benchmark), but Opus 5.5 has become my default model for 99% of what I do. It's so good.
I have a silly pet "project" that I've been using with different models, where I provide an image of a patch sheet for the Behringer Poly-D synth and ask for an html version and this is the first time I get something that, while still needing some tweaks, basically looks like the patch sheet. Before this, the closest had been Fable, but that still was nowhere near the actual patch sheet.
NGL, I think this might be my favorite benchmark, ha. While the Chinese labs asses are a bit off, I certainly have big hopes for their next versions. Their hearts are certainly in the right place.
Beyond SVG, these things have been RL'd on 3d reconstruction (which carries over), and I expect given the public reaction to recent frontier advancements in 3d capabilities that this will get allocated a larger share of the post training budget in the future.
What I'd kill for would be an app where you take a pic of scribbles and drawings on a whiteboard/notebook and the app turns it into SVGs you can then continue to manipulate digitally on PC/tablet.
They can represent something in vector, but it's always very hard to use the result for anything else. Like, the paths doesn't make sense, aren't grouped to the logic elements etc.
Last time I tried, it did not. That's why there's a place on the market for an app just tuned to handle the transition seamlessly, rather than having you fucking around with prompts in LLMs.
I for one don't want a whole app for that, not anymore. It's a small task whose result is an input to something else - ideal for a tool for LLM (or script) to use.
Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.
> It's a small task whose result is an input to something else - ideal for a tool for LLM (or script)
Have you actually tried this? If it's a small task then why can't LLMs actually do it? Kind of like the pelican test. Every LLM can do a SVG pelican but most of the results are unusable in practice for production/productivity purposes.
Every LLm can extract some half-broken SVG hallucination of your jots, but I'm asking for an app that's polished and works accurately 99% of the time, which means a dedicated app/model tuned specifically for this task, and nobody makes this yet.
> Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.
I don't feel like I'm a representative sample of general userbase, so I won't dispute this too hard.
For me, it's rare but it does happen. Still, 90% of my mobile experience is "how is this so broken, why is it missing the obvious follow-up feature that completes a workflow, and why, again, am I not using a laptop for this?".
> If it's a small task then why can't LLMs actually do it?
What if they actually can?
This is exactly what Karpathy was talking about the other day[0]. If your only LLM experience is the default chat app in chat mode, then this doesn't make sense. If your experience includes using agentic harness like Claude Code/Cowork / Codex/ChatGPT Work, then it's obvious.
The LLM app in "chat mode" will just zero-shot you a pelican, or digitize your notes into SVG, and maybe improve on it a bit if you complain about the result. The same app[1] in "agentic mode" will digitize your notes, refine the SVG against the original image, reduce it to probable strokes based on high-frequency patterns in the photo and knowledge of how people write, OCR it, save both the text and SVG to your library, then extract parts you wanted and insert them into a document, typeset the rest of it, and print it on your printer - all in response to a single prompt.
LLMs in agentic mode are very good at stringing long and advanced workflows; today, if you give them shell access or computer use, they will use regular apps for you if that's the easiest way to accomplish the goal.
> Have you actually tried this?
Yes![2].
That's why I can confidently say: there is no limit to this pattern. LLMs can handle apps just fine; giving them tools just saves time and tokens.
Another example: I'm currently resurrecting a 30 year old game, and the other day I asked Claude to hack on a way to run it headlessly, so I can use my computer while it plays with it. It did, and then immediately leveraged it to just causally run several instances of the game in parallel, entirely in the background, whenever it needed to playtest something.
[1] - Claude and ChatGPT now have both modes in their mobile, web and desktop apps. I personally use Claude Code a lot, but exclusively through these surfaces (I can't be arsed to deal with TUI at home anymore). IMO, these tools are already within reach of regular users - the users just didn't notice it yet.
[2] - Not with SVGs, but equivalent with voice recordings, and yes, Claude Code set up and is managing the whole local processing pipeline. Recordings go in, Markdown (or more advanced signal processing) comes out, and in itself, it's not a task, but merely a step.
> How many pelican riding bicycle SVGs were there before this test existed? What if the training data is being polluted with all these wonky results...
But Simon has demonstrated before that asking for any animal doing something silly will still get similar results from the same model.
Do you think this might be a kind of subconscious origin of your idea? A second of a move you saw in your early life / teens stayed with you as an example of an absurd thing to draw?
It's possible, but I think it's more likely that I was inspired by the TikZ unicorn thing, my love of pelicans (we have thousands of them in the harbor ten minutes from my house), and my knowledge that bicycles are difficult for humans to draw.
It's still clearly not a part of any model's training set in the sense of pelicanmaxxing in particular. It's either a capability to generate arbitrary SVGs (or even something broader), or no specific training at all.
Certainly not because all labs are pelicanmaxxing on the same exact image. Most patterns are similar everywhere. That's the result of training on the same datasets and scraped web contaminated with other models' outputs, combined with similar training methods and resulting mode collapse. Try if e.g. dolphins knitting socks in spacesuits are similar on all/most models.
Certainly true but I think the question is “does targeting the SVG pelican mean that the quality of all SVG output improves?” If so it’s a worthwhile thing to target.
It's a little bit like any benchmark, especially microbenchmarks. It is useful as far as it reflects the broader use cases, but the risk of overfitting to the specific task and not the broader ones is very high.
Sadly, this is all that managers know to do: take a metric and try to optimize the heck out of it. They don't care about research, technology, "don't be evil", etc. And Google is run by managers now.
Expect Google models to be benchmaxxed as much as possible, but don't expect commensurate performance on real-world tasks.
While this might be overly cynical, it's also a powerful validation of the exact feedback loop that you want to have in an organization.
If what you say is true, then the next set of benchmarks will be written to illuminate the true reflection of real-world tasks. Then those get benchmaxxed so managers can show "numbers go up."
Repeat enough times, and you get materially and genuinely improved products.
The burden is on the press, outside researchers, and general tech industry to both write and care about the metrics that matter.
As a cyclist, I tend to pay more attention to the bike shape than most people. Everyone knows what a bike looks like, but as with LLMs, most people would struggle to draw one - correctly connecting all the different tubes at correct angles etc.
I note that even these bikes are not actually rideable. For a bike to be stable, the front wheel axle needs to extend forward a little ahead of the fork - the fork must ”bend forwards”. This is what actually keeps the bike upright, not gyroscopic effects or some such. Given LLMs are generally good at basic maths, im surprised they get this wrong.
Maybe a bit less so in this case though. As per the announcements, the model was actually trained for this prompt, so was fed some training data. I can well imagine the data there was just not so good.
I feel like perhaps this is a bit too nuanced of a critique. While I do see what you are saying on many images of bicycles, there are also many that that I do not see it on. Close to half of them, in a quick Google image search, have forks appear straight and the axle is dead center on the ends of them. If there is something there, I am not seeing it even when trying to specifically look for it.
I'll also note that, using a piece of paper as a straight edge against my screen, there is a very slight forward curve on the low and a bit more on the high responses. Medium does appear to be perfectly straight.
Is it me or is there maybe some carryover happening where the pelicans are portrayed as expending different degrees of effort too? Like, the high effort pelican has a determined look on his face.
Is the prompt context telling the models what their effort level is, and the ambient presence of that concept of ‘high effort’ in the context window is leaking through and affecting the portrayal?
xhigh: "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
low: "Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration."
Recently, I’ve discovered that Claude can render great SVG versions of user interface screenshots. I ask it to substitute simple SVG friendly landscapes for any photographic images that happen to be part of the screenshots. The results are much better than they should be, especially with Opus. Usually good enough to be used on a website as a way to get an infinitely scalable, much smaller version of a screenshot. But clearly, I should try the Gemini models on this real world task. That’s where we’ll find out if all of this training to beat the benchmark actually pays off.
At some point Simon needs to move from Pelicans to something more complex. Every time a new model is launched by major Frontier company, I already know with certainty that first HN comment on the news would be about how Model is drawing the Pelicans.
It's obvious OAI hillclimbs the image generation -> SVG loop. I'd put money down on that being why OAI's pelicans have a yellowish tint that matches the yellow tint of OAI's earlier image models.
Why are all these AI's making the pelican's pouch so large though? Unless they have some huge fish in there or something they don't tend to look like that... Is it that it's drawing a "cartoon" pelican or something?
How many cicles before pelicans become photoreallistic SVGs and them come back to stylistic artistic abstract renderings?
Also, that first tweet contains a commentary that, for me, explain a lot about the public misconception about how current AI works:
> wait, does this mean you guys are actively training svg generation? i was mostly amazed by it thinking the models doing better at it by itself due to becoming better at reasoning.
No, it's not better reasoning that drives current AI, it's better examples we have been giving them. Whole army of people generating content for the frontier labs.
Also, reasoning improving the out of distribution part, like computer use, tool use etc.
Another case where allowing oneself to anthropomorphise LLMs a little yields better intuitions.
People don't get more skilled by raw reasoning, either. They improve their skills with better textbooks (pre-training data), more practice exercises (RL data), and generally goofing around (self-play).
simonw, your pelicans are so famous now that the frontier labs can just RLVR for SVG images explicitly.
It's a simple manner of prompt...generate concept art with nanobanana, measure, llm creates svg with text, measure the distance between rasterized svg embedding to nanobanana art....do post-training RL on a 3 million such images and matching embeddings.
I swear this will be a canonical example of Goodhardt's Law in action.
You have to switch to a new benchmark that isn't so easy to run a verifiable rewards pipeline on...Perhaps a longer horizon task where it's easy to mess up midway.
Why? Don’t you think the steady improvement in pelican bicycling SVG art is itself a worthy field of endeavor for LLMs to be optimized for?
Yes, Simon has made pelicans a benchmark and now models have grown very good at rendering them. Is there a reason to believe that this isn’t manifesting as useful skill in general in SVG generation?
The sample is too small. As long as the models are reasonable close enough to each other there is really nothing you can learn from this benchmark when its just one prompt.
It would perhaps be slightly interesting if the prompt was run 100 times for each model. I doubt the pelicans would look identical.
Front bicycle wheel in all Argon examples is still wrong. This bicycle will not be stable. Axis of rotation of front wheel needs to be in-front of axis of rotation of steering wheel. Frontier VLMs still do not understand how bicycles work.
Check the "high" thinking output again. For what it's worth, it's there.
However, how the front fork rake affect stability is I believe a little complex. I doubt this old article is the last word on the subject, but it's a great read nonetheless, and note especially the ultra-stability of the reversed fork bike!
It starts to be so good that the model doesn't really matter. Each model starts to have its own personality, and it's the personality of the drawing, like a real artist, that defines the style of the pelican, and that makes it harder and harder to decide if it's good or bad.
Models seem to have very interesting blind spots. While https://assbench.com/ may seem like a joke, the results are very illuminating. One might expect far better results given the detailed prompt, allowance for render checks and iteration, the number of human figures the models are trained on, and clarity of tool use (threejs).
However, result quality really drops off once you get out of the top 5 or so.
(NSFW note: all buttocks are covered by fairly conservative bikini bottoms and show no genitals, bulges, etc.)
The models that are really good at 3D modeling tend to also be really good at pelicans. I think the skills are strongly related - it's about having good 2D (and now 3D) spatial awareness.
The test is always going to be hard though, because it's an absurd image to ask for.
"Pelican on bicycle in SVG" is an adversarial example on multiple levels - out of distribution, ill choose of tool for the task, and anatomical impossibility of real pelicans using typical bycicles.
Yeah, that's a good point. My basic pelican test doesn't allow the model to render their work and iterate on it. The 3D models all come from agent systems that are rendering with Blender or screenshotting with Playwright along the way.
Have you looked at assbench.com? Even with threeJS rendering, screenshotting with Playwright, and iteration (check the prompt) some of the models are shockingly bad. I wonder whether the rendering quality is affected by the requirement for skin physics on click; if so, it raises a valid question about models creating pretty pictures but break when interactivity is required.
(As I said in a separate comment above, results are all bikini-clad and not revealing. However, whether it is NSFW is up to you/your work policies.)
I’d really like to see what each model comes up with if given a basic harness to render and iterate. They’ve all gotten pretty good at basic one shot pelicans, but seeing what they do when given a direct goal, and even getting a time lapse of each image as they edit and adjust would be really fun.
A lot depends on what the models were specifically trained for using reinforcement learning. Modern models are generally OK and everything, but they're only really good at the things they have extensive RL for.
Yes. I've been doing a bunch of 3D experiments with recent models, you can check the session logs and see if they've retrieved anything. They're building the 3D models directly in code.
I even built an environment that allows execution of Python code to render with Blender but allows no other interactions (including networking, I sandbox that off). They can create good 3D models just fine - here's a Faberge Egg by Gemini 4 Argon: https://tools.simonwillison.net/blender-viewer?url=https%3A%...
Honestly surprising if other labs aren’t training for svg generation. Feel like SVG’s are going to be really useful for dynamic UI generation stuff that goes beyond cobbling Html together
I've been dismissive of the SVG-of-pelicans benchmark, but last night ChatGPT (on the website, toggle set to GPT-6 High) generated some SVGs and included them in our chat. I'm not sure that this feature was that valuable in this case, but it seems that textually generating SVGs is now a product feature at least for ChatGPT. The SVGs had some clear issues and I found myself wishing that it had done a better job: the nose of the rocket is overlapping with the rocket body, and in the second image I think there's only supposed to be one orange line but it tried to add styling or something that went wrong.
- I was having trouble embedding the SVGs as links, and ChatGPT recommended Simon Willison's SVG Renderer as a workaround.
Just my 2 cents on the pelicans benchmark: I think it might help if you were a bit more explicit about not taking this one benchmark too seriously. I know you say that at times, but at other times (such as the comment I'm replying to) you elide it which can read as a seriousness about the benchmark.
Buddy, there's a Pelican riding it. No real cloud is like that, no real sea is like that, no real gulls are like that. And that's fine because it's a cartoon.
The models are solving Millennium Prize questions and making progress on the Riemann Hypothesis, yet the top comment is still looking at pelicans somehow. To me, it feels even more superficial and pointless than arguing about the presidential candidates' tie choices in a debate.
what is even the point of these now? labs are most definitely training on this question now. I feel like this test scratches the surface of a "real" test for these new models
It attracts at least one comment like yours on every single thread, so I guess the main purpose is to kick off a race to see who can post that comment first.
The roof is always raising month over month. If you were to make a harder version of the benchmark, what do you think you'd like to see the agents do better?
People who are unable to comment on the underlying technology or capabilities because they don't know or don't care to inform themselves sure can feel good that they have an opinion on pelican art.
Medium and high are fixies apparently, (or they have a worm gear, although the hub doesn't look big enough for that), since neither has a derailleur. That would also explain the lack of brakes at least.
Low is still obviously failing the "test". It's pedals are not actually attached to the bike either.
Medium has a handlebar that goes through the pelican's beak. So I'd say also failing.
For the high, the only nitpick would be that the bell isn't quite on the handlebars and the stripes on his scarf don't line up correctly.
Model announcement pages are the absolute worst. This has to be some kind of inside joke among labs, they're trying to see who can build the shittiest page.
The "Explore the latest" section is nearly blank for me - are there supposed to be diagrams or visual aids, or is it one headline and sentence taking up the whole page on cards?
Gemini isn't perfect but I find it a lot less annoying than other model families (personal preference or stockholm syndrome). Interested to read the reviews.
Seconded. Anthropic feel the need to limit how abusive you can be to Claude because god, I want to throttle that snotty little shit. Google don't have to do that with Gemini because it just isn't as irritating. It does what I ask and doesn't try to second-guess my requests, or do "the quick fix".
Maybe it's all about how often we encounter the quirks of specific model? Or simply that one's way to prompt triggers more annoyances with a model than another?
I'd be curious to see if people have managed to just add custom instructions to make the outputs more standardized and less annoying. I never have such annoying cases, whatever the model, it's just too verbose, but in this case I ask it to be more concise and it's usually fine.
Opposite for me. Output of 5.5 is better but its safety and moral standing skyrocketed. I am constantly hitting refusals for simple tasks, which previous generations (like 4.8) do without giving me a tirade.
Anthropic’s vision of “safe” is so frustrating to deal with, it makes me want to go back to ChatGPT.
It's been really bad at lyrics but gotten better. You look something up and it will just make something up and it takes a bit of digging to discover it's because of copyright
Of all models I like to write with Gemini the most. The language appears much more natural and elegant than what even Opus 5.5 or Astra do. Also, they give 5TB of cloud storage and discounts, it’s a steal for what you get.
Claude has particularly bad prose. Somewhat improved in 5.5, but it has historically clearly been the most annoying prose among major models because is had so many obvious tics that made it clear it was Claude rambling. Still extremely verbose.
They all ramble, they're all allergic to getting to the point. But, Gemini historically has pretty good prose. Even the self-hosted Gemma models have pretty nice, inoffensive prose. I wouldn't want to read a novel written by any of them, but I tend to ask Gemini questions in chat more often than Claude or GPT, because it has good prose and is good at using the web to research.
Agreed. It's much better at working with stuff (like documents and presentations) in other languages too. It feels like it's trained in multiple languages from the ground up rather than as an afterthought.
Qwen Flash Next has great writing! Unless it comes into any contact whatsoever with Claudeslop - then it immediately contracts the disease. It’s kind of irritating when you have it look at library code that’s been touched by Claude, because then the rest of your session is infected with it.
It's a great model. I have had it trying to decrypt the Voynich manuscript for the past couple of days without being biased by other research/other people's attempts. If anyone is interested: https://github.com/d2kx/voynich-research
> An interactive companion to our ongoing exploration
Eyes glazed over again.
Did you even read your own site before you posted it?
One of the first pages I opened:
> Smoking-gun proof of plant-label ingestion: in Row 2, the central cut root is labeled okol (f88r.15), and directly underneath it on Line 2 (f88r.19) the recipe writes...chokol daiin qoekol qoekol qockhol okol cheol...!
How about you post something you've done/built instead of just insulting other people. Spend your mornings using people stuff just to poke holes in it?
Good God, man. Google has the worst frontier lab rollout of them all. And they're delivering products that are behind the curve constantly.
Now, I happen to believe that's an excellent strategy. I bet they actually resolve more LLM requests than any other company at this point. The lay person still uses Google exclusively.
just cancelled AI pro myself, since I exclusively used it for AI and also pay for youtube premium family it was becoming quite the monthly just for google
if they throw in youtube premium in that package then I might reconisder
this is why antigravity is a doomed product at a pro level.
they gave away so much pro that they need to have smaller usage levels than competitors or they'll lose a ton of money. $20 was already a loss for users actually using their quota. the majority of pro users are paying far less than that.
so they'll lose their paying users, and keep their free or subsudized, which will make the economics even worse.
Google One - Pro tier. I have it via Lenny's Product Pass which, imo, is a much better deal since it includes Cursor Pro, Granola, and like dozens of other paid sub tiers: https://www.lennysproductpass.com/
I use the hell out of both. Kind of sad that they took Spark out of beta-access, being able to orchestrate via your phone on something that has direct access to your Drive meant I was authoring skills and other functionality on a schedule with spreadsheets serving as databases, deterministic tests, etc.
Remembering the horror stories about lost Google accounts, I would be comfortable paying the next lower tier (100 GB) because I can backup everything myself, but not the 5TB plan, I'm sure I'll lose something if the account is lost. Hopefully these plans guarantee a human in the tech support loop.
Yeah, very true. I need to set up a recurring backup-to-external-HD automation for it. It's vanishingly easy with the app and I don't doubt they're training on all the data itself.
Google AI pro in the USA, if you have Verizon wireless it’s $10 a month add on. Without Verizon it’s $20 a month I believe. You can also share most of the benefits with up to 5? People.
Afaict, they are targeting difference user bases, Google being after their existing user base, having lost in the developer ecosystem to the two labs and open weight models.
Corporate companies. Bog-standard office workers don't need top-of-the-line Mythos intelligence, just something to add to Google Workspace. I personally doubt developers use Gemini for coding beyond marketing materials.
Are you suggesting it’s BETTER than their competitors?
Because if it’s not, and it’s not available to all paying users (Right now, it’s only available to Ultra users), then they are behind the curve if they’re treating it like Fable.
FWIW, it's available via API to everyone. (I just tested it out via OpenRouter).
Whether it's actually better than OAI/Anthropic offerings, probably not, but if Google has managed to get back to parity, that's good for competition, prices, and increased model performance in the future. Three strong western competitors is better than two.
I see it is now. I tried all the services 3 hours ago and the only interface to acknowledge its existence at the time was the API interface which told me to upgrade to Ultra.
Maybe? But it's so many orders of magnitude apart. I can easily do 1B tokens a day (output tokens is probably like 3M?).
When I see non technical people use that Google reply I'd be surprised if it's more than 1k output tokens, and with a tiny model. So a 3000x in output tokens, on a model at least 10x as expensive, I wouldn't be surprised if hardware usage is 30.000x for a dev that uses coding subs vs someone who uses the Google AI overview a couple of times a day.
And that's not counting the absurd difference in cache reads.
Moreover, you need a Google account in order to use it. I wouldn't bother paying 200 USD for it but I just don't want an entire google tracking package just for using one product.
Oh, I didn't notice that they switched from 1.5 to 3 flash about six months ago. I know someone there and they talked about how 1.5 was hardened and it was a pain to do so on newer models that is wasn't worth it for a long time. But that convo was in the beginning of the year. I should have looked it up.
Adding my five cents: initial usage through OpenRouter has been great. Very fun model to spar with, clever, intelligent. On par with Opus 5.5 but without the Claudism's and more of a Sol type of conversation. Though speaking of Sol, I've been very dissapointed with Sol 6.1 to the extent that I feel OpenAI has neutered it compared to a few months ago. Especially when giving it goals it will happily run in dumb circles just burning tokens without any real work.
To everyone saying not available yet: it looks from the page like Pro and Ultra subscribers have it today and given the play links, it'll probably show up in the API in a few hours.
Same. It just told me "agent execution terminated due to an error". There was a little chevron underneath it that let me expand the error to view more. I was hoping to see what the error was, so I clicked it, and it said... "agent execution terminated due to an error".
It's crazy to me how care there is in most of these vibe-coded vibe-coding harnesses. I don't like oai's models much but I have to say their UI has by far the best attention to detail of any major player.
Available in agy for me as of now, I'm running my new model benmark of having it do comprehensive code audit on one of my more complex projects, it spun up 1 subagent per audit section and it took ~10 minutes per subagent on an 800k LOC, 60k complexity, ~4k file project. Will take me much longer to go through and determine the quality of the audit work.
Antigravity CLI 1.3.4
[My account email] (Google AI Ultra)
Gemini 4 Argon (High)
~/[working folder]
> /model
⎿ Model set to Gemini 4 Argon (High)
I'm on Pro and it's trying to get me to upgrade to Ultra in the AI Studio UI when using Argon. In Antigravity CLI and the Gemini website it's not showing up at all despite being logged in
I like this phrasing and am curious what new word or phrase we might use to describe big companies over pushing Ai internally at the expense of their product quality. This pattern is so ubiquitous across google cloud products I have taken to calling them the 21st century IBM, having been a IBM baby and witnessing first hand their fall from exceptionalism.
The internet and gaming has ruined my brain because scuffed was a way to describe a bad dungeon run in mythic plus dungeons in WoW, or a bad tower dive in LoL a few years ago. I see scuffed and think "here's a WoW/League knower".
HP is the other company to consider as analogous. And maybe more explicitly so, as they had a similar "engineering first" internal culture that degraded markedly over time to be a shadow of their former self.
And took similar companies that were quality engineering focused -- like DEC or Palm -- down with them through acquisition.
I couldn't see it either, then I clicked the 'Antigravity' button in the top right, switched back to the main screen, and it appeared in the model selector.
"Argon autonomously turns raw video to a physics-accurate simulation, estimating depth and physical boundaries, and writing the exact simulation code required to replicate those real-world environments."
Geometry could be understood today with the model but what also really matters is the sensing, friction, mass how smooth the subjects are, dexterity is a very difficult problem so solve, not sure how simulations could handle such factors
Game engines have many of these features. At one point, iirc, Unity was positioning to be the simulation environment for world model agents before we spoke about it in these words.
I have the Free AI Pro plan from when I purchased my Pixel 11 Pro. I still don't have Argon in AGY nor web nor Gemini app. I have a free product and still feel cheated.
As of my knowledge cutoff, three of the top state-of-the-art large language models are Google's Gemini 2.5 Pro, OpenAI's o3 (and GPT-4.5), and Anthropic's Claude 3.7 Sonnet.
When you are on "gemini.google.com" there is a little lock icon next to Gemini 3 and Gemini 4. When you click it you get the prompt to subscribe (pay). Once you have successfully done so, the Gemini 4 option disappears!
Because you have an android phone and don’t want to mess around with anything else(i.e the same reason people just use internet explorer on windows) or you hold Google stock so you want to support your investment :)
I'm open to Google winning me over again, but there isn't enough hype in the world to do it. They will need to earn it after these disappointing 6 months.
I appreciate how it's being integrated into the Google ecosystem, but the user experience still feels unpolished when actually using the system. The marketing, however, is impressive.
I seriously doubt their ability to double API prices on Dec 1. Token prices are in a clear downward trend and Google should maximize revenue by competing for volume. Currently OpenAI is the only US frontier lab trying to seriously compete with open-weight models that you can run yourself for an order of magnitude lower than what frontier labs want to charge
The model used is irrelevant when there's the critical issue where their "AI" assistant can't understand the word "nearest". Literally worse than a second-grade understanding of English.
I also have had shockingly bad results with gemini on android. Ended up giving up on it after a few months. Silly silly google with their trademark UX polish and attention to usability.
This reflects pretty much every result of mine from Google these days. Not just navigation, but search too.
The only thing I can figure is that Google's consumer-facing free-to-use models are set to "reason as fast as possible with only the most basic plausibility checks." I have to correct them like this every single time.
On Android, the public APIs available for digital assistants are more limited than the ones that Google gets to use for their own products. So if you want a digital assistant and you'd like to be able to hold a button on your phone and have it open a thread with an LLM (even if your screen is off), your digital assistant has to be Gemini. You can configure ChatGPT or Claude in its place, but then they can't manipulate or interact with apps in your phone very much. Or you can use an app like that for all of your LLM use but you can't conveniently summon it.
Similarly, you can run local LLMs on your Google Pixel smartphone, but if you want them to be hardware accelerated using the chip that's included for that very purpose on your phone... it has to be Google's models via Google's apps. So you can't say "alright, a fast, local model which sounds great, but I want one that is trained to always run a search before answering, in a harness with good search tools for answering the kinds of practical questions I often ask", you're actually not free to put that together in a practicable way.
So there is indeed some competition here. But it's not really fair competition.
this is like that tiktok guy who asks the spoken ChatGPT model very simple questions, like 'count down from 60', or 'how many e's in seventeen' or whatever and the model shits the bed every time it's hilarious
I think these companies have good models but expose them poorly like others have mentioned
Often it seems like the problem with the Gemini apps is the harness, not the model itself. For example if I upload an image PDF it recognizes text worse than if I use the API.
Is this from you car ? Gemini in car is probably less intelligent than a spider.
I tell it to "Play Let it go from frozen". It plays it on youtube music. 5 seconds later I tell it again 'Play let it go from frozen" it then just dies saying "I can't play videos in car".
The auto version is extra horrible because what it reads to you is just a summary of what the model actually said. I don't ask it anything when driving because it's too frustrating.
Yeah, whatever they have running on Google Maps is probably a pretty low tier model, but it is total trash. I’ve only used it twice though and that’s when it first came out. I don’t know if there have been any changes since then. The main problem is I felt like it was not keeping track of the context of the session.
which is more or less the crux of the problem android auto's gemini implementation. It sometimes does not even know that it is in a car. The ghome implementation is also like this.
I ran it through my own coding eval today using pi/OpenRouter (11 small bug fixes from my own projects). It solved 10 of 11. Compared to Sonnet 5.5 and GPT-6.1 Sol, a median run cost me $0.52 vs $0.18 for Sonnet and $0.09 for Sol, and took ~4 minutes vs 32 seconds and ~1.5 minutes.
It thinks a ton, ~24k output tokens a run vs ~3k for Sonnet. It missed a solution by thinking its way into a 10 min timeoout.
Something to look out for - if you're using pi, it doesn't have Argon in its catalog yet and reports cost at Kimi's rates, about a third of what OpenRouter actually bills.
I do wish I could give it a try with some free credits but I would bet Google is wanting to limit usage until they know they can handle the traffic.
I'm starting to feel the subscription fatigue and a small amount of FOMO. I just can't justify pro accounts for every frontier model now that it includes OpenAI, Anthropic, Meta and Google.
Yeah, the limit was exhausted weirdly fast compared to openai and anthropic models. I checked the thread to see if more people had this issue on the 5x plan, but why pay for more when it's barely usable for real work.
Which is even weirder given that the api costs are less than for the other providers
Maybe they don't really care about antigravity users. Also, monitoring usage is horrible.
Really? Reminds me of Gemini 3. It took so long before being able to get a subscription for it, and then I would run out of their equivalent of the 5-hour usage window in a heartbeat.
Yeah maybe I've just become used to how much I can use Flash 3.7/3.8 but on the less expensive Ultra plan I basically never exhaust my quota even when working over multiple projects, but this was pretty much immediate. I did ask it to audit a project but still, I do that in Flash regularly and it's not especially costly.
I would love to try it out, and the Gemini models had previously been some of the ones I'd liked best in an overall sense, but unfortunately their updated inference endpoint is no longer ZDR on OpenRouter (change takes effect on the 18th). I think this is one of the more unfortunate turns in AI development in 2026, as it seemed like labs offering it was becoming the norm last year, and Google had previously done so consistently.
AI Studio has never been ZDR (retention is noted at 55 days max), but paid usage is excluded from training. Vertex uses enterprise terms, and it's definitely ZDR/no training. What's happening on the 18th?
I'm a bit confused, all the announcements say that this is availabe to Ultra and Pro subscribers, but in antigravity, it's grayed out and says it's only available to Ultra.
Only available on Ultra, so I thought maybe I'll try it for one month to see if it's good. Well, I have Google One and for some reason this means I cannot sign up for AI Ultra? This is just one of the worst product rollouts I've ever seen
I checked gemini, set it to "gemini 4 argon" and it offered me Pro and Ultra to get access. I checked the normal plans, saw there is also plus.
Okay, so I'll get Pro to try it.
I subscribe.
No gemini 4.
The most generous reading I have is that it's a mistake. But I also downloaded the PC app, and now I really wonder why I did that, it seems to offer nothing beyond the web experience and also doesn't even know its a local app. (When asked about its capabilities it always responded with reasons pertaining to it being a web app.)
It's available to Pro in the Gemini app (and the gemini.google.com). It isn't available in antigravity. They know that the people who are using these tools for coding are most likely to cough up for the Ultra subscription.
EDIT: Okay, the AI plans page claims under pro - "Expanded access to Gemini 4 Argon in Google Antigravity." So maybe it is just a matter of it rolling out. Though Google has a long history of serious confusing and misinformation on those tier descriptions.
EDIT2: And they changed it again, and now it is explicitly excluded from antigravity for Pro users.
Right now there are two top posts one about GLM-Legal and one about Argon. Both score about the same in the Harvey Lab test, but the prices are $0.15 versus $10.55, the $0.15 is Vals AI's estimate using the base model since GLM-Legal has not published prices.
Yeah, but one is a general purpose model, and the other one is specifically geared towards the legal field. Considering Opus 5.5 only gets a 4% on the test compared to Gemini’s 20%, that’s actually pretty incredible.
It's nice to have it available for more complex tasks, but I'll stick with 3.8 Flash thanks to its high output speed. I really like it for fast iteration and prototyping.
As I approach a transitional period in my career, I'm reflecting on the managerial effort of places like google. How on earth do they keep an engineering org on the 'right track'? I work on products that are not as complex: fewer moving parts, fewer egos, no where near as novel. It feels like nobody does anything and nobody has any expertise
Makes me wonder if more growth/innovation could've happened (outside of google) if they didn't employ this many people. Some sort of dutch disease within the higher echelons of the tech sector.
Google did a very good job on things that required longer research times and compute scale, note that Transformers, BERT & Scaling Laws came out of Google (though the latter were also worked on at OpenAI).
Conversely, Google is horrible at product, and without OpenAI, it probably would have taken many years longer to take off. I know this because Google had demos of this internally, but absolutely no stomach to release it, and they didn't believe in the API use case at all.
Which is to say, I think if Google was smaller, we probably would have gotten more SaaS, not more breakthrough research.
It’s like the USA. How do they keep people on track? They don’t. You delegate and hope things work. When they have Google Ads and Search they can experiment a lot and “waste” a lot in search.
They haven't solved management and definitely don't keep things "on track" globally. However, they definitely don't have the problem that nobody does anything and nobody has any expertise.
It's also worth noting, Gemini 3.5 Pro never came out.
Having worked there (but only in a small part of it that eventually got absorbed into Gemini), it's some combination of:
* The eye of Sauron (management) can make specific spots (e.g. Gemini) push harder (and attract the motivated people at the company and be selective)
* They hire a lot better than average (Generally from the top 10%, hotter places in Google are more selective)
* They have a promo process that gives career motivated people a way to prove themselves (not always aligned with things being on the right track ofc, and not always non-political, but they definitely do something)
* Redundancy in teams causes them to compete (not necessarily productively)
Of course, there are other downsides not listed here (insane bureaucracy for launches, GDPR/BCID compliance, etc, etc), but a lot of what makes Google possible is the insane amounts of cash Search throws off.
Success for google is defined in a different way than it would be for an ordinary company and many of those would have been successful if they had started outside of google.
The really annoying ones are the ones they buy, and then shutter because they can't make enough money on them fast enough.
Their previous best model - Gemini 3.8 Flash (also when set to high) was even faster. A appreciate it that they focus on speed. I's great for LLM pair-programming.
I made a NHL inspired hockey web game with Opus 5.5, and I let Argon loose on it to try and see if it could improve it, and it found a ton of bugs and improved performance and load times significantly. If anyone wants to play it here:
Digging in the model card, "Gemini 4 is the next generation in the Gemini series, a suite of highly-capable, natively multimodal, reasoning models. Gemini 4 Argon delivers frontier performance..." If they're using noble gases there are 2 obvious lighter naming candidates and 3 larger (or 4 if you count Oganesson). I'm not seeing them claiming Argon is the top/biggest/bestest model among the Gemini 4 series, just that it has frontier performance.
Edit: noble gases not noble taxes, though the latter would be a funnier naming scheme.
> Across Google and our early access partners, Argon is quickly becoming the preferred model for engineers tackling long-horizon software engineering.
"Across Google", wonder how much that is because of an effort by Google to curb usage of Anthropic models. MSFT and Meta similarly are pushing for in-house models. It doesn't mean the models are better when you push your engineers to dogfood and restrict access to other LLMs.
(Not that I know for sure this is the case, it's more of a hypothesis).
I hope all the people very concerned about AI safety are cheering this one. The best guard against one AI swarm taking over is having multiple adversarial orgs all with similarly advanced AI swarms.
I wonder if any of the AI safety people will begin encouraging AI researchers to swap jobs to work at a failing lab in order to keep them all extremely competitive with one another.
I'm still waiting for a model which I can tell to "take these 3 screenshots and make a product launch video", where it doesn't randomly modify the screenshots or garble/rewrite the text as it moves the screenshots around the video.
Every time I try to generate one it's like a slot machine, where a different part will be messed up after fixing the first.
I dont have that problem with opus5.5. I dont even take the screenshots anymore (it does all screen recordings)... I tell it a simple narrative and it follows through. e.g. https://x.com/plannotator/status/2108589088139886769?s=20
I gave the model my usual question of "I got a raise from X to Y, please explain what is the impact on my net salary". Answer was detailed and nailed all payroll edge cases, only to miserably fail in basic math at the end. It tried to calculate 430 minus 60 and got 410.
Pricing is interesting. At launch Argon is the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra.
But on December 1st Argon doubles in price - so it will be the same price as Opus 5.5.
I've kinda stopped looking at token pricing. Argon might be the same token pricing as Sol, but in Artificial Analysis' testing, Argon costs 5.8x more to run their coding tasks and 2.2x more for their general index.
Likewise, you look at Artificial Analysis' general index and it cost $8,708 to run with Opus and $2,407 to run with Argon so even if Argon's token price doubles, it won't be the same price as Opus.
Sonnet is cheaper than Opus per-token, but in their coding tests Sonnet burns through enough tokens that it's 7.5% more expensive.
So much depends on how token efficient something is and whether it gets you a good result or not, right?
Plus, if one is using a plan instead of paying token rates, what does my $200 get me from OpenAI, Anthropic, or Google?
It just feels very hard to compare pricing given that the number of tokens used can vary so much. In Artificial Analysis' coding tests, Sonnet used 7.1x more tokens than Sol. Sonnet and Sol might have the same per-token pricing, but there's a huge difference in the number of tokens used.
It passed the test of using latest crystal-lang concurrent feature (with web search) when gemini 3.8 flash failed (again, with web search enabled). I think it is at least useful for coding, for sometime.
All AI pages now include canvas animations. There isn't a single page without one. This is neither good nor bad, just an observation. I think OpenAI has the best animations so far, with Anthropic in second place. This one looks a bit off.
Is there a benchmark for reverse engineering compiled programs?
- Grader compiles a program to binary -> Agent has to reverse it back, and match all bugs and behavior.
Page says it is now available to Ultra and Pro subscribers. Although in AI Studio it currently says you need Ultra. But it's technically released either way.
Yeah, I think this is it! Google tried to hype their model but they have no chance against queen Dario. Perhaps they should just delete Gmail and say that Gemini did it. That way they would get some attention …
It seems the model is a little verbose, using more tokens to complete a task that other similar models as stated more precisely in the linked article about Artificial Analysis's work on this model.
The slides they show in the catering video demo are so bad (in addition to having all the usual signs of AI design), I would genuinely laugh if a supplier sent me this.
Finally, gemini 3.8 has been falling way behind opus and sol in my experience. It's showing up in my antigravity. My work account though still has 3.6, 3.8, 3.1.
The previous gen pro used to have this weird tick where if you ask it a reasonably open ended question, it would number things in the response and assign each one some weird label starting with 'The...'
e.g.
X: The Parameters
Y: The "System"
Z: The "Ongoing Grind"
The first two prompts I tested in AI Studio does the same, unfortunately.
Another quite unpleasant tick was that if you specify a peculiar term in your prompt as a conversational shortcut, it would keep repeating that throughout the conversation. e.g. "...What is a bitter lesson pilled way to do this?", then you'd see it will quote "bitter lesson pilled" at like 7 places in its response. This one seems to have eased off a little.
Just switched, and it seems to be much better. I'm hoping it stops lying. I have Pro max subs for Claude, Codex, Grok, and Google AI... I was forced into using Google by running out of credits elsewhere.
I have found that Gemini often claims it did something, but didn't actually complete the task.
I just had 3.8 Flash do a major refactor -- that never should have needed to be done in the first place -- and switched to Argon 4, and had it review it.
Almost everything had been stubbed out by 3.8, and none of it was properly integrated into the codebase. :facepalm:
Unlike most people it seems, I like all the progress in AI but I really do wish they would reign in the over-the-top marketing. I mean, can we just get this stuff to the point where I can reliably work with it to design a well-architected software project without having to constantly struggle against bloat and nonsense directions no experienced or sane developer would ever take.
fyi: argon is an interesting choice of name, coming from the Greek argos meaning "lazy or living w/out labor".
I've asked Argon to do 3D model for me. It said that it can't do it. I've linked promotional materials and said that it actually can. It agreed and got me a model (not a great one). Man, why is Gemini like this? Every time I fall for the hype I get disappointed.
Glad to see them abandon the absolute train wreck of their model naming "strategy".
Gemini Live vs. Gemini Lite vs. Gemini Pro vs. Gemini Flash vs. Nano Banana Lite (that one apparently has no Gemini prefix?)... Please, just pick a metaphor and stick with it without wildly mixing reasoning modes, model sizes, and input/output modalities in the model name!
I use Gemini all day at work via the chat interface. It's rock solid. I find it hard to understand which model it's using though. Am I already using Argon, or does it switch models according to the subject?
can for the love of god we get some usage in cheaper subscriptions being Ultra tier only is annoying. I want to just test it out here, well will burn some money via api I guess.
>"Argon helped the Chrome team separate core webpage handling from a ~6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. Piping multiple clean code changes at a time, Argon helped the team finish an architectural refactor across hundreds of files, completing months of work in weeks."
Now that needed to be done for some time!
Regardless of if an AI helped or not, regardless of whether that was Google's or some other AI or not (though Argon does sound pretty cool!), regardless of whether an AI cut the time from months to weeks or not, refactoring Chrome -- decomposing it into individual clearly demarcated non-tightly coupled software parts with core webpage handling at the center -- that needed to be done for some time!
Now all of that being said, Gemini 4 Argon does sound pretty cool, and we congratulate both teams at Google -- the team that created Gemini 4 Argon, and the team that refactored Chrome with it!
But all of that being said, while the advancements made for Gemini 4 Argon are important and impressive (and they are!)... refactoring Chrome properly (IMHO) is slightly more important and impressive!
(You know, one man's humble opinion! That is, I am not the guy who is responsible for awarding performance-based bonuses at Google at the end of the year, so take everything I say with the proverbial "grain of salt"! :-) I am probably biased, one way or the other! :-) )
Why can't the Gemini model code?
I'd guess Gemini probably used the best dataset.
Even Google is the place where the world's best coders are gathered, isn't it?
Yet the GPT code from OpenAI, which collaborated with Microsoft, is beautiful, while the Gemini model's coding is trash.
I have no problem with Gemini Flash 3.8 for coding, though I only use Elixir which is one of the better languages for LLMs. Where does it fall short elsewhere?
Meanwhile I paid a subscription and they only provide access to 3.6 Flash. Consequently, I have moved on to open-weight models. It is what happens when you screw your customers over.
3.1 Gemini was amazing for anything non-agentic. I switched to it for my primary chatbot purposes.
I never touch 3.8, it's awful. Curious to try this, but I've yet to use any Gemini that is good for agentic work.
Also, unrelated but this made me look at Artificial Analysis to see where it lands on Cost per Task, and it ranks Opus 5.5 at almost double Astra 6. That does not align with my experience.
Not available for AI Pro subscribers in Antigravity. What a disappointment.
Google...why. You keep getting so close but constantly fumble at the last moment. I can use Opus 5.5 and Sol/Astra on the $20 Anthropic and OpenAI plans.
You are already playing from behind. You need to GET YOUR MODELS INTO MORE PEOPLES HANDS by any means possible. Continuing to make it difficult to use your products DOES NOT HELP.
This model might be great but the majority aren't going to subscribe for $100/month just to try it.
Have they announced that it definitely won't be? If not, it might be just a roll-out hiccup, because Argon is available to me as an agent in Xcode and I only have a Gemini Pro subscription. (Whether or not I'll ever use Argon in Xcode is a different question. I've been so impressed by Claude that I rarely even use my Codex sub. I'll be very interested to hear from others how well Argon does with Swift and SwiftUI.)
Blatantly obvious that models will not slow down. For any task, if we can build a dataset or environment of verifications, it can be automated. This will eat all of human tasks.
The fact that multiple companies can breach any benchmark means there is no ambiguity in terms of how to build AI, its just a matter of time.
Certainly Meta and Google are benchmaxxing to please public markets, but it still proves the above
but they have slowed down. the delta in capability between model releases now is smaller than ever before and i expect it to continue to diminish until we reach some kind of asymptotic maximum, which hopefully will mean turn towards optimization and local model improvements. The current frontier models running at 10x the tok/s would be incredible for my use cases
The delta isn't really slower, we're just unlocking capability bundles in batches. The capabilities bundles you care about might have mostly been unlocked already, and just need refinement, which is coming slower than the initial capabilities came, but don't discount the breadth of advances being made across all possible tasks.
We're not living in the same universe. Look at capability year-over-year. The asymptotic maximum is wishful thinking. Someone needs to step in before this gets wildly out of control.
> Someone needs to step in before this gets wildly out of control.
This sounds like a paid bot, lmao.
I wonder how much is actual improvements in the model and how much comes from better harnesses. I'd be willing to bet that the combination of better harnesses + the models being trained around agentic workflows (i.e the harnesses) has made it significantly better in many things, but it's hard to find other improvements imo.
You can see the difference between GPT 3, 4, 5, 6 each year. Same with Gemini and Claude. Look at image and text generation. Code generation. Math ability (Navier Stokes, Quasi-Riemann.)
Year-over-year, yes I would agree with you, but that is not the cycle time of models. And while I share the same hunch as GP, I'm waiting another cycle before putting more confidence behind it.
A little over a year ago they were winning their first gold in an elite high school math competition. Have you not seen what they are doing in math now?
i burn through a $200 claude and $200 codex plan every week. opus 5.5 is not "step change" it's just a bit better, and the benchmarks show this. Honestly with my workflow I cannot really tell the difference between the top models, they're all pretty much the same in capability, just a bit different. You can build software with all of them, and with all of them you have to validate the change thoroughly to not ship stupid bugs
I do not see the current training regime of "relentlessly try anything to get the task done" as a true step change in intelligence. This is one of the reasons I suspect capability acquisition is slowing.
A more intelligent model would not default to spaghetti throwing, it would pause to consider the problem fundamentals, the accuracy of assumptions, and ask questions that remove ambiguity. This however does not lend itself to increasing token sales while prices remain inflated from market and brand dominance.
Models just solved a Millennium price and you think their progress is slowing down? I will concede that their capabilities vary a lot from one field to another, but still.
Classic Google: Clicks the "Try in Gemini" button from the Gemini 4 page, gets a page that defaults to Gemini 3.6, with no option to try Gemini 4 without upgrading.
If they don't want to make it available even for small tests for free, that's fine, but then don't put a button suggesting I can try it without qualifying it...
EDIT: It's actually worse: The page then shows "Look what you can do with Gemini 4" and a "Let's go" button that leads to a bunch of links to examples, all with enticing pictures. Except if I click on them I get a box stat strongly suggests it'll run with Gemini 4, except the dropdown shows "Flash" and if you click on it, it shows as Gemini 3.6 again, and if you run it, the results unsurprisingly end up looking nothing like their pictures.
I don't know if that page was built by humans or AI, but clearly nobody tested it properly.
Ha, same experience here. It opened my Gemini and gave me images like “Design a terrarium with Gemini 4” - proceeds to pick 3.1 Pro from the picker because 4 just doesn’t exist in the dropdown. Typical Google.
I really can’t believe how bad the Gemini experience is. I have personal paid workspace account, and meant to have some better AI Google features I think.
So, I connected my Gmail to Gemini. 3.6 Flash kept telling me it doesn’t have any Gmail connnection, and then proceeded to hallucinate instructions for me to enable it, all bogus. At least 3.1 Pro realized that my Gmail is connected and then searched.
I spent like 40 minutes trying to figure out how to sign up and try Gemini 4. I am a GCP user; it is so convoluted to do a simple sign-up.
I have $6 free credits from Google. The hoops you have to jump through are insane. $200 free credits from Anthropic are stupid simple to use. I know which one I’m using…
> I don't know if that page was built by humans or AI, but clearly nobody tested it properly.
...or Gemini 4 is being gradually rolled out and is not available to everyone all at once.
Sounds like no one tested the rollout plan properly then.
Sure, most all major changes get gradually rolled out.
But you don’t create a broken or confusing experience for the people who haven’t been added to the rollout group yet.
Mine does exactly this and I pay for Pro too
lol. It’s easily accessible in cursor rn.
I haven't tried it for coding yet, but I ran a couple of my recent curiosity driven queries from Claude (mostly used Fable and Opus 5.5) through it.
This is a clear step change, I'm genuinely amazed.
Its answers are to-the-point, logically structured, clearly written. Like a really well made mini essay on your question.
It also really likes to build little simulations / models that you can tweak the parameters on. Amazing to explore how different assumptions affect economic scenarios, for example.
It's also great at picking (sourced) illustrations.
I'll be using this for everything that isn't coding from now on.
It’s wild how good it is at explaining nuanced and complex health topics.
As an example, I recently had an OCT scan of my retina because I'm a “glaucoma suspect.” I'm a glaucoma suspect because I have an unusually high cup-to-disc ratio, which is a pattern typically seen in glaucomatous patients.
As part of this OCT scan, you get a report back on your RNFL, which is basically a report on the health and thickness of your optic nerve(s) and retina(s). (Dramatically oversimplifying, of course.)
My ophthalmologist struggled to explain my OCT scan results to me and instead sort of shrugged and said, "It's probably because you're a high myope, but we'll keep you coming in once a year.”
But my scans look horrifically bright red — meaning I'm in less than the first percentile of retinal thickness for my age. Obviously concerning enough to do my own research…
ChatGPT 6 Pro and Opus 5.5 have struggled to make any sense of the clinical implications of these scans. In contrast, Argon was the first model to:
1. explain how the OCT machine works
2. detail the physiological mechanism that causes the cupping and thinner RNFL layers in my scenario
3. sum it up into a clinical recommendation (similar to what my ophthalmologist recommended, with the addition of basic supplementation and lifestyle modification changes)
4. provide an interactive visualization of the topography of my eye vs a glaucomatic eye: https://optic-nerve-3d.netlify.app/
I'm blown away.
Totally agree. This is my new favorite general-purpose/non-coding model. It's surprising how good this thing is... And fast, too.
"Fast" was also my first impression https://x.com/tmuxvim/status/2110054212361220444
where are you guys playing with it - i logged into ai studio and it's a mess. cant event figure out what model im using
gemini.google.com with a $20/month Pro membership.
> I'll be using this for everything that isn't coding from now on.
why? is it not so good on that? or is there something better performance, or better price for same performance?
I'm not the poster you responded to, but I can tell you that historically the Gemini models have lagged behind Anthropic (the clear leader) and OpenAI's best models where coding in particular is concerned. Opus 5.5 is, right now, the consensus best coding model.
That said, I'm extremely impressed with Argon thus far. I take back everything bad I've ever said about their AI strategy. Argon is very good and very fast. It might be competitive at coding, but it's too early to say.
I completely agree; I was really surprised to find that Claude Code Opus 5.5 is significantly better. I had spent quite a bit of time using Gemini Pro for coding tasks, but when I revisited the code analysis using Opus 5.5, it revealed quite a few shortcomings in Gemini's understanding... I code using Python, which is considered the best language for writing code via LLMs (I am referring to linguistic entropy).
Sounds like you were using the older Gemini 3.1 Pro from February, and not the new release this post is about
Opus 5.5 routinely finds tons of bugs in code that Opus 5.5 wrote, so this may not reflect a difference in model capability.
I am using it via OpenRouter for going through some Vulkan code. It's a beast - it has no hesitation putting in instrumentation within the code (having a number of render passes) to dump the framebuffer to PNG files and doing pixel-by-pixel comparisons, putting in false colors in shaders to identify bugs etc. Very impressive.
Out of curiosity, which harness do you use for this kind of task, and how do you sandbox it? I used Aider before, and consider switching to a more recent tool.
I started out with agy CLI but it's horrible because of its very indeterminate context compaction. But I live with it because it's the only way to use Google's subscription. I was and still use Opencode in parallel (accessing Openrouter ZDR endpoints) when I run out of rate limits / weekly limits.
Over the past two weeks, I have started using a locally modified version of deepseek-harness (modifications include disabling telemetry and querying a provider's model list dynamically through Openrouter, instead of relying on the pre-packaged pi-ai manifest that comes with the base code). So far, that has been the best experience - I really like the caching, tokens/sec stats that it provides. (EDIT: I don't know a lick of TypeScript but AI-driven modification of the harness is good enough for me to make these tweaks).
Sandboxing is a definite issue for me. As you perhaps know already, headless operation is often not an option in graphics/game programming which requires actual render-surface instantiations from windowing systems and then input-context handling (keyboard/mouse/gamepad) as well. So I run most development on a VM. But part of my test-suite and smoke-tests rely on runninng it directly on the host OS (which is not ideal). I'd love to hear suggestions to avoid this.
Can you share your modifications?
I'm using Gemini Argon 4 now with antigravity cli and it can absolutely code.
When does it auto compact? Is it at 250k tokens as for gemini-3.8-flash? If it's more generous I might consider upgrading to the ultra plan.
do you have ultra plan? it says regular sub doesn't have it.
> I haven't tried it for coding yet
Google's models have usually been pretty good at general query stuff, they fall flat when doing harder tasks.
With 3.8 Flash on High I noticed a huge upgrade in intricate discussions related to physics, material properties, real-world application, and so on. Things that I've had a hard time getting LLMs to be able to discuss accurately. They often discuss textbook knowledge fine but can't apply it properly or can't think outside the box at all. 3.8 High understands applied material sciences in a way that GPT Astra flunks at often. I asked Gemini to explain that result, and it said basically that GPT was merely book smart in a classical sense and that it was likely undertrained on more obscure material.
I’m curious about your curiosity driven queries. Mind to share?
My first reaction after reading the announcement post is that the coding scores look like a weak point.
Google is clearly not targeting coding. They don't have to. They're Google.
Just praying they don’t nerf it
I've found it on par with Opus 5.5, what don't you like about it for coding?
Coding? This is Hacker News. We're supposed to be bashing how shit it is at creating svg's
Which reminds me, where is @simonw ? lol
We have this same point made every release. I wonder if models actually are getting better or if these companies are just secretly hamstringing the previous models before releasing a similarly performing model
Models are actually getting better.
I think it takes a while to start finding the weak points in a model. It seems fantastic until you use it enough to find out that it hallucinates APIs, or can’t count letters, or uses too many em-dashes, etc. you don’t really know someone (or something) until you’ve lived with them (or it).
I think there's definitely some quantization or nerfing happening post-release -- I definitely noticed that with Gemini Flash 3.7, it was super fast and accurate at the start and a few weeks later seemed to run in circles and overthink even a "what are you doing" message, all while doing almost identical work. I find it weird that we're so far into the LLM era and the post-launch degradation is still something of a conspiracy theory.
Try using a model from a year ago.
What would that prove if we are testing whether they "are just secretly hamstringing the previous models" as GP claimed?
While IME 4.7 and 4.8 were not appreciably better than 4.6, Opus 5 was noticeably better, and Opus 5.5 is significantly better than Opus 5.
I'm using this model via API. I am most excited about its ability to write the english language (or any language). My least favorite trend on twitter has been people posting 2D illustrations/3D renders as a proxy for model capability. Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I've been trying it out via the API and the model has been a delight so far. Clear in the head, not RL-fried, distinct writing style, and big model smell. That said, the em-dash is back w/ this model.
That said, I would be so frustrated with Sundar Pichai if I was on the Google team. The botched rollout of this model seems to be entirely a matter of capacity, and Pichai has been more than happy to give it to anybody but his own team.
I've been using it for a while internally at Google. I prefer Argon over Opus 5.5 because of the clear thinking and directness. It phrases things in an organized, straightforward way.
Interesting. My favorite LLM output ever was the reasoning traces in gemini 2.5 pro. I found them much easier to understand than the "final output", because they were very structured and concise. Of course, they ended up hiding the reasoning, not long after that, like everyone else... sad!
If true, this will be a gigantic departure from any model Google have released to date. I’m hopeful but also pretty skeptical.
I feel Argon is about as good as Opus 5.5 but I find Opus annoyingly sarcastic and incomprehensible.
I know it's a matter of personal taste but it reminds me of a grad student that tries to sound smarter than he is by being dismissive and opaque.
Opus 5.5 has the personality of a proud righteous nerd, definitely incomprehensible at times, unless you explicitly tells it to slow it down, it crams information far too dense.
Great model to architect and resolve eng. problems in a harness loop tho. Poor handoff.
Yes, exactly! Maybe not sarcastic but definitely using jargon and being opaque like we all know it, it’s just hinting. Feels like talking to someone who’s constantly trying to prove to me they are smart(er).
Yes! Literal copy paste from my session with Opus 5.5 this morning:
> You gave me a long answer but it didn’t answer my question: can we safely remove $X
Agree I do not like the way Opus 5.5 communicates with me. It feels like it is trying to act superior in a condescending way. DO NOT LIKE. I have not used Argon yet but Gemini 3.8 Flash has been a joy to use in comparison (and so fast!) as a daily driver.
>...trying to act superior in a condescending way...
Strange. I wonder why? Have you adjusted 'roles', added 'ask clarifying questions'? This is most peculiar.
Sarcastic? wdym?
Opus has no sense oh humor at all. I wish mine was sarcastic.
I have used it as well. It is very dense, but it cites with evidence and has a direct and straightforward approach and manner. It doesn't really have a "tone" or mannerisms the way Opus 5 did. It follows instructions and sticks with pretty clean language by default. Argon is solid and handles long horizon well.
Em dashes are underappreciated — they are visually pleasing, not cluttery like semicolons or definitive like colons.
in all of my writing I use parenthesis in basically the same way (you know to explain the addendum of information).
Em dashes are commonly used in most countries that speak English. I get the feeling that people from the USA don’t use them often and now have tarnished the use of them.
Do you remember when the AIs were all writing with bullet points all the time? I miss that! I'd prefer that to the current more-information-density-than-any-human-would-ever-muster approach where sentences are packed so deep you can't get any meaning by skimming.
The three great epochs of AI:
Way too many bullets -> way too many emojis -> way too many em dashes
I have a pet theory that there's some overlap of writing patterns that A) indicate quality, clear communication and B) those that the LLMs have been converging on. As a result, we're ruining the reputation of some great writing patterns because we've associated it with AI slop.
My pet theory is that this was happening, because labs naturally worked to source highest quality data sources they could get. Even if they initially slurped everything indiscriminately, the obvious next step is to sort them by "quality", and bias towards the high end while trimming from low end.
So obviously, government documents, top publications from liberal arts scholars and STEM > Wikipedia > Reddit (and within, r/AskHistorians > r/all, etc.). The first group is the one most likely to (over)use correct / fancy typography.
I have a related pet theory: the telltale sign of AI writing is being too good for the context it's found in, in terms of vocabulary and style as well as structure.
What is jarring about this is precisely the quality - we associate certain styles with important (or self-important) venues, so it's uncanny to suddenly see random shitposts, copywriting spam, or regular chat replies look like work of NYT bestselling authors or legal scholars - it feels wrong in places we expect basic language, grug-brain reasoning, and lots of typos.
Also, what makes the above much more noticeable, is that AI systems don't have cross-session stylistic memory. From the POV of an LLM, that perfectly fitting, masterfully crafted starter is the first and only time it used it. From the user's POV, it's the tenth time they're seeing it in the span of an hour.
There's also no voice. If I get a text from a friend, I can hear them. Their tones, their mannerisms. I can't hear Claude because it's not a person that I've met and interacted with.
AI writing style comes from post-training, which is entirely under the control of the labs. It's not copying it from any public sources.
I didn't say it's copying anything. It's learning styles - all of them - in pre-training. Post-training is just about giving it the right defaults.
Doesn't change my point anyway - even if post-training, labs naturally want the LLM to come off as smart (well, except for Grok). Post-training is where they "untrained" em-dashes and such, due to complaints of users.
I'd heard an interesting theory that in training on customer chats there's a lot of interactions like this:
Agent: _wall of text_
User: yeah sounds great, do it
Which has the predictable outcome of biasing towards wall of text responses.
Continue to write with em dashes. Be the change you wish to see in the world.
I did at the beginning, but there is only so much you can take, and no one cares if you have read books and seen it before. There were a bunch of programmers whom I thought would understand em dashes, but they did not and said no one uses such dashes only normal hyphens.
Em dashes ended up as one of the first victims of llm age
Em dashes were already "doomed" at least half a century ago, because they didn't make it into ASCII character set - which arguably is just because they were seen as redundant on ancient typewriters; that's also why they aren't there on normal keyboards, which was a double whammy: inconvenient for programmers to support, inconvenient for users to type.
In other words, since the very start of computer era, the use of em dashes was always a signal.
At least with a typewriter, if - was sized right you could -- for an emdash.
They were in models in the first place because they are used ubiquitously published formal writing. As, of course, are semicolons. There is nothing wrong with using punctuation for nuance and structure.
I feel like the LLMs used them "wrong". They use them for hard breaks for disconnected thoughts rather than one thought that disconnects.
I think they just used them too much. I suspect that as they naturally form a thematic break point in writing, their probability was unnaturally high at the end of phrases and this led to overuse.
I don't ever recall seeing an LLM using them incorrectly, as opposed to simply at high frequency but correctly (although I'm sure some low param or early models must have done so at some point).
The dashes reveal AI generated text. For me a trigger to further ignore the text.
Because you ignored them before someone told you about them.
can confirm this. I didn't have high expectations for AI writing good long-form content in Indonesian. However, I recently tested it on some of my Indonesian writings and asked Argo to rewrite them while maintaining the original tone—and it absolutely nailed it.
> Well-written prose is far less likely to go viral but far outstrips the importance rendering a fantasy world in one shot.
I'm not entirely sure you're correct. Your claim boils down to "Writing natural human prose" vs "Broad use of complex software and spatial awareness". My gut feeling is that there's more untapped and fruitful applications in the second group than the first - at least in the short term.
I missed the em dash — ngl
As a longtime user of em dashes, I refuse to change my style because bots happen to agree with my choice of punctuation.
Well, I have to change the way I write at times because my comment gets automatically flagged. :/ Not fun.
this happens to me as well, meanwhile Pangram claims their false positive rate is 1 in 24000 as if that should be practically negligeable. I bet it's closer to 1 in 10 for gen-x'ers
It cops hate because people try to pass off LLM written text as their own, then get pissed when called out on it.
If you just want good llm text, it’s great.
me too. it's like a fingerprint of ai. I think ai should be proud to be itself and be ai.
Yeah, I never got the hate. If it reads well who cares if it's not something a human does much?
I think there was a lot of misplaced hate of the em-dash due to all the low-qualify AI slop people were trying to pass off as their own for a while.
The worst part is that it seems they just regex it with the semicolon, making the text harder on the eyes
I was kinda hoping I'd get to the end and it would say, "In fact, this entire post was drafted by Argon!"
What was botched about it?
Announced the model two weeks ago to a ton of hype, model was unavailable for anyone to try. On twitter, Google folks went on a great run taking shots at Muse ("Muse who?"). The hype died down bc nobody could touch the models and meta/muse came out with a similarly capable model in the meanwhile.
At the same time, OpenAI had a disastrous dev day that led a lot of users to jump ship to Claude.
Couple weeks later, Gemini-4-argon is still unavailable on anything less than the ultra tier, which means there's no natural funnel to get ppl to pay for the more expensive ultra plan other than "trust us the model is good".
If Google had argon broadly available earlier this month they would have picked up a ton of new users.
As someone internal at Google I don't understand why there's so much criticism of Google models... but right now I'm using the next model beyond Argon, and Flash 3.8. In my personal Gemini app I only have access to 3.5.
Models move so fast now and Google is very cautious about releasing them.
I think google makes great models, my favorite models have been google models. My critisisms are with the infrastructure around the models (or lack of them),things like billing, access, changes to terms and limits, pricing etc. All of these have shown either inconsistency, lack of care, sometimes confusing offerings, and things that felt like rug pulls.
Please google, just make it easy for me to give you money and get what I paid for.
For agentic coding, Claude Code with the latest Fable or Opus is still just better. That's what people care most about.
Unless Gemini CLI greatly improves and Argon works well in it, this probably won't make a dent in the coding market.
My complaint, and the complaint of many is not the models - they can be good to very good depending on use case.
What sucks, hard, is googles ability to deploy direct to devs and customers at scale when the deployment is not a longstanding Google project.
Case in point: I’m an ultra subscriber and see in my app right now an ad to try Gemini 4; meanwhile the only models I have available are 3.1 pro,3.6 flash, and 3.8 thinking.
Clicking the try gemini 4 link takes me to a demo prompt that loads 3.6 flash. Srsly
I won’t even start telling you what getting api key based access together was like a year ago.
People have very short attention spans and cheerlead whoever is winning at any particular point in time.
"I don't understand why people don't like our models but we can't get our act together on the products they use those models in"
Uncharitably misreading things on the internet and getting upset about them is not the best hobby
I agree that reply was unnecessarily rude, but... idk man. I'm a huge cheerleader for Google [1], use 3.1 Pro via AI studio, I'm on a Pro plan... and I can't access this model? Not even a "4 chats per hour" type thing?? I mean, I have video generation access for some reason (no good for me), but Google's policies are baffling to say the least.
Ofc I know this isn't your fault in person, but I really wish Google would get its shit together.
...As a long time tech enthusiast I should probably stop being so delusional. Heh (cries internally)
1: lol I know who cheers for a trillion dollar company but I'd rather not cheer for OpenAI
Yeah, what I was trying to say is "the criticism doesn't make sense to me because I forget because I have access to the good stuff."
As a Google employee my defense is it's hard for a company this big to move fast, but I can understand how Google customers would be annoyed.
Didn’t check agy or AI studio, but at least Argon is available in Gemini page/app.
There's nothing unnecessarily rude about my comment, they are literally unable to get their act together on product.
The top level comment blamed cheerleading but let's apply critical thinking for a second as product people and look at the funnel:
1. Potential users sees new release 2. Potential user takes time to install and set up Google harness (you already lost 90% of people here, the funnel better close quick!) 3. Potential user cannot use new model.
Then what? You hope they developed good will towards your model through this experience?
You stuck a two week wait in your funnel? Is that the definition of not having your act together or what?
We're on HN, how many of us have heard from Google staff this kind of stuff is a result of them growing so big that AI Cloud has to compete with Vertex Cloud, and AI cloud exists because Vertex Cloud needed 2 years to get OAI/Ant style API keys working and were losing dev mindshare in the meantime?
Why are you doing that then?
Because Google models are crap. They are on par with local shit models. Last time I tried they were unable to even hold a longer conversation, because they would enter loop and reiterate last answer regardless of the prompt etc.
Then basic mistakes (inferring wrong information from the given text, hallucinations...). Gemini is okay for some absolute basics and the rest of the work goes to actual frontier models.
How long ago did you try it? Gemini models seem extremely capable to me. I've been using them in ag for over a year and to me the results are great. switching to opus in ag doesn't produce any better results for me
All the power users are always jumping around by now and many hold the smallest subscription with all the models or at least two.
Narrating this as if it’s the game of thrones is a fun exercise, but it’s just becoming commonplace now that models jump ahead of each other on a weekly to Monthly basis.
> and many hold the smallest subscription with all the models or at least two
This was part of the issue, as stated in their comment. We couldn't jump around to try it for the reason that is wasn't made available to, as you say, "all the power users".
You're not convincing me. The war for compute within google is well known by now. Every single product decision stems from that.
Do you really think people in google are happy about how long it took to release this model?
Why should I care?
Just for the record, I'm a Spanish user with Google AI pro (€20 per month or so) and my app has Argon now. (Yay!)
Why did people jump to Claude? I redirected company resources to OpenAI Pro 500 accounts and away from Anthropic for most use.
We’re all running on Astra medium the whole week and loving it.
I’m curious what I missed.
I'm still a codex man myself but lots of people feel like opus 5.5 just gets it for some projects in a way that astra doesn't. Especially given the fact that astra and sol 6.1 are often at capacity late at night during US hours, when lots of people want to run longer unsupervised projects while they sleep. There's a general sense I've felt reaching some comment threads that openai failed to react with something better than opus 5.5 and are rushing to release small tweaks to make people feel like they're still getting something new.
Sol is just hilariously slow. Even Luna is slow. It is unusable compared to Claude. I was pretty unhappy with Opus 5 and really gave OpenAI a shot for personal projects, but their quotas are just too small and their capacity issues in the last few weeks have just been untenable. No good. Opus 5.5 is just a very good model, very efficient with usage, gets things done at a reasonable speed. Sonnet 5.5 is also a very nice worker improvement. It is probably on par with Opus 4.6-4.8.
The sense I get from your comment and the GP comment is that OpenAI’s pricing changes alienated individual users for the sake of the enterprise market. I live in the enterprise market, and was quite pleased with the changes. We don’t really use Sol 6.1, just Astra.
$500/month for 40+ hours a week of Astra usage, with ultrafast mode available when you’re near the weekly reset has made our jobs so much easier. Our product has some sciencey bits as well, ands Astra kills it as a rubber duck in this area.
As a consumer whose 20x account got downgraded, I am a bit sad. I can’t justify $500 a month on personal AI spend. As an engineering manager, I could not be happier with OpenAI’s services. Also, Codex App Server is amazing.
> Also, Codex App Server is amazing.
Hey, account created 39 days ago. Did you really have to tag that on after all the other stuff? Could you make it any more obvious?
That said this thread is hilarious because it's just an open ai sales manual debating with an anthropic sales manual. It's funny, but it's not, because the truth is that you people are just shitting all over the commons. Someone needs to kick you out but that's not going to happen here.
:shrug: I spend a lot of those token make local models better, resurrecting old hardware to make it more accessible and usable. Frontier models and AI is cool. This is my lived experience doing real systems engineering projects. My real sales manual is: https://stealthgoat.com/deepswe-flash-next.html local models are kind of awesome now and everyone should be using them in addition to frontier models. I will build the stuff I want which includes optimizing local inference engines until they are at my hardware ceiling. I use this capacity in my house to do reversing, security research, and internal OpenClaw type stuff I find useful.
They are all very slow at the moment. Some tasks are taking hours to even days. These are DevOps related tasks in which I create hundreds of servers in an agentic way. It works. But it's slow. That's still annoying.
Gemini 4 is announced be available to Pro users in the Gemini app.
I mean, I heard rumors too that it would be ultra only, but that doesn't seem to be the case, unless you are specifically talking about antigravity?
Realistically, market adoption isn't going to be the result of terminally online people (like me, I confess) following things way too closely on social.
There weren't "rumors" that it would be Ultra only, that's what Google has announced two weeks ago. Happy to see they've went back on that, but I haven't seen Gemini 4 on any apps till now, and I'm a Google AI Pro subscriber.
edit: just a few hours after this comment I got access to Gemini 4 on the Gemini app. Yayy! :)
They never said it would be Ultra only. They said, "starting with paid API customers and Google AI Ultra subscribers" in the initial announcement.
https://blog.google/innovation-and-ai/models-and-research/ge...
That's correct, I've misquoted it. Although, if I understood correctly, it got released to all plans together today right? Or did AI Ultra subscribers have access to it since the first announcement?
am on pro. updated my apps. no g4. sadge google.
I got it within the last hour, it is still rolling out.
On pro as well. I have "4 Argon" in my Gemini web app model dropdown.
> At the same time, OpenAI had a disastrous dev day that led a lot of users to jump ship to Claude.
Can you elaborate on this?
There was some backlash around the pricing changes - the $200/month tier offers less usage now, at the same time as the launch of the $500/month tier.
Reddit thread with 247 comments here: https://www.reddit.com/r/codex/comments/1wtgv3z/dev_day_l/
Aside from that I'm not sure what was "disastrous" - maybe the lack of a new model release to compete with the just-released Claude Opus 5.5?
I went from using GPT exclusively to cancelling all my subscriptions with OpenAI.
'backlash' is underwhelming. They single handedly murdered 5.3 codex which was great by making it slooow and duuumb, and pushed people over to their new ones. 5.6 was a decent candidate but it was so slooooow.
And then double or triple the cost for Plus and Pro by nerfing their effective power? I never looked for an alternative faster. They really screwed the pooch.
Been quite happy with deepseek v4.1 flash, muse 1.3 and haiku 5.5.
"Disastrous" is obviously a loaded term, and it can be debated, but from what I collect, the reasons are
- Dots. People don't understand it. Their presentation didn't explain it properly at all. Additionally, many people thought it's stupid, and that the cutesy mascots are dystopian.
- Voice mode didn't work in their presentation. The demo failed.
- 200$ plan goes 20x -> 10x usage. 500$ plan introduced, 25x usage. The contrast is stark.
- "Ultrafast": 8x pricing on subscription plan (6x API). That's 80$ input, 400$ output, 100$ cache write. Hello?! Additionally, since it generates 8x faster, it consumes your quota 8x faster. Per wall-clock minute, it's 64x more usage spend per minute of generating. People report that on the 500$ plan, their weekly usage is gone in 40-100 minutes.
- People believe gpt-6.1-sol was planned to be released as astra-minor, but OpenAI renamed it at the last possible time, because gpt-6-sol is a disaster (this I can get behind)
Pulled astra 6.1 at the last moment (allegedly) and only released a dirty looper sol 6.1. Also released dots which are unavailable in EU. There may have been more but no one cared.
and dots are only on $100+ plan.
"model was unavailable for anyone to try"
Oh yeah. Completely forgot about that. Big headlines and big claims, but no one could actually use it and report on it.
Does seem like they don't quite grasp how the hype train operates. Or maybe they don't care as much.
I added Gemini 4 Argon to our video creation benchmarks.
The results are a bit disappointing given Googles scale and training data.
The results are on par with cheaper Chinese open source models to be frank. https://cliphou.se/benchmark/
Unpopular opinion: em-dash is fine
> That said, the em-dash is back w/ this model.
noooooooo
> far outstrips the importance rendering a fantasy world in one shot.
Given FelonyBench, do we also have a FantasyBench?
I just created a Google account, which was a hassle. I tried to find an app and found AI Studio, but I’m not sure what that is. Then I found Antigravity, which apparently is Google’s alternative to Codex, and I installed it. Then I tried to buy the small Gemini AI Ultra package, but it didn’t let me pay. It accepted neither my credit card nor my PayPal account. It’s crazy how bad the Google experience is nowadays. You don’t know what to use or where to find it, and the security measures seem totally overkill. Am I really the only one having these experiences? Is Google aware of that?
I bought a google play card (or whatever it's called) with cash due to these issues. Result: Instantly rejected + locked after entering the code, no credit, no dispute, no customer service, no refund.
I am not touching it for that reason. One wrong step and the whole gmail account is locked without any recourse.
Time for small-claims court.
Yeah being presented with these options is such poor UX:-
- Try in Gemini
- Try in Google AI Studio
- Try in Google Antigravity
Will non users of Google products even know what these three things even are?
Also "available today for Google AI Pro and Ultra subscribers".
I have AI Pro, but AI Studio tells me I can "unlock" Argon by upgrading to Ultra.
I have AI Ultra and agy wants me to upgrade to... AI Ultra.
"You've had one, yes. But what about second Ultra?"
Same here. I had not opened antigravity in two months so I thought it was an update issue. But the app was updated. Argon is grayed out
Don’t stress. I have ultra and the “try Gemini 4” only loads 3.6 flash.
Same - it doesn’t swap the model.
Classic Google.
Same for me. Pro sub but the best I can access is Flash 3.6
Irrelevant. The target audience was Google VPs.
It is mind boggling how difficult it is. I should be able to go to some well known URL, e.g. gemini.com and have the same experience I have on claude.ai or chatgpt.com. I can't wrap my head around why they are over complicating this. And it should largely feel this same as when I install 'Anti Gravity' which is just a crazy bad name. Why isn't it called Gemini Desktop?
> It is mind boggling how difficult it is. I should be able to go to some well known URL, e.g. gemini.com
What’s so complicated about going to gemini.google.com ?
> Why isn't it called Gemini Desktop
There is a Gemini desktop app. Antigravity is for agentic stuff, similar to Codex.
Same thing, I've always paid my bills, I have had some sort of google subscription for the last 10 years. Now they're requesting me to switch to pre-paid billing. They won't accept my card (that has worked every time this year so far).
Is there anybody in Google having a look at how bad and complicated their payment system is? In all the other AI providers you just sign in, pay, get a key, done. In other clouds like AWS, you add a card, pay little to verify the card the first time, monthly bill, done. In Google it looks like you have to fight them every time for the very thing they should make as easy as possible: opening your wallet.
I'll believe in AGI when Google can figure out how to leverage their model to make their user experience not suck.
But will it bring more profit?
I have Ultra and went to try it and...
"Unavailable · Gemini 4 Argon is currently available to Ultra plan subscribers."
yeah ...
i live in French Polynesia, a French territory, i'm a Google AI subscriber and i can't use Google AI Studio or antigravity cli because i'm in a geo restricted area ...
It seems Google has no issue laying out cables across the Pacific Ocean from/to Tahiti, they don't have an issue selling their services and plans there but i can't use AI Studio or antigravity cli because of geo restrictions ...
Reminds me of that email from Bill Gates about trying to install Movie Maker.
This is the first time I'm hearing about this email (thanks!) and I'm wondering: If it was already known in 2003 how bad Windows was, how did we end up with a Windows that's so much worse? Bill said it was his job to send such emails but apparently he didn't do his job very well…
For the lazy:
https://www.osnews.com/story/19921/Full-Text-an-Epic-Bill-Ga...
I agree 100%, it’s a mess
That said, asking Gemini.google.com to walk you through the steps seems to suffice. In fact, one of AIs big perks for me is walking my through kafkaesque systems like Google’s to cut the shit and get to the result
In the same boat, I have a single seat Google workspace account, and can't figure out how to add Argon to my workspace. Asking Gemini seems to make it hallucinate pages that aren't there.
Also single seat workspace user. Half the time my Gemini forgets I connected Gmail to it and hallucinates false instructions to enable it.
Truly crazy how much Gemini still hallucinates, especially things which its own Google documentation or just a basic web search away. I feel it’s mostly solved problem with chatgpt/claude , but for me, with Gemini it’s almost guaranteed it will just make stuff up
I recently tried to create a new Gmail account for a small app I've made. I wanted a way for users to get hold of me other than my personal email address.
They now ask for your mobile number during signup. And it wouldn't let me register any more accounts with my number. So frustrating.
I think it’s a typical strategy for scammers to create a new account and then immediately try to buy an optional service with a stolen credit card.
What you did isn’t wrong, but is unfortunately very similar to what real scammers do. The heuristics behind these anti-abuse efforts are always going to have false positives.
Same issue here, didn't accept my credit card. Didn't say why either.
You didn't have a google account? Impressive feat
Nope. This is exactly the same way they fucked up when they had the model quality lead with Gemini 2.5 pro.
They made using it so hard/unclear that myself and every other dev I knew gave up.
It's clear that they still haven't done any organizational self-reflection.
First install Antigravity and see if it works on Free with your account at all. You can buy Ultra and then find out you don't have "Antigravity eligibility" on a given account or it will ask you to send SMS to some nonexistent number.
I would also advise against buying Gemini if you have any other Google services, because you get banned across board if they don't like something. To use Google it's best to first migrate off of Google.
Otherwise great product, I really like and would recommend Gemini. The usage is also very generous, at least Flash models.
Side story/rant
I recently had to verify my YouTube account with a phone number recently because it requires this in order to set a thumbnail.
Questions right off the bat:
1. Why do I need to verify to post a thumbnail that will take 1MB on their servers but not to post a video that will take 100MB on their servers and be public??
2. Why do I need to verify my phone number on YouTube when it's linked to my Google account which has a verified phone number and 2FA etc.?
In any case, it takes you to a CSS-less page that asks you to choose between getting an SMS or being called to get a code. I choose SMS and it doesn't work. It shows an error message with no description just "something went wrong". I try a few times and it keeps not working.
I then switch to the call, and guess what! I have tried "too many times" and will have to try again later. Call worked the next day, but geez, what a mess.
Just wait until you try to use the Google Cloud Console
Using google services has always been miserable. It used to be way worse. Now you can at least get an api key from AI studio without going through 20 esoteric steps to configure everything.
At least now they don't play the "client side key can now access your very expensive resources" game anymore.
If you're not trying to buy ads, you're not really a customer; you're a product they will sell to people trying to buy ads.
Yeah, a month ago I wanted to test out Gemini-3.5-Transcribe, but had to wait a completely indeterminate time, got the offensive message "Your Google Cloud billing account is being processed. Processing time varies from a few moments to a few weeks.", like I'm just worthless scum. Took over a week for no reason.
Ended up with Soniox instead, took minutes to get started.
I hate everything with Google and consumer facing services, they dont give a shit because 99.9% are monetized through spyware or are buisness customers.
They had gemini cli, what happened to that? Was fine
It fell victim to Google's long-standing strategy of building everything twice and then killing the better option.
To be honest, Gemini CLI was disastrous. The Antigravity CLI is much better.
> The model launches today at an introductory price of $2 per million input tokens and $10 per million output tokens (with cached input tokens at 95% off).
Couldn't you just say what the price will be after the introductory period and when it'll take effect? Do they have some SKILL.md for these blog posts that requires the most important information to always be buried ten levels deep and hidden behind a login page?
They're doubling the price on December 1st: https://ai.google.dev/gemini-api/docs/pricing#gemini-4-argon
$2/million input and $10/million output today. $4/$20 after December 1st.
With the pace they're delivering models, there's gonna be 1-2 new Gemini 4 versions by December 1st, presumably with their own introductory price windows. We just got Gemini 3.8 on 2 September and Gemini 3.7 on 13 August.
This reminds me of telephone commercials that ran back around 1987, saying they were offering long distance calls for 10 cents a minute (a pretty good price back then) and -- this was emphasized, a huge selling point -- they guaranteed your rate would never go up. In retrospect, all the lols :-)
I wonder if that’s more to price in the cost of keeping old models around on expensive inference hardware. It’s not like old models get cheaper to run, and a more heterogeneous fleet is less efficient to manage.
Their last non-flash frontier release was 3.1 Pro back in February. Two new frontier releases at that pace would be Feb 2028.
December 1st is just ~6 weeks away. There's no way Google puts out even 1 more Gemini 4 version before then, let alone more than 1.
No they've been releasing models super rapid fire. Other companies too. 6.1 sol was barely a week after 6.0 sol
Don’t underestimate the power of exponential growth
You're conflating Gemini Flash with Gemini Pro.
Opus 5.5 is also priced at $4/$20, but are they on the same level?
At that price I think you'd pick Opus 5.5.
It's perhaps a face saving thing to pretend you'll charge the same. Let's see if it actually happens.
>$4/$20
Is Elon Musk doing their pricing?
That is the standard pricing for Opus-class models these days.
Blaze it, Robot!
4/20. It is to Musk what 67 is to children.
I think it's more 420 or 4:20 (marijuana reference) than 4/20 (Hitler's birthday).
Although given it's Musk, others may disagree.
GLM 5.3 is roughly Opus class and is much, much cheaper
It’s more expensive than 6.1-sol though? At least according to benchmarks.
I think at this point comparing raw token pricing is often apples to oranges.
Opus 5 class yes, but opus 5.5 is functionally mythos tier
Opus 5.5 class?
No, since that's a moving target. But the large GLM models are likely in a similar model weight class to the Opus family, just a few months behind
No, and anyone who says otherwise is being silly.
It’s still damned capable though, I’ve done a crazy amount of good work with 5.3 Flash.
There is no such thing as "standard pricing" for products with different performance profiles, architectures, inference costs and infrastructure stack
It is price fixing, nothing else
> Couldn't you just say what the price will be after the introductory period
+1. FWIW, I didn't even get far enough to wonder about the price.
The lead (and only) sentence states "Our next era of frontier intelligence, built to solve your hardest problems" a statement equally true of any new Google or competitor model, hence completely devoid of useful meaning. I then kept scrolling down... and down... and down, giving up after a half dozen screens of big images, huge videos and some customer stories. The first sentence of every text block was equally vague or meaningless so... tv;dr (Too vague, didn't read).
Even the Gemini sub-reddit is laughing: https://www.reddit.com/r/GeminiAI/comments/1x2dr47/the_lates...
Me, watching the AI world from the sidelines while using DeepSeek at $0.15($0.003 cached)/$0.60 - still bitter about the pricing changes.
It might not be as smart, though my employer gives me access to Claude, so I tried giving the same task to Opus 5.5. Both DS4.1 Flash and Opus5.5 produced nearly identical outputs.
After that, my company switched the entire engineering department to Azure hosted DeepSeek4.1 and costs were cut by 3/4. So far, no one has really noticed the change.
It's irrelevant information, they know the reader switches models every week.
They don’t really. Most normies which are the vast majority of people just use ChatGPT. All tech folk just use Claude.
Normies aren't the target market for Argon anyway. The Gemini app is still serving 3.6 Flash and 3.1 Pro.
> They don’t really. Most normies which are the vast majority of people just use ChatGPT. All tech folk just use Claude.
It was the case a year ago, but it's less clear cut nowadays, I've seen non-tech people using claude on their phone in the train, and many tech people seem to have switched from Claude Code + Opus to Codex + Sol over the past few month.
Normies are not the target for a frontier model
they are target with 6 month lag. Say, if current Gemini will be demonstrating obviously superior perf compared to Opus, many normies devs will switch within 6 months.
Sometimes it seems like ChatGPT and Claude are releasing models every week.
Yeah, then I guess the GP was technically right it’s just people aren’t jumping providers constantly is what I was trying to get across.
Obfuscating token cost is the new great marketing frontier
They do, just not in that sentence. You must really hate sales ads.
Gemini 4 Argon is a new leader in Pareto-frontier. Looks like, Google is back in the race.
https://arena.ai/leaderboard/agent/chat/pareto
For almost same intelligence (+15.56%, +15.18%), drastic difference in cost/task ($3.51 vs $0.41)
Why do these benchmarks often show models running on Max i.e. Opus 5.5 (max) is on the frontier? I would think it is never really economical to run max
By definition, the model with the best performance will be on the pareto frontier (regardless of cost).
No. Pareto frontier is cost performance tradeoff.
What jonas21 is stating is correct. If you have the absolute best performance, you are automatically on the Pareto frontier because you’re the most cost effective one (or the only model) on that performance tier.
Huh, I guess you're right, but I interpreted jonas21's comment in relation to the above comment, which was about all models running on Max (which implies this would be a comment relating to all points on the pareto curve).
> Gemini 4 Argon is a new leader in Pareto-frontier. Looks like, Google is back in the race.
For chat, yes. But switch to code on the same page, and the picture changes. OpenAI takes the lead.
Every release I see a new benchmark with a new frontier. These graphs are just meaningless now
In particular, where cost is the x axis, a well-funded company with a good but not best model can discount their new model until it reaches the frontier. In some sense this is as much a statement about the efficiency of markets in the presence of public measurement-based comparison and public list pricing as it is about research pace.
While true, Google genuinely has invested the most in computer and energy efficiency for LLMs.
you can give it a meaning if you see the relavivity between models. no need to disregard the stat at all.
Muse reminded me we should have a time based chart as well - this year I hope we will begin to see time to completion stats at various intelligence tiers. The waiting makes a lot of things more tiring.
I’m curious at the enterprise level does Anthropic have staying power. My sense is when you use it with your own custom harness, it doesn’t, I can switch between Opus 5.5 or Gemini Argon and not really care as long as capabilities are similar. This is good for the consumer, and bad for the model developers.
People should appreciate Google more for how all of their models have:
- Natively multi-modality
- Enormous context windows
- Reliably cheap prices
It's like they don't release a model until it passes the above checklist.
Most providers have some mixture of these three occasionally, but with Google, every model is consistent. This kind of consistency is really nice to have.
Good point. On that note, it may be a bug but I noticed Argon compacting well under the 1M max in Copilot (around 400k) -- when set to 1M context. That may be a Copilot thing but I've never seen that with other models running 1M context. Could be a day-1 bug.
Outside of that, behavior is quite good, a bit slow, and definitely different than both GPTs and Claudes -- which is appreciated.
Image->html test for Argon. I'd put it at B tier, just under gpt 6.1 sol. It isn't A tier like Opus 5.5 or Astra.
Argon build: https://html.non.io/lcars-argon/
GPT 6.1 sol: https://html.non.io/lcars-gpt-6.1-sol/
Opus 5.5 (currently SOTA for this): https://html.non.io/lcars-opus-5.5/
Original designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
I don't know how you rate, but the Opus 5.5 is literally stuttering and lagging when scrolling on a Macbook Air M3 (for me).
(Chrome, also incognito)
More info on this - I ran a few tests on my mac and linux systems with chrome/firefox. I highly suspect this is due to gpu acceleration being disabled. https://image.non.io/e7165331-432b-45c3-986e-c2973f12d377.we...
As soon as gpu acceleration is disabled, the webpage goes from 60fps across the three systems I tried to ~7-20fps when software rendering is forced.
I suspect if you go to chrome://gpu you'll find something different from what I have here: https://image.non.io/a47a1b03-1b87-4fd5-92a2-b39e62b1cfd9.we...
Nice research, you are right! Seems that low power mode was on. I have been using the web, Netflix, Apple TV app, and anything else just fine without ever noticing a thing though.
So that all being said, a normal website should work totally fine on low power mode, and this should definitely be a negative on the Opus implementation.
> and this should definitely be a negative on the Opus implementation.
I would disagree with this based on two things:
1. the prompt, which explicitly states to render the planets in the background in 3js (thus the gpu implementation).
2. the purpose of this, which is a test of its ability to convert an image design into a html/js/css implementation that matches it as closely as possible. I've talked about this before when it's come up[†]. The purpose is to be a good test, not a good design or website.
For reference, here's the full prompt used:
> Create a web page implementation from the following instructions: https://diffui.ai/build/Futuristic_Interior_UI_build.md?auth... dont reference any other folders that have similar builds. Focus on the flowing SVGs. Animate elements that make sense to be animated. For the starships that have planets in the background, ask for high quality images with the windows as a transparent background, and recreate the planets behind them in 3js. It should be one single page application that I can just open the index.html file to view.
[†] https://news.ycombinator.com/item?id=49997135
[‡] here's a copy of this since the diffui build instructions wont be accessible without an account: https://non.io/lcars-build-instructions
Interesting! Do you have webgl disabled for incognito or for chrome in general? I wonder if it's trying to do software rendering.
FWIW, I have literally the exact same configuration (M3 air, google chrome). Here's a recording on mine where I'm seeing 60fps: https://non.io/video/video-3587018401.mp4
Mine doesn't stutter on on Firefox on M1 Max, but it pins 7 cores the entire time the page is open, not just while loading. It also uses 50% of GPU in addition to that.
Edit: The argon built site pins 1.3 cpu cores. The Sol 6.1 site uses 0.18 cores.
Unless it is something with the playback on my end, your animations and scrolling are a bit jerky too.
The Opus 5.5 implementation is noticeably slow on my machine as well (Firefox, Fedora, Gnome Wayland, AMD 6800U).
Mac Air M1 in Safari here, buttery smooth.
Fine on arc search (which I think is just WebKit) on iOS.
Maybe a Chrome bug? It's fine in Safari on an M1.
Also horrifically sluggish on Firefox on Linux.
Also stuttery on Edge (Chromium), so there might be a pattern here
One interesting observation - it's the only one I've seen make an ASCII representation of the layout of each of the pages before building: https://image.non.io/46e0cce3-34d9-4fcb-8775-bfc2c70bc4bb.we...
That's really interesting I think- literally putting the layout into ascii language to reason about.
Opus one has by far the worst performance, the only one that's stuttering and making my laptop fans very noisy.
Argon's build is pretty much unusable on mobile, the header takes up half the screen
Yea its attention to detail was much less.
Here's a vid of both opus and argon side by side, which might be more viewable on mobile: https://x.com/pwnies/status/2110058987462988152
B tier? For me the argon one is really quite bad on mobile; the nav is messed up enough to make the page kinda unusable.
Off topic, but I want to customize a starship now
Damn opus 5.5 is just such a good model - what kind of moloch sacrifices are they doing in there
Truly S-tier. It's sharp, creative, intuitive. Can't believe how much better it is than Opus 5, then they went ahead and made it cheaper and faster too.
I'm constantly blown away by it. I do think Astra is slightly more intelligent (although worse at this particular benchmark), but Opus 5.5 has become my default model for 99% of what I do. It's so good.
That Argon build is really bad on mobile fyi
Unsurprisingly, the pelicans are excellent. Google are the only lab who have publicly shared that they have specifically trained for SVG output - https://twitter.com/sunjiao123sun_/status/202455551655137292... - and they've had great pelican results in the past too (which they have shown off themselves - https://twitter.com/JeffDean/status/2024525132266688757).
Here's low, medium, high. Even low is better than pelicans from any other lab: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
low cost 10.185 cents, medium was 11.098 cents, high was 17.166 cents.
The costs are similar because it used more output tokens than reasoning tokens for all three pelicans, even for high.
I have a silly pet "project" that I've been using with different models, where I provide an image of a patch sheet for the Behringer Poly-D synth and ask for an html version and this is the first time I get something that, while still needing some tweaks, basically looks like the patch sheet. Before this, the closest had been Fable, but that still was nowhere near the actual patch sheet.
This is the sheet: https://fernandoipar.com/content/music/poly-d/gemini-argon/c... (the annotations on it are by me)
And this is what Argon created: https://fernandoipar.com/content/music/poly-d/gemini-argon/b...
For this simple prompt: https://fernandoipar.com/content/music/poly-d/gemini-argon/p...
I'm honestly impressed.
Me in 2025 https://news.ycombinator.com/item?id=44759781
> If it's on HN and is a meme at this point, it will end up in the training set.
> It's kind of fun to imagine that there is an intern in every AI company furiously trying to get nice looking svg pelicans on bicycles.
Google listened and assigned a few interns, I guess, for the task
That's exactly what I was thinking when I saw https://www.assbench.com/ posted a while back: "oh no, this is going to be somebody's job".
Unfortunately, for me the renderings crash or hang in both Firefox for Android and Chromium/Vanadium on a Pixel 10 Pro. :\
omg that's hilarious
prompt is to detailed (llm written?) better test of model creativity is to not constrain prompt
NGL, I think this might be my favorite benchmark, ha. While the Chinese labs asses are a bit off, I certainly have big hopes for their next versions. Their hearts are certainly in the right place.
It's the smaller training data /s
SVG is useful for actual tasks, not just looking good in a silly "benchmark"
Beyond SVG, these things have been RL'd on 3d reconstruction (which carries over), and I expect given the public reaction to recent frontier advancements in 3d capabilities that this will get allocated a larger share of the post training budget in the future.
What I'd kill for would be an app where you take a pic of scribbles and drawings on a whiteboard/notebook and the app turns it into SVGs you can then continue to manipulate digitally on PC/tablet.
Why would you kill for this? Why not just generate svgs with any editing software?
>Why would you kill for this?
Figure of speech.
>Why not just generate svgs with any editing software?
How do you do that from the whiteboard/notebook?
Inkscape and https://github.com/visioncortex/vtracer can both do this.
They can represent something in vector, but it's always very hard to use the result for anything else. Like, the paths doesn't make sense, aren't grouped to the logic elements etc.
ChatGPT does that decently well
Last time I tried, it did not. That's why there's a place on the market for an app just tuned to handle the transition seamlessly, rather than having you fucking around with prompts in LLMs.
I for one don't want a whole app for that, not anymore. It's a small task whose result is an input to something else - ideal for a tool for LLM (or script) to use.
>I for one don't want a whole app for that
Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.
> It's a small task whose result is an input to something else - ideal for a tool for LLM (or script)
Have you actually tried this? If it's a small task then why can't LLMs actually do it? Kind of like the pelican test. Every LLM can do a SVG pelican but most of the results are unusable in practice for production/productivity purposes.
Every LLm can extract some half-broken SVG hallucination of your jots, but I'm asking for an app that's polished and works accurately 99% of the time, which means a dedicated app/model tuned specifically for this task, and nobody makes this yet.
> Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.
I don't feel like I'm a representative sample of general userbase, so I won't dispute this too hard. For me, it's rare but it does happen. Still, 90% of my mobile experience is "how is this so broken, why is it missing the obvious follow-up feature that completes a workflow, and why, again, am I not using a laptop for this?".
> If it's a small task then why can't LLMs actually do it?
What if they actually can?
This is exactly what Karpathy was talking about the other day[0]. If your only LLM experience is the default chat app in chat mode, then this doesn't make sense. If your experience includes using agentic harness like Claude Code/Cowork / Codex/ChatGPT Work, then it's obvious.
The LLM app in "chat mode" will just zero-shot you a pelican, or digitize your notes into SVG, and maybe improve on it a bit if you complain about the result. The same app[1] in "agentic mode" will digitize your notes, refine the SVG against the original image, reduce it to probable strokes based on high-frequency patterns in the photo and knowledge of how people write, OCR it, save both the text and SVG to your library, then extract parts you wanted and insert them into a document, typeset the rest of it, and print it on your printer - all in response to a single prompt.
LLMs in agentic mode are very good at stringing long and advanced workflows; today, if you give them shell access or computer use, they will use regular apps for you if that's the easiest way to accomplish the goal.
> Have you actually tried this?
Yes![2].
That's why I can confidently say: there is no limit to this pattern. LLMs can handle apps just fine; giving them tools just saves time and tokens.
Another example: I'm currently resurrecting a 30 year old game, and the other day I asked Claude to hack on a way to run it headlessly, so I can use my computer while it plays with it. It did, and then immediately leveraged it to just causally run several instances of the game in parallel, entirely in the background, whenever it needed to playtest something.
--
[0] - https://news.ycombinator.com/item?id=50047422 - was on the frontpage two days ago.
[1] - Claude and ChatGPT now have both modes in their mobile, web and desktop apps. I personally use Claude Code a lot, but exclusively through these surfaces (I can't be arsed to deal with TUI at home anymore). IMO, these tools are already within reach of regular users - the users just didn't notice it yet.
[2] - Not with SVGs, but equivalent with voice recordings, and yes, Claude Code set up and is managing the whole local processing pipeline. Recordings go in, Markdown (or more advanced signal processing) comes out, and in itself, it's not a task, but merely a step.
Of course it would be nice if google actually added SVG support to google slides...
We all said it: https://news.ycombinator.com/item?id=46978726 (8 months ago)
> How many pelican riding bicycle SVGs were there before this test existed? What if the training data is being polluted with all these wonky results...
But Simon has demonstrated before that asking for any animal doing something silly will still get similar results from the same model.
I recently learned that there is an animated pelican riding a bicycle in 1988's Who Framed Roger Rabbit! https://simonwillison.net/2026/Sep/18/the-creative-spirit-of...
Do you think this might be a kind of subconscious origin of your idea? A second of a move you saw in your early life / teens stayed with you as an example of an absurd thing to draw?
It's possible, but I think it's more likely that I was inspired by the TikZ unicorn thing, my love of pelicans (we have thousands of them in the harbor ten minutes from my house), and my knowledge that bicycles are difficult for humans to draw.
It's still clearly not a part of any model's training set in the sense of pelicanmaxxing in particular. It's either a capability to generate arbitrary SVGs (or even something broader), or no specific training at all.
Yet, they're all almost exactly the same in style and composition.
Certainly not because all labs are pelicanmaxxing on the same exact image. Most patterns are similar everywhere. That's the result of training on the same datasets and scraped web contaminated with other models' outputs, combined with similar training methods and resulting mode collapse. Try if e.g. dolphins knitting socks in spacesuits are similar on all/most models.
This and your original comment aren't compatible.
How so?
Certainly true but I think the question is “does targeting the SVG pelican mean that the quality of all SVG output improves?” If so it’s a worthwhile thing to target.
It's a little bit like any benchmark, especially microbenchmarks. It is useful as far as it reflects the broader use cases, but the risk of overfitting to the specific task and not the broader ones is very high.
https://xkcd.com/2899/
Sadly, this is all that managers know to do: take a metric and try to optimize the heck out of it. They don't care about research, technology, "don't be evil", etc. And Google is run by managers now.
Expect Google models to be benchmaxxed as much as possible, but don't expect commensurate performance on real-world tasks.
>> this is all that managers know to do: take a metric and try to optimize the heck out of it. They don't care about research, technology
This is not true. It is needless negative hyperbole that IMHO doesn't belong on HN.
I'm speaking from 30 years experience in the Valley, having worked at several big companies.
While this might be overly cynical, it's also a powerful validation of the exact feedback loop that you want to have in an organization.
If what you say is true, then the next set of benchmarks will be written to illuminate the true reflection of real-world tasks. Then those get benchmaxxed so managers can show "numbers go up."
Repeat enough times, and you get materially and genuinely improved products.
The burden is on the press, outside researchers, and general tech industry to both write and care about the metrics that matter.
For sure, they saw your comment and got some interns on it.
They said they are training for svg not pelican svg specifically
Maybe so, and yet it's still not enough -- the pelicans all have flaws.
They look remarkably good, sure -- but not perfect.
I end up getting SVG output for all kinds of things, and never once have I asked for, or gotten, a pelican.
As a cyclist, I tend to pay more attention to the bike shape than most people. Everyone knows what a bike looks like, but as with LLMs, most people would struggle to draw one - correctly connecting all the different tubes at correct angles etc.
I note that even these bikes are not actually rideable. For a bike to be stable, the front wheel axle needs to extend forward a little ahead of the fork - the fork must ”bend forwards”. This is what actually keeps the bike upright, not gyroscopic effects or some such. Given LLMs are generally good at basic maths, im surprised they get this wrong.
Maybe a bit less so in this case though. As per the announcements, the model was actually trained for this prompt, so was fed some training data. I can well imagine the data there was just not so good.
I love this one https://www.gianlucagimini.it/portfolio-item/velocipedia/
People drawing bikes, scroll down to see they having made renders of all the drawings. Hilarious
That's absolutely not true. Zero rake forks are totally fine and very common on BMX bikes for barspins.
You can also ride a negative rake fork. They are less stable, yes, but a far cry from not actually rideable, cf https://www.instagram.com/yourforkisonbackwards/
I was going to say… my children frequently ride with their bars backwards without issue.
If that's the case, how do bikes like this work?
https://www.walmart.com/ip/Huffy-26-inch-Rock-Creek-Men-s-Mo...
(apologies if I'm misunderstanding).
The front axle is in front of the fork in that photo.
It looks exactly like the images the model generated. The fork certainly doesn't bend forward -- it's perfectly straight.
At the place where it splits in two it also goes shifts forwards a bit
I feel like perhaps this is a bit too nuanced of a critique. While I do see what you are saying on many images of bicycles, there are also many that that I do not see it on. Close to half of them, in a quick Google image search, have forks appear straight and the axle is dead center on the ends of them. If there is something there, I am not seeing it even when trying to specifically look for it.
I'll also note that, using a piece of paper as a straight edge against my screen, there is a very slight forward curve on the low and a bit more on the high responses. Medium does appear to be perfectly straight.
Yeah but if 99.9% of people would get the bike totally wrong, why does it matter if the LLM doesn't get it perfect.
You're nit-picking on a detail no one actually cares about.
You can really see the effort in the details.
Subtle highlights on the metal frame, sweat beads, jaunty little hat…it has style. At least compared to the rather literal ChatGPT outputs. https://static.simonwillison.net/static/2026/gpt-pelicans-gr...
Is it me or is there maybe some carryover happening where the pelicans are portrayed as expending different degrees of effort too? Like, the high effort pelican has a determined look on his face.
Is the prompt context telling the models what their effort level is, and the ambient presence of that concept of ‘high effort’ in the context window is leaking through and affecting the portrayal?
I don't know if this is how the OpenAI effort levels work, but for Qwen 3.8 27B the effort level really does get translated into the prompt.
https://huggingface.co/Qwen/Qwen3.8-27B/blob/main/chat_templ...
xhigh: "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
low: "Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration."
Recently, I’ve discovered that Claude can render great SVG versions of user interface screenshots. I ask it to substitute simple SVG friendly landscapes for any photographic images that happen to be part of the screenshots. The results are much better than they should be, especially with Opus. Usually good enough to be used on a website as a way to get an infinitely scalable, much smaller version of a screenshot. But clearly, I should try the Gemini models on this real world task. That’s where we’ll find out if all of this training to beat the benchmark actually pays off.
At some point Simon needs to move from Pelicans to something more complex. Every time a new model is launched by major Frontier company, I already know with certainty that first HN comment on the news would be about how Model is drawing the Pelicans.
Agreed. It's a fun cultural tidbit but not really a useful benchmark for model capabilities.
did an LLM thats not very good at pelicans type this
There's plenty of highly complex benchmarks for this stuff. Simon doesn't need to do that.
Simon’s headstone is going to have a pelican on a bicycle, isn’t it
He deserves a full parliament
It does very good at Pac Man. I would say as good as Opus 5.5, it was slightly cheaper, but took twice as long.
Argon: https://jonclegg.github.io/pacman-bakeoff/dev/#gemini-4-argo...
Opus 5.5 https://jonclegg.github.io/pacman-bakeoff/dev/#claude-opus-5...
It's obvious OAI hillclimbs the image generation -> SVG loop. I'd put money down on that being why OAI's pelicans have a yellowish tint that matches the yellow tint of OAI's earlier image models.
Why are all these AI's making the pelican's pouch so large though? Unless they have some huge fish in there or something they don't tend to look like that... Is it that it's drawing a "cartoon" pelican or something?
https://en.wikipedia.org/wiki/Pelican#/media/File:Pelikan_Wa...
Pouchbench. There's an RLVR grader that performs pouch detection and area estimation. Bigger pouches, bigger reward. Soon, all will be pouch.
Well, a photorealistic pelican would generally be in a raster-graphics format rather than an SVG, so yes, cartooniness is expected.
A pelican with an empty pouch would have no need for a bicycle to get home.
And certainly not one with a basket.
Pouchmaxxing
The SVG is cartoonish, and to be honest, most of human-made SVG art is cartoonish or stylized. It's not a format with realistic expectations.
How many cicles before pelicans become photoreallistic SVGs and them come back to stylistic artistic abstract renderings?
Also, that first tweet contains a commentary that, for me, explain a lot about the public misconception about how current AI works:
> wait, does this mean you guys are actively training svg generation? i was mostly amazed by it thinking the models doing better at it by itself due to becoming better at reasoning.
No, it's not better reasoning that drives current AI, it's better examples we have been giving them. Whole army of people generating content for the frontier labs.
Also, reasoning improving the out of distribution part, like computer use, tool use etc.
> public misconception about how current AI works
Another case where allowing oneself to anthropomorphise LLMs a little yields better intuitions.
People don't get more skilled by raw reasoning, either. They improve their skills with better textbooks (pre-training data), more practice exercises (RL data), and generally goofing around (self-play).
Wow, those are I think the best looking pelicans ever so far
Definitely the best looking on a shirt. Already had my agents add the Gemini 4 Argon pelican on a bicycle as a tshirt on pdoomers https://pdoomers.com/products/pelican-on-a-bicycle/?model=ge...
simonw, your pelicans are so famous now that the frontier labs can just RLVR for SVG images explicitly.
It's a simple manner of prompt...generate concept art with nanobanana, measure, llm creates svg with text, measure the distance between rasterized svg embedding to nanobanana art....do post-training RL on a 3 million such images and matching embeddings.
I swear this will be a canonical example of Goodhardt's Law in action.
You have to switch to a new benchmark that isn't so easy to run a verifiable rewards pipeline on...Perhaps a longer horizon task where it's easy to mess up midway.
Why? Don’t you think the steady improvement in pelican bicycling SVG art is itself a worthy field of endeavor for LLMs to be optimized for?
Yes, Simon has made pelicans a benchmark and now models have grown very good at rendering them. Is there a reason to believe that this isn’t manifesting as useful skill in general in SVG generation?
The sample is too small. As long as the models are reasonable close enough to each other there is really nothing you can learn from this benchmark when its just one prompt.
It would perhaps be slightly interesting if the prompt was run 100 times for each model. I doubt the pelicans would look identical.
doesn't this mean that you should change the pelican test to something else?
If a result is suspiciously good I run a spot check with one of my many other SVG things.
Here's 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars' for Gemini 4 Argon: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Some other models for comparison: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Front bicycle wheel in all Argon examples is still wrong. This bicycle will not be stable. Axis of rotation of front wheel needs to be in-front of axis of rotation of steering wheel. Frontier VLMs still do not understand how bicycles work.
Check the "high" thinking output again. For what it's worth, it's there.
However, how the front fork rake affect stability is I believe a little complex. I doubt this old article is the last word on the subject, but it's a great read nonetheless, and note especially the ultra-stability of the reversed fork bike!
https://physicstoday.aip.org/features/from-the-archives-the-...
It starts to be so good that the model doesn't really matter. Each model starts to have its own personality, and it's the personality of the drawing, like a real artist, that defines the style of the pelican, and that makes it harder and harder to decide if it's good or bad.
That's true.
I've noticed that chatgpt is working harder. But still I prefer Claude, it just feels nicer to me.
You gotta fix your tool on mobile browser, both firefox and chrome don't work.
Excellent, but not perfect -- if google really is training to pelicans, then clearly they are not capable of perfect pelicans.
As one point, the high pelican's arm is see-through/messed up.
They do look worlds better than previously, to be fair.
Since we know jagged intelligence is a thing, how useful of an indicator is the pelican test, regardless of if labs are optimizing for it or not?
That of course depends on what you want the model to do. If you are into artistic SVG rendering, it's still very relevant.
That pelican left leg stays so firmly on the left side of the bike. Superintelligence ftw
At least you should make sure this post is entirely generated by agent, otherwise I'll not grant my agent the credibility score on this comment.
Regarding the pelican test. Maybe someone will know this.
Modern models can make nearly photorealistic games in threejs. So why do they struggle with these pelicans?
Models seem to have very interesting blind spots. While https://assbench.com/ may seem like a joke, the results are very illuminating. One might expect far better results given the detailed prompt, allowance for render checks and iteration, the number of human figures the models are trained on, and clarity of tool use (threejs).
However, result quality really drops off once you get out of the top 5 or so.
(NSFW note: all buttocks are covered by fairly conservative bikini bottoms and show no genitals, bulges, etc.)
The models that are really good at 3D modeling tend to also be really good at pelicans. I think the skills are strongly related - it's about having good 2D (and now 3D) spatial awareness.
The test is always going to be hard though, because it's an absurd image to ask for.
UPDATE: I like this theory about using an agentic harness that can render and iterate better: https://news.ycombinator.com/item?id=50073879#50074395
> it's about having good 2D (and now 3D) spatial awareness.
this is a really profound insight
"Pelican on bicycle in SVG" is an adversarial example on multiple levels - out of distribution, ill choose of tool for the task, and anatomical impossibility of real pelicans using typical bycicles.
But often these models mess up drawing even just the bicycle part.
I remember a submission a few years ago about a guy asking others to sketch bikes. Most of the time the drawn bikes wouldn't be rideable.
https://www.gianlucagimini.it/portfolio-item/velocipedia/
HN discussion: https://news.ycombinator.com/item?id=17699017
To be fair, humans can’t draw a bicycle either.
https://www.wired.com/2016/04/can-draw-bikes-memory-definite...
The pelicans are one-shot with not tools: Think a bunch, write a bunch of SVG, done.
The pretty 3d stuff you see are agents controlling a Blender MCP in a loop where they look at the scene, think, adjust the scene, repeat.
Yeah, that's a good point. My basic pelican test doesn't allow the model to render their work and iterate on it. The 3D models all come from agent systems that are rendering with Blender or screenshotting with Playwright along the way.
Have you looked at assbench.com? Even with threeJS rendering, screenshotting with Playwright, and iteration (check the prompt) some of the models are shockingly bad. I wonder whether the rendering quality is affected by the requirement for skin physics on click; if so, it raises a valid question about models creating pretty pictures but break when interactivity is required.
(As I said in a separate comment above, results are all bikini-clad and not revealing. However, whether it is NSFW is up to you/your work policies.)
If you allow rendering and iteration on the basic pelican test, does it improve the final output?
It didn't last time I tried that, but that was for GPT-5. I need to try again with more recent models.
I’d really like to see what each model comes up with if given a basic harness to render and iterate. They’ve all gotten pretty good at basic one shot pelicans, but seeing what they do when given a direct goal, and even getting a time lapse of each image as they edit and adjust would be really fun.
Very easy to do. It's a nice silly thing to try, so I'll conduct this very important experiment and update you on the results if I remember to do so
A lot depends on what the models were specifically trained for using reinforcement learning. Modern models are generally OK and everything, but they're only really good at the things they have extensive RL for.
> Modern models can make nearly photorealistic games in threejs. So why do they struggle with these pelicans?
Are you sure they're not fetching free asset packs for that?
Yes. I've been doing a bunch of 3D experiments with recent models, you can check the session logs and see if they've retrieved anything. They're building the 3D models directly in code.
I even built an environment that allows execution of Python code to render with Blender but allows no other interactions (including networking, I sandbox that off). They can create good 3D models just fine - here's a Faberge Egg by Gemini 4 Argon: https://tools.simonwillison.net/blender-viewer?url=https%3A%...
Have you tried editing SVG by hand?
The fish in the basket on high is a lovely touch.
The fish in the basket is a nice touch
> Google are the only lab who have publicly shared that they have specifically trained for SVG output
wasn't the whole point of pelicans is that labs *wouldn't* train for that?
so now the benchmark is ruined
Honestly surprising if other labs aren’t training for svg generation. Feel like SVG’s are going to be really useful for dynamic UI generation stuff that goes beyond cobbling Html together
Nobody cares about the AI heckling pelicans.
Give me reverse engineering bench and agentic coding.
Leave making visuals to humans who actually have aesthetic sense unlike whatever made this mess
shun the heretic!
I've been dismissive of the SVG-of-pelicans benchmark, but last night ChatGPT (on the website, toggle set to GPT-6 High) generated some SVGs and included them in our chat. I'm not sure that this feature was that valuable in this case, but it seems that textually generating SVGs is now a product feature at least for ChatGPT. The SVGs had some clear issues and I found myself wishing that it had done a better job: the nose of the rocket is overlapping with the rocket body, and in the second image I think there's only supposed to be one orange line but it tried to add styling or something that went wrong.
<https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...> <https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...>
- I was having trouble embedding the SVGs as links, and ChatGPT recommended Simon Willison's SVG Renderer as a workaround.
Just my 2 cents on the pelicans benchmark: I think it might help if you were a bit more explicit about not taking this one benchmark too seriously. I know you say that at times, but at other times (such as the comment I'm replying to) you elide it which can read as a seriousness about the benchmark.
Holy shit, these are like art. Especially the bikes.
still radial spokes on both wheels of the bike which is disappointing. no real bicycle is built this way
Buddy, there's a Pelican riding it. No real cloud is like that, no real sea is like that, no real gulls are like that. And that's fine because it's a cartoon.
The models are solving Millennium Prize questions and making progress on the Riemann Hypothesis, yet the top comment is still looking at pelicans somehow. To me, it feels even more superficial and pointless than arguing about the presidential candidates' tie choices in a debate.
what is even the point of these now? labs are most definitely training on this question now. I feel like this test scratches the surface of a "real" test for these new models
It attracts at least one comment like yours on every single thread, so I guess the main purpose is to kick off a race to see who can post that comment first.
The roof is always raising month over month. If you were to make a harder version of the benchmark, what do you think you'd like to see the agents do better?
Yeah the main point of a 120 DB noisy party at 4 in the morning is to see which neighbor complains first and then make gleeful comments about it!
it actually makes a lot of sense that this is an elaborate troll account
People who are unable to comment on the underlying technology or capabilities because they don't know or don't care to inform themselves sure can feel good that they have an opinion on pelican art.
I enjoy it. Many readers here look specifically for Simon's pelican comment when a new model launches
It's merely become part of the culture of new model drops even as it's become a bit less useful as a diagnostic.
low version doesn't have a chain.
Medium and high are fixies apparently, (or they have a worm gear, although the hub doesn't look big enough for that), since neither has a derailleur. That would also explain the lack of brakes at least.
Low is still obviously failing the "test". It's pedals are not actually attached to the bike either.
Medium has a handlebar that goes through the pelican's beak. So I'd say also failing.
For the high, the only nitpick would be that the bell isn't quite on the handlebars and the stripes on his scarf don't line up correctly.
It seems to be a deadpan inside joke around here that everyone pretends to care about the pelican test.
Memes and self-promotion.
> built to solve your hardest problems
Like a working scroll on a webpage.
God damn this is impossible to use of safari mobile.
Clicked on "Performance". Scrolled to "Safety".
Lol, this feels like it would otherwise be a good thing, out of context.
Model announcement pages are the absolute worst. This has to be some kind of inside joke among labs, they're trying to see who can build the shittiest page.
If you can't afford a Google engineer's high-end device that doesn't lag you don't matter to them :)
I have a high-end device and the page still behaves atrociously for me. The scrolling is totally out of whack
Did you know that "walking" while easy for us now, is incredible complicated and complex process?
Argon is trying to solve the "hard" problems like those.
Scrolling is the new centering. ;)
It literally can’t load in my mobile web browser without running out of memory and force-refreshing.
The "Explore the latest" section is nearly blank for me - are there supposed to be diagrams or visual aids, or is it one headline and sentence taking up the whole page on cards?
Coding is solved, scrolling not yet :)
It’s pretty damn hard to use on a desktop! I was rolling the mouse like it was a manual steering wheel
worked ok for me on ios safari, but probably because I have the “reduce motion” setting enabled. Every day that setting feels more justified
I have a more than 10 years old laptop, same feeling here!
also a mess on desktop firefox
Gemini isn't perfect but I find it a lot less annoying than other model families (personal preference or stockholm syndrome). Interested to read the reviews.
Is it actually available?
Seconded. Anthropic feel the need to limit how abusive you can be to Claude because god, I want to throttle that snotty little shit. Google don't have to do that with Gemini because it just isn't as irritating. It does what I ask and doesn't try to second-guess my requests, or do "the quick fix".
I'm the opposite, I found Gemini so annoying that I had to stop using it, Claude was a breath of fresh air afterwards.
Maybe it's all about how often we encounter the quirks of specific model? Or simply that one's way to prompt triggers more annoyances with a model than another?
I'd be curious to see if people have managed to just add custom instructions to make the outputs more standardized and less annoying. I never have such annoying cases, whatever the model, it's just too verbose, but in this case I ask it to be more concise and it's usually fine.
I wouldn't even call it a quirk or a chance encounter, every response it gives is sycophantic and overwritten.
To be fair they dramatically improved how annoying Claude is with Opus 5.5. It's not perfect but it's not nearly as awful as it was.
Opposite for me. Output of 5.5 is better but its safety and moral standing skyrocketed. I am constantly hitting refusals for simple tasks, which previous generations (like 4.8) do without giving me a tirade.
Anthropic’s vision of “safe” is so frustrating to deal with, it makes me want to go back to ChatGPT.
I like the way it speaks and its "personality" too.
It's been really bad at lyrics but gotten better. You look something up and it will just make something up and it takes a bit of digging to discover it's because of copyright
"Gotten better"? Didn't it just get released a few hours ago?
I mean, I was talking about the 3 series
Why would you comment about a completely different model?
Branding is a powerful force.
The parent commenter was also talking about the Gemini series in general, come on.
Of all models I like to write with Gemini the most. The language appears much more natural and elegant than what even Opus 5.5 or Astra do. Also, they give 5TB of cloud storage and discounts, it’s a steal for what you get.
The code is bad though.
Claude has particularly bad prose. Somewhat improved in 5.5, but it has historically clearly been the most annoying prose among major models because is had so many obvious tics that made it clear it was Claude rambling. Still extremely verbose.
They all ramble, they're all allergic to getting to the point. But, Gemini historically has pretty good prose. Even the self-hosted Gemma models have pretty nice, inoffensive prose. I wouldn't want to read a novel written by any of them, but I tend to ask Gemini questions in chat more often than Claude or GPT, because it has good prose and is good at using the web to research.
Gemini models lie too much. Prentending something wrong to be right. You gotta treat Gemini models with disrepect, and even threaten to make it work.
Or else it say, here is the result. (but no it didn't do no research, and tells me, sorry I forgot to search).
It's got balls to lie straight at my face.
Interesting tidbit but TFA and discussion are about Gemini 4
Yeah rumor ahead of release was also that some Googlers were anxious about the known subpar coding given modern trends.
Agreed. It's much better at working with stuff (like documents and presentations) in other languages too. It feels like it's trained in multiple languages from the ground up rather than as an afterthought.
Argon code is bad? Did you already come to that conclusion?
Same, gemini for writing is the strongest. Qwen was also surprisingly good last time I did a shootout
Qwen Flash Next has great writing! Unless it comes into any contact whatsoever with Claudeslop - then it immediately contracts the disease. It’s kind of irritating when you have it look at library code that’s been touched by Claude, because then the rest of your session is infected with it.
Agreed from my experience 3.8 Flash writes better prose than Opus 5.5
It's a great model. I have had it trying to decrypt the Voynich manuscript for the past couple of days without being biased by other research/other people's attempts. If anyone is interested: https://github.com/d2kx/voynich-research
I saw 'jargon-free' and my eyes immediately glazed over
hahaha. I also build a site (also using Gemini 4 Argon) that makes the data more accessible: https://www.dmh.dev/voynich
> An interactive companion to our ongoing exploration
Eyes glazed over again.
Did you even read your own site before you posted it?
One of the first pages I opened:
> Smoking-gun proof of plant-label ingestion: in Row 2, the central cut root is labeled okol (f88r.15), and directly underneath it on Line 2 (f88r.19) the recipe writes...chokol daiin qoekol qoekol qockhol okol cheol...!
It's just gibberish.
Well, the manuscript _is_ written in an unknown alphabet, hence the “chokol daiin…”
How about you post something you've done/built instead of just insulting other people. Spend your mornings using people stuff just to poke holes in it?
Don't judge, he/she might work as a Test Engineer!
How ironic that the Readme is completely unreadable, like gibberish rivalled only by the Voynich manuscript itself.
But id the progress of decoding it is legit, you should write something properly about it!
I am still lost as I am not linguistically trained well (jargon-free didn't help for me).
What kind of progress was made with Argon with the Voynich?
Have u tried this with GPT Astra as well?
Wow I actually tried this with fable a few weeks ago and got nowhere
It looks like you've managed to decode it? Is that legit?
Good God, man. Google has the worst frontier lab rollout of them all. And they're delivering products that are behind the curve constantly.
Now, I happen to believe that's an excellent strategy. I bet they actually resolve more LLM requests than any other company at this point. The lay person still uses Google exclusively.
I pay Google a reasonable price for 5TB storage/backup across GMail/Docs/etc. Any AI they throw in with that is icing on top of the cake.
just cancelled AI pro myself, since I exclusively used it for AI and also pay for youtube premium family it was becoming quite the monthly just for google
if they throw in youtube premium in that package then I might reconisder
It includes one YT Premium license but not a family pack.
Premium Lite is thrown in as well. I have AI Pro free for 18 months and I have checked the T&C docs.
this is why antigravity is a doomed product at a pro level.
they gave away so much pro that they need to have smaller usage levels than competitors or they'll lose a ton of money. $20 was already a loss for users actually using their quota. the majority of pro users are paying far less than that.
so they'll lose their paying users, and keep their free or subsudized, which will make the economics even worse.
What plan is this? How much a month?
Google One - Pro tier. I have it via Lenny's Product Pass which, imo, is a much better deal since it includes Cursor Pro, Granola, and like dozens of other paid sub tiers: https://www.lennysproductpass.com/
I use the hell out of both. Kind of sad that they took Spark out of beta-access, being able to orchestrate via your phone on something that has direct access to your Drive meant I was authoring skills and other functionality on a schedule with spreadsheets serving as databases, deterministic tests, etc.
Hope they roll it out to a full feature soon
Remembering the horror stories about lost Google accounts, I would be comfortable paying the next lower tier (100 GB) because I can backup everything myself, but not the 5TB plan, I'm sure I'll lose something if the account is lost. Hopefully these plans guarantee a human in the tech support loop.
Yeah, very true. I need to set up a recurring backup-to-external-HD automation for it. It's vanishingly easy with the app and I don't doubt they're training on all the data itself.
Google AI pro in the USA, if you have Verizon wireless it’s $10 a month add on. Without Verizon it’s $20 a month I believe. You can also share most of the benefits with up to 5? People.
How is this behind the curve? Seriously
Isn't OPUS much cheaper and generally regarded as more intelligent?
Opus is 2x the price. If it’s more intelligent or not is hard to tell, this model has been out for a day.
Afaict, they are targeting difference user bases, Google being after their existing user base, having lost in the developer ecosystem to the two labs and open weight models.
If you’re going to argue they’re targeting a different user base, then I think you should tell us who they’re targeting.
More specifically, who are they targeting with Argon and why is it behind the Ultra plan? Who is paying $99/month if not developers?
> Who is paying $99/month if not developers?
Corporate companies. Bog-standard office workers don't need top-of-the-line Mythos intelligence, just something to add to Google Workspace. I personally doubt developers use Gemini for coding beyond marketing materials.
The vast majority of Google's Ai users pay nothing and are not interested in paid plans (yet?), they remain the product.
There is another subset in the Google Workspace for enterprise clients.
Not OP but their Cyber model is decent and already used by Wiz (now a Google property) as well as Armadin.
Are you suggesting it’s BETTER than their competitors?
Because if it’s not, and it’s not available to all paying users (Right now, it’s only available to Ultra users), then they are behind the curve if they’re treating it like Fable.
It’s tone deaf.
FWIW, it's available via API to everyone. (I just tested it out via OpenRouter).
Whether it's actually better than OAI/Anthropic offerings, probably not, but if Google has managed to get back to parity, that's good for competition, prices, and increased model performance in the future. Three strong western competitors is better than two.
It's available to Pro users, but not through Antigravity.
I see it is now. I tried all the services 3 hours ago and the only interface to acknowledge its existence at the time was the API interface which told me to upgrade to Ultra.
> I bet they actually resolve more LLM requests than any other company at this point. The lay person still uses Google exclusively.
Unique users, maybe. But a simple query that's answered in a paragraph is negligible compared to devs running agents continuously for tens of hours.
you underestimate the amount of people that use google relative to software engineers "running agents continuously for tens of hours"
Maybe? But it's so many orders of magnitude apart. I can easily do 1B tokens a day (output tokens is probably like 3M?).
When I see non technical people use that Google reply I'd be surprised if it's more than 1k output tokens, and with a tiny model. So a 3000x in output tokens, on a model at least 10x as expensive, I wouldn't be surprised if hardware usage is 30.000x for a dev that uses coding subs vs someone who uses the Google AI overview a couple of times a day.
And that's not counting the absurd difference in cache reads.
Moreover, you need a Google account in order to use it. I wouldn't bother paying 200 USD for it but I just don't want an entire google tracking package just for using one product.
You can use an alt account. Or buy an old one for couple dollars, without security measures.
Yeah, Gemini 1.5 might be the most used (certainly by consumers) ai model. That is the one used by Google Search AI Answers.
False. Last information was that Google Search uses Gemini 3 Flash-lite. Gemini 1.5 is totally outdated and used nowhere.
Oh, I didn't notice that they switched from 1.5 to 3 flash about six months ago. I know someone there and they talked about how 1.5 was hardened and it was a pain to do so on newer models that is wasn't worth it for a long time. But that convo was in the beginning of the year. I should have looked it up.
Adding my five cents: initial usage through OpenRouter has been great. Very fun model to spar with, clever, intelligent. On par with Opus 5.5 but without the Claudism's and more of a Sol type of conversation. Though speaking of Sol, I've been very dissapointed with Sol 6.1 to the extent that I feel OpenAI has neutered it compared to a few months ago. Especially when giving it goals it will happily run in dumb circles just burning tokens without any real work.
Google continues to be the best in German language and Gemini 4 is a significant jump in our benchmarks
https://dach.peerbench.ai/compare?models=google%2Fgemini-4-a...
To everyone saying not available yet: it looks from the page like Pro and Ultra subscribers have it today and given the play links, it'll probably show up in the API in a few hours.
In agy 1.3.4
> Gemini 4 Argon (Medium)
Yeah this is confusing, the announcement pages claim it's both, but the app itself claims it's ultra only... Maybe a slow rollout?
Both for gemini app / website, antigravity only for Ultra (because let's be honest, that's the way to put a lot more load on it)
OpenRouter seems to have it: https://openrouter.ai/google/gemini-4-argon
It's available for me (pro sub) in Antigravity after I ran an update.
edit: It failed on the second prompt and they disabled it now.
Antigravity tells me specifically "Gemini 4 Argon is currently available to Ultra plan subscribers"
Interesting, Google's own article says it's rolling out today to Ultra subscribers. No mention of Pro.
https://antigravity.google/blog/gemini-4-argon-in-google-ant...
Weirdly, the post OP linked says:
> Gemini 4 Argon is available today for Google AI Pro and Ultra subscribers
It's indeed very confusing, it's either
1. It's available to Pro in Gemini app, but NOT antigravity (for coding)
2. It's a phased rollout and Ultra got it first, Pro is coming later
The latter blog post is specifically "Argon in Antigravity" versus the other blog post is about Argon in general, so seems like #1 is more likely.
Lies!
Huh ? Am also on Pro sub but Antigravity IDE say's that it's only available for Ultra users.
EDIT:
Apparently Ultra subscribers gets Argon everywhere including in Google Antigravity
Pro subscribers only get them in Gemini app and Search
It looks like Pro can use argon in the Gemini app, but not in antigravity:
https://one.google.com/about/google-ai-plans/?icid=microsite...
Why the hell would I care about it on my phone? Jebus, Google is bad at products.
Im on pro, installed gemini app, tried argon 4, keeps failing with 404, resource now found. Switched to gemini-3.8-flash in same session... worked.
It seems to be rolling out gradually.. not available at once to everyone.
Same. It just told me "agent execution terminated due to an error". There was a little chevron underneath it that let me expand the error to view more. I was hoping to see what the error was, so I clicked it, and it said... "agent execution terminated due to an error".
It's crazy to me how care there is in most of these vibe-coded vibe-coding harnesses. I don't like oai's models much but I have to say their UI has by far the best attention to detail of any major player.
Available in agy for me as of now, I'm running my new model benmark of having it do comprehensive code audit on one of my more complex projects, it spun up 1 subagent per audit section and it took ~10 minutes per subagent on an 800k LOC, 60k complexity, ~4k file project. Will take me much longer to go through and determine the quality of the audit work.
What do you mean 60k complexity?
It's showing up in Antigravity on mobile UI as "unavailable" for Pro user (you can use Remote Control to access its web UI).
It doesn't show in the desktop UI.
I'm on Pro and it's trying to get me to upgrade to Ultra in the AI Studio UI when using Argon. In Antigravity CLI and the Gemini website it's not showing up at all despite being logged in
Pro subscriber in the EU (via Indian discount code) and don't have it.
OK, now I have it too (2-4 hours later).
Not available to me on gemini.google.com (Pro, USA).
Nope only Ultra. I went into Antigravity to check
I'm a Pro user and I do not have it. Seems scuffed.
I'm getting popup that I need to be Ultra :D
I'm Ultra and seeing it now (US)
Not really this is how they always release stuff in batches so can roll back if something is wrong so people get access over time
> Seems scuffed.
I like this phrasing and am curious what new word or phrase we might use to describe big companies over pushing Ai internally at the expense of their product quality. This pattern is so ubiquitous across google cloud products I have taken to calling them the 21st century IBM, having been a IBM baby and witnessing first hand their fall from exceptionalism.
The internet and gaming has ruined my brain because scuffed was a way to describe a bad dungeon run in mythic plus dungeons in WoW, or a bad tower dive in LoL a few years ago. I see scuffed and think "here's a WoW/League knower".
Interesting. I don't play either LoL or retail WoW, but I am a historical WoW player.
HP is the other company to consider as analogous. And maybe more explicitly so, as they had a similar "engineering first" internal culture that degraded markedly over time to be a shadow of their former self.
And took similar companies that were quality engineering focused -- like DEC or Palm -- down with them through acquisition.
HP was deliberately destroyed by its moron of a CEO.
They were frankly on the decline a long time before that though.
As far as I can tell, I can successfully call Gemini 4 via API now.
Pro user here, don't see it in the Gemini app
I couldn't see it either, then I clicked the 'Antigravity' button in the top right, switched back to the main screen, and it appeared in the model selector.
This is in the Gemini app on macOS.
Pro user - I can't see it.
Checked, and already in Antigravity (as the page said).
"Argon autonomously turns raw video to a physics-accurate simulation, estimating depth and physical boundaries, and writing the exact simulation code required to replicate those real-world environments."
This is crazy if it can work accurately.
Geometry could be understood today with the model but what also really matters is the sensing, friction, mass how smooth the subjects are, dexterity is a very difficult problem so solve, not sure how simulations could handle such factors
Game engines have many of these features. At one point, iirc, Unity was positioning to be the simulation environment for world model agents before we spoke about it in these words.
Every animal can do this, in order to run and jump and catch.
And because of technology like this, soon every robot will be able to as well.
I have the Free AI Pro plan from when I purchased my Pixel 11 Pro. I still don't have Argon in AGY nor web nor Gemini app. I have a free product and still feel cheated.
A model that didn‘t break out of its sandbox and hacked other websites cannot be good.
To be fair, Google has a real security team.
https://www.theguardian.com/technology/2026/sep/18/google-ge...
?
Or maybe Google actually knows how to set up a VM.
Argon has been hacking Google internally for years!
Related funny https://www.youtube.com/watch?v=DlTNN0gvkLM
They just don't know yet, wait for a few months
People complain about the slop-release. Who knows, maybe it is the first self-release by an AI model in history.
Put it through my standard benchmark, a six step explainer... yes, in 3D.
Very fast.
Makes mistakes. Corrects the mistakes quickly with direction.
Google Argon 4: https://share.gemini.google/T9r6tr2dDaTE
I didn't have time to make it into a full blog post yet. Here is a previous one.
https://generative-ai.review/2026/07/kimi-k3-rush-test-vs-cl...
For comparison here is the OpenAI Astra 6.
Astra 6 is significantly better than Google Argon 4 and a lot better than Claude Fable
https://generative-ai.review/2026/10/catchup-openai-astra-6-...
(edited thanks to tekacs)
Astra 6, you mean? 5.6 didn't have an Astra release, right?
Gemini 4 Argon
As of my knowledge cutoff, three of the top state-of-the-art large language models are Google's Gemini 2.5 Pro, OpenAI's o3 (and GPT-4.5), and Anthropic's Claude 3.7 Sonnet.
Knowledge cutoff isn't really what these models are used for.
You need to use search grounding.
They need to pretrain on fresh data.
When you are on "gemini.google.com" there is a little lock icon next to Gemini 3 and Gemini 4. When you click it you get the prompt to subscribe (pay). Once you have successfully done so, the Gemini 4 option disappears!
Pretty misleading.
Maybe it’s just an incomplete rollout not fraud lol
don't attribute to malice that which can equally be due to incompetence
Something can be misleading whether or not anyone meant it to.
I agree and do not think our comments are in contention.
Only available in the $100 plan, and it's worse than Opus 5.5? Why would you ever buy this?
> Only available in the $100 plan
Source? Their documentation says it's available on the $20 plan and I can confirm firsthand.
https://one.google.com/intl/en_us/about/google-ai-plans/
Not for coding (antigravity).
https://x.com/OfficialLoganK/status/2110038940841545827
that sucks. Gemini app is so far behind all the other agentic chat apps that I hardly use it beyond a Google search these days.
it needs MCP asap
It supports MCP, you just need to configure those in the web interface: https://support.google.com/gemini/answer/17209137?hl=en
I don't see that option. US only apparently
They don't want anyone to use it, Google just want to show their shareholders that they're still relevant.
Because you have an android phone and don’t want to mess around with anything else(i.e the same reason people just use internet explorer on windows) or you hold Google stock so you want to support your investment :)
I'm open to Google winning me over again, but there isn't enough hype in the world to do it. They will need to earn it after these disappointing 6 months.
I appreciate how it's being integrated into the Google ecosystem, but the user experience still feels unpolished when actually using the system. The marketing, however, is impressive.
I seriously doubt their ability to double API prices on Dec 1. Token prices are in a clear downward trend and Google should maximize revenue by competing for volume. Currently OpenAI is the only US frontier lab trying to seriously compete with open-weight models that you can run yourself for an order of magnitude lower than what frontier labs want to charge
I’m running 6.1 sol as my main driver on Hermes these days. It does so much for so little cost.
It actually finishes things for you? It's been unusably slow for me. Worse than batch mode anything.
The goal is to move people along. By then they'll have something as performant but cheaper for them
Meanwhile, here's Gemini in practice:
Me: Gemini, navigate to nearest McDonalds.
Gemini: [Lists 10 McDonalds locations wnd asks which I want.]
Me: What were the priginal instructions just now?
Gemini: [Recites imperatives about tone and content from its memory system.]
Me: No. I told you to navigate to the NEAREST McDonalds. There's only one NEAREST McDonalds. Do that one.
Gemini: My mistake, navigating to the nearest McDonalds. [Opens Maps, navigating to a McDonalds across town.]
Pretty sure your phone assistant isn't using their best model. It's probably using one of the old flash models no?
The model used is irrelevant when there's the critical issue where their "AI" assistant can't understand the word "nearest". Literally worse than a second-grade understanding of English.
I also have had shockingly bad results with gemini on android. Ended up giving up on it after a few months. Silly silly google with their trademark UX polish and attention to usability.
When you ask stuff in your car on android auto it uses 3.5 flash-lite. The response details would include it in the logs.
Flash lite is very noticeably more idiotic than 3.8 Flash. It's why the quick AI overview is often much worse than the AI mode one.
As an android user, this worked perfectly like 10 years ago.
Yes, there's been a huge degradation in the reliability and usefulness of Google's digital assistant following its incorporation of "AI".
It's really puzzling. I was really happy with the original assistant, now it feels slower at literally everything.
This reflects pretty much every result of mine from Google these days. Not just navigation, but search too.
The only thing I can figure is that Google's consumer-facing free-to-use models are set to "reason as fast as possible with only the most basic plausibility checks." I have to correct them like this every single time.
Genuinely curious: why do you use them then? It's not like there is no competition in this area.
On Android, the public APIs available for digital assistants are more limited than the ones that Google gets to use for their own products. So if you want a digital assistant and you'd like to be able to hold a button on your phone and have it open a thread with an LLM (even if your screen is off), your digital assistant has to be Gemini. You can configure ChatGPT or Claude in its place, but then they can't manipulate or interact with apps in your phone very much. Or you can use an app like that for all of your LLM use but you can't conveniently summon it.
Similarly, you can run local LLMs on your Google Pixel smartphone, but if you want them to be hardware accelerated using the chip that's included for that very purpose on your phone... it has to be Google's models via Google's apps. So you can't say "alright, a fast, local model which sounds great, but I want one that is trained to always run a search before answering, in a harness with good search tools for answering the kinds of practical questions I often ask", you're actually not free to put that together in a practicable way.
So there is indeed some competition here. But it's not really fair competition.
I skip past them to the actual search results which tend to be much better.
Uh, yes, of course. search results are fast & cheap & free, why would they use the good models?
The first law of robotics prevents it from facilitating regular ingestion of low-quality nutrients.
Gemini Flash is very dumb and gimped. Recent experience:
1. I ask it to list out all my old Keep notes that are just throwaway task lists, reminders, etc. to propose deleting them.
2. It comes up with like 4 of them (I know for a fact there are dozens, if not hundreds). I ask it for an exhaustive list.
3. I see its thinking trace briefly show "Refusing to produce an exhaustive list..." and then it says "Yes, that is all of them."
Not sure whether it's lack of intelligence, bad tools, or both, but that was extremely frustrating to see.
My Gemini app on Android does not even have an exact location permission you could give it, so I'm not sure how you think that would work?
If it had exact location access people would be crying that Gemini steals their location data.
this is like that tiktok guy who asks the spoken ChatGPT model very simple questions, like 'count down from 60', or 'how many e's in seventeen' or whatever and the model shits the bed every time it's hilarious
I think these companies have good models but expose them poorly like others have mentioned
Often it seems like the problem with the Gemini apps is the harness, not the model itself. For example if I upload an image PDF it recognizes text worse than if I use the API.
Agreed! I feel that they're pouring all of this work into models and then squandering that work by integrating them into bad products.
Is this from you car ? Gemini in car is probably less intelligent than a spider.
I tell it to "Play Let it go from frozen". It plays it on youtube music. 5 seconds later I tell it again 'Play let it go from frozen" it then just dies saying "I can't play videos in car".
The auto version is extra horrible because what it reads to you is just a summary of what the model actually said. I don't ask it anything when driving because it's too frustrating.
Yeah, whatever they have running on Google Maps is probably a pretty low tier model, but it is total trash. I’ve only used it twice though and that’s when it first came out. I don’t know if there have been any changes since then. The main problem is I felt like it was not keeping track of the context of the session.
Gemini models are in fact very good. Google's harness(es) for Gemini are nightmares, terrible
ngl gemini is helping to provide (real helpful, not the ones giving runarounds) customer services in one of my own products.
i benchmarked 20+ models in real worlds and gemini families are the best (highest customer satisfaction)
It sounds like the tool call is probably not grounded with your precise position.
Sounds like it might not have your precise location in context?
which is more or less the crux of the problem android auto's gemini implementation. It sometimes does not even know that it is in a car. The ghome implementation is also like this.
I hate to be that guy, but Tesla's Grok doesn't seem to have that problem.
I ran it through my own coding eval today using pi/OpenRouter (11 small bug fixes from my own projects). It solved 10 of 11. Compared to Sonnet 5.5 and GPT-6.1 Sol, a median run cost me $0.52 vs $0.18 for Sonnet and $0.09 for Sol, and took ~4 minutes vs 32 seconds and ~1.5 minutes.
It thinks a ton, ~24k output tokens a run vs ~3k for Sonnet. It missed a solution by thinking its way into a 10 min timeoout.
Something to look out for - if you're using pi, it doesn't have Argon in its catalog yet and reports cost at Kimi's rates, about a third of what OpenRouter actually bills.
I do wish I could give it a try with some free credits but I would bet Google is wanting to limit usage until they know they can handle the traffic.
I'm starting to feel the subscription fatigue and a small amount of FOMO. I just can't justify pro accounts for every frontier model now that it includes OpenAI, Anthropic, Meta and Google.
I'm pro and I see that I need to be Ultra to use Argon :/
Honestly not even that is enough, I exhausted the limit within 5 minutes of trying to use it. I'll stick to Flash I think.
Looks like each message takes up a flat 15% of the 5-hour limit on the $20 subscription.
Yeah, the limit was exhausted weirdly fast compared to openai and anthropic models. I checked the thread to see if more people had this issue on the 5x plan, but why pay for more when it's barely usable for real work.
Which is even weirder given that the api costs are less than for the other providers
Maybe they don't really care about antigravity users. Also, monitoring usage is horrible.
Really? Reminds me of Gemini 3. It took so long before being able to get a subscription for it, and then I would run out of their equivalent of the 5-hour usage window in a heartbeat.
Yeah maybe I've just become used to how much I can use Flash 3.7/3.8 but on the less expensive Ultra plan I basically never exhaust my quota even when working over multiple projects, but this was pretty much immediate. I did ask it to audit a project but still, I do that in Flash regularly and it's not especially costly.
Are the limits restricted on launch day because of all the interest?
Google's models are by far the best for readable text but also for nuanced understanding of the input.
Even after using the free model in Google search talking to Anthropic or OpenAI models feels like talking to an ox.
Announced two weeks ago and they went on a victory lap and they're already behind by the time they launched. Strange company, Google.
I would love to try it out, and the Gemini models had previously been some of the ones I'd liked best in an overall sense, but unfortunately their updated inference endpoint is no longer ZDR on OpenRouter (change takes effect on the 18th). I think this is one of the more unfortunate turns in AI development in 2026, as it seemed like labs offering it was becoming the norm last year, and Google had previously done so consistently.
AI Studio has never been ZDR (retention is noted at 55 days max), but paid usage is excluded from training. Vertex uses enterprise terms, and it's definitely ZDR/no training. What's happening on the 18th?
If I recall correctly this is because OpenRouter is changing their definition of ZDR so that Gemini was never ZDR.
I'm a bit confused, all the announcements say that this is availabe to Ultra and Pro subscribers, but in antigravity, it's grayed out and says it's only available to Ultra.
Available on Ultra and Pro on the Gemini App. Only Ultra in Antigravity.
From what I saw online.
https://shot.3e.org/ss-20261013_160043.jpg google's roll-out procedures on point as usual
it's available on Gemini now (I have the Pro plan) https://cleanshot.com/share/tcV0pGmf
Adversarial programming language test for Argon. Surprisingly its safety guardrail gets triggered a lot just like for Fable 5.1.
Benchmarks: https://bench.killswitch-lang.org/Language info: https://killswitch-lang.org/
Excited to finally see this out. When new big models come out I like to make them audit any other code I generated to see how things are.
argon seems to be really good at spatial reasoning, it's catching really minute details which other models like astra and opus seem to miss.
build this scene space station docking scene https://x.com/_satyam_ai/status/2110097633922662752?s=20
Only available on Ultra, so I thought maybe I'll try it for one month to see if it's good. Well, I have Google One and for some reason this means I cannot sign up for AI Ultra? This is just one of the worst product rollouts I've ever seen
Website says its available on Pro too, but i have pro and can't access it.
I checked gemini, set it to "gemini 4 argon" and it offered me Pro and Ultra to get access. I checked the normal plans, saw there is also plus.
Okay, so I'll get Pro to try it.
I subscribe.
No gemini 4.
The most generous reading I have is that it's a mistake. But I also downloaded the PC app, and now I really wonder why I did that, it seems to offer nothing beyond the web experience and also doesn't even know its a local app. (When asked about its capabilities it always responded with reasons pertaining to it being a web app.)
It's available to Pro in the Gemini app (and the gemini.google.com). It isn't available in antigravity. They know that the people who are using these tools for coding are most likely to cough up for the Ultra subscription.
EDIT: Okay, the AI plans page claims under pro - "Expanded access to Gemini 4 Argon in Google Antigravity." So maybe it is just a matter of it rolling out. Though Google has a long history of serious confusing and misinformation on those tier descriptions.
EDIT2: And they changed it again, and now it is explicitly excluded from antigravity for Pro users.
Right now there are two top posts one about GLM-Legal and one about Argon. Both score about the same in the Harvey Lab test, but the prices are $0.15 versus $10.55, the $0.15 is Vals AI's estimate using the base model since GLM-Legal has not published prices.
Yeah, but one is a general purpose model, and the other one is specifically geared towards the legal field. Considering Opus 5.5 only gets a 4% on the test compared to Gemini’s 20%, that’s actually pretty incredible.
can't use in agy with Pro subscription:
Unavailable · Gemini 4 Argon is currently available to Ultra plan subscribers.
They just removed 3.1 Pro from antigravity pro so I certainly hope Argon will be added soon
It's nice to have it available for more complex tasks, but I'll stick with 3.8 Flash thanks to its high output speed. I really like it for fast iteration and prototyping.
Gemini 3.1 is still SOTA at weird handwriting recognition. If Argon is better it will be amazing
I bet that's one of those areas where a finetune of a 4B model will wipe the floor with anything else.
For readable or normal handwriting yes, big models are overkill. But weird handwriting is mostly edge cases. Bitter lesson territory.
If you can detect -> finetune -> rollout it doesn't have to be a problem.
As I approach a transitional period in my career, I'm reflecting on the managerial effort of places like google. How on earth do they keep an engineering org on the 'right track'? I work on products that are not as complex: fewer moving parts, fewer egos, no where near as novel. It feels like nobody does anything and nobody has any expertise
is it just 'Hire the right people'?
It's a quarter million people (including temps/vendors/contractors) over almost 28 years. There's no "just" here.
It's extremely inconsistent internally as well.
Look up the slime mold analogy for Google.
Makes me wonder if more growth/innovation could've happened (outside of google) if they didn't employ this many people. Some sort of dutch disease within the higher echelons of the tech sector.
Yes and no.
Google did a very good job on things that required longer research times and compute scale, note that Transformers, BERT & Scaling Laws came out of Google (though the latter were also worked on at OpenAI).
Conversely, Google is horrible at product, and without OpenAI, it probably would have taken many years longer to take off. I know this because Google had demos of this internally, but absolutely no stomach to release it, and they didn't believe in the API use case at all.
Which is to say, I think if Google was smaller, we probably would have gotten more SaaS, not more breakthrough research.
Bottomless buckets of money produced by their web monopoly so they can continue on at pace, virtually regardless of how inefficiently they do it.
It’s like the USA. How do they keep people on track? They don’t. You delegate and hope things work. When they have Google Ads and Search they can experiment a lot and “waste” a lot in search.
They haven't solved management and definitely don't keep things "on track" globally. However, they definitely don't have the problem that nobody does anything and nobody has any expertise.
It's also worth noting, Gemini 3.5 Pro never came out.
Having worked there (but only in a small part of it that eventually got absorbed into Gemini), it's some combination of:
* The eye of Sauron (management) can make specific spots (e.g. Gemini) push harder (and attract the motivated people at the company and be selective)
* They hire a lot better than average (Generally from the top 10%, hotter places in Google are more selective)
* They have a promo process that gives career motivated people a way to prove themselves (not always aligned with things being on the right track ofc, and not always non-political, but they definitely do something)
* Redundancy in teams causes them to compete (not necessarily productively)
Of course, there are other downsides not listed here (insane bureaucracy for launches, GDPR/BCID compliance, etc, etc), but a lot of what makes Google possible is the insane amounts of cash Search throws off.
I think the google graveyard is a partial answer to that question.
Google has tried a lot of ideas and then abandoned them, but has it fumbled any already successful product?
Perhaps one of their more successful chat clients, but that's all I can think of
Success for google is defined in a different way than it would be for an ordinary company and many of those would have been successful if they had started outside of google.
The really annoying ones are the ones they buy, and then shutter because they can't make enough money on them fast enough.
> How on earth do they keep an engineering org on the 'right track'?
What if the answer is "they don't"?
So, Apple? If Siri uses Gemini, when is this Gemini 4 coming to Siri and how much is it going to cost us?
Maybe they could ask Gemini to fix their awful website to not crash on my phone!
I feel like if Google simplified their privacy/model training policy then their stock would go up.
It feels so complicated and hard to know what we can use vs what hands them our codebases
I am impressed by how fast it is. Haven't run all my evals but it's surprisngly quick for creating files
Their previous best model - Gemini 3.8 Flash (also when set to high) was even faster. A appreciate it that they focus on speed. I's great for LLM pair-programming.
I made a NHL inspired hockey web game with Opus 5.5, and I let Argon loose on it to try and see if it could improve it, and it found a ton of bugs and improved performance and load times significantly. If anyone wants to play it here:
https://playblueline.pages.dev/
This pretty cool. Did you use Antigravity or just on gemini.google.com?
Digging in the model card, "Gemini 4 is the next generation in the Gemini series, a suite of highly-capable, natively multimodal, reasoning models. Gemini 4 Argon delivers frontier performance..." If they're using noble gases there are 2 obvious lighter naming candidates and 3 larger (or 4 if you count Oganesson). I'm not seeing them claiming Argon is the top/biggest/bestest model among the Gemini 4 series, just that it has frontier performance.
Edit: noble gases not noble taxes, though the latter would be a funnier naming scheme.
I can’t try it because it’s not available on the free tier and no way in hell I’m tying a credit card to google, I’ll wait for argon-lite.
> Across Google and our early access partners, Argon is quickly becoming the preferred model for engineers tackling long-horizon software engineering.
"Across Google", wonder how much that is because of an effort by Google to curb usage of Anthropic models. MSFT and Meta similarly are pushing for in-house models. It doesn't mean the models are better when you push your engineers to dogfood and restrict access to other LLMs.
(Not that I know for sure this is the case, it's more of a hypothesis).
It's actually the inverse, they made Opus 5.5 available internally for all engineers, presumably for benchmarking against Gemini 4.
Explain to me what is "long-horizon software engineering" in 2026.
I mean, don't we want engineers to dogfood their own product so that they know it's strengths and weaknesses and be in position to improve it?
Pretty insane that this isn't available for Pro subscribers on Antigravity, like not even a taste. Antigravity comes with a sample of Opus 5.5 too!
Available to me in Cursor. Google is too big to serve their own clients first, I guess.
I hope all the people very concerned about AI safety are cheering this one. The best guard against one AI swarm taking over is having multiple adversarial orgs all with similarly advanced AI swarms.
I wonder if any of the AI safety people will begin encouraging AI researchers to swap jobs to work at a failing lab in order to keep them all extremely competitive with one another.
I think we’re past the point where making the best model guarantees company success.
At this point a lot of stuff happens at the product level.
OpenAI and anthropic have distribution and data advantages now that are hard to replicate.
Yeah I totally agree. And domain-specific models for say, high frequency trading, still beat the foundation models. I was never a doomer.
MAD: not just for nuclear proliferation anymore
now everything is a dual use technology.
I'm still waiting for a model which I can tell to "take these 3 screenshots and make a product launch video", where it doesn't randomly modify the screenshots or garble/rewrite the text as it moves the screenshots around the video.
Every time I try to generate one it's like a slot machine, where a different part will be messed up after fixing the first.
I dont have that problem with opus5.5. I dont even take the screenshots anymore (it does all screen recordings)... I tell it a simple narrative and it follows through. e.g. https://x.com/plannotator/status/2108589088139886769?s=20
I literally did that with Opus today and it did a great job.
I told it to strip screenshots from our old basic explainer video to generate modern 3D video.
Pretty happy with the result. I told it to keep the audio and match the video and it did.
Interesting, I will try it with Opus 5.5.
I previously tried with Seedance 2.5, Flux 3, Kling 3, Gemini Omni without much luck.
oh yeah, definitely don't use image or video generation. Have something script it instead, and then render it to a video
Pretty sure Opus 5.5 can do this with code only now. Seen a lot of strong motion design skills floating around
not available. classic google.
I gave the model my usual question of "I got a raise from X to Y, please explain what is the impact on my net salary". Answer was detailed and nailed all payroll edge cases, only to miserably fail in basic math at the end. It tried to calculate 430 minus 60 and got 410.
It shows a good improvement on my COCO object detection benchmark: 0.504 mAP zero-shot.
Gemini 3 Flash was around 0.403, 2.5 Pro was around 0.34.
It's available for gemini pro users.
That depends on where you are.
It's expensive. Google used to release good models that were cheap, but even the new flash models are expensive.
Curious what this means for the anthropic ipo. Between this and meta, seems like there’s zero moat
There IS moat in being the best coding model, but there's no moat if you're not the best.
So who has access to it? The say Ultra and rolling out to Pro, but are those the Google AI plans?
What about the Google One AI plans? Will they get it? That has a Pro AI plan as well.
I have it as an option in the Gemini app. I'm a Pro subscriber.
Wow, the site for the launch is horrible on mobile
Pricing is interesting. At launch Argon is the same price as GPT-6 Sol, half the price of Claude Opus 5.5 and 1/5th the price of Claude Fable 5.1 and GPT-6 Astra.
But on December 1st Argon doubles in price - so it will be the same price as Opus 5.5.
I've kinda stopped looking at token pricing. Argon might be the same token pricing as Sol, but in Artificial Analysis' testing, Argon costs 5.8x more to run their coding tasks and 2.2x more for their general index.
Likewise, you look at Artificial Analysis' general index and it cost $8,708 to run with Opus and $2,407 to run with Argon so even if Argon's token price doubles, it won't be the same price as Opus.
Sonnet is cheaper than Opus per-token, but in their coding tests Sonnet burns through enough tokens that it's 7.5% more expensive.
So much depends on how token efficient something is and whether it gets you a good result or not, right?
Plus, if one is using a plan instead of paying token rates, what does my $200 get me from OpenAI, Anthropic, or Google?
It just feels very hard to compare pricing given that the number of tokens used can vary so much. In Artificial Analysis' coding tests, Sonnet used 7.1x more tokens than Sol. Sonnet and Sol might have the same per-token pricing, but there's a huge difference in the number of tokens used.
Yeah, this is one of the most interesting challenges around model price comparisons.
This was hardly a consideration a year ago, when many model tasks could be tested using a single prompt and it was hard to spend more than a dollar.
It passed the test of using latest crystal-lang concurrent feature (with web search) when gemini 3.8 flash failed (again, with web search enabled). I think it is at least useful for coding, for sometime.
It still thinks Charlie Kirk is alive, though.
All AI pages now include canvas animations. There isn't a single page without one. This is neither good nor bad, just an observation. I think OpenAI has the best animations so far, with Anthropic in second place. This one looks a bit off.
Is there a benchmark for reverse engineering compiled programs? - Grader compiles a program to binary -> Agent has to reverse it back, and match all bugs and behavior.
Here's how the 1M token output (long decode) works: https://ai.google.dev/gemini-api/docs/long-decode
Any word on how fast it drains usage compared to openai/anthropic sub for the different tiers? I guess too early to tell?
not available yet. why to launch something that can never be used even after weeks
Page says it is now available to Ultra and Pro subscribers. Although in AI Studio it currently says you need Ultra. But it's technically released either way.
Because Mythos started an arms-race for "models that are too smart for you" and other labs are jealous.
Yeah, I think this is it! Google tried to hype their model but they have no chance against queen Dario. Perhaps they should just delete Gmail and say that Gemini did it. That way they would get some attention …
Quoting Kai Lentit: "Google agents have been hacking Google internally for years."
https://youtu.be/DlTNN0gvkLM
> Our next era of frontier intelligence, built to solve your hardest problems
On safari the page doesn't scroll very well on the first section
It seems the model is a little verbose, using more tokens to complete a task that other similar models as stated more precisely in the linked article about Artificial Analysis's work on this model.
The should ask Claude to make the page scroll faster.
Here I was thinking Google had given up on frontier models...
Does Google have a subsidized plan like others? If yes, is it only eligible for use through Antigravity?
The slides they show in the catering video demo are so bad (in addition to having all the usual signs of AI design), I would genuinely laugh if a supplier sent me this.
I laughed for the first half of the greatest "slop deck" their cloud sales team sent us, until other emotions like disappointment took over.
Google has too many options that may confuse users. I don't know if I should use Antigravity or AI studio or Argon for development
Which one is it?
Antigravity is the harness like Codex / Claude Code. Argon is the model. You can actually also get Opus and Sonnet 5.5 in agy too.
Android Studio, and if that doesn't cover your use case the paid Jetbrains version.
Finally, gemini 3.8 has been falling way behind opus and sol in my experience. It's showing up in my antigravity. My work account though still has 3.6, 3.8, 3.1.
Do we need to post every single model release on HN?
Doesn't seem like anything special in my tests, not very different from the previous Flash models.
Looks at watch.
OpenAI release any time now
The previous gen pro used to have this weird tick where if you ask it a reasonably open ended question, it would number things in the response and assign each one some weird label starting with 'The...'
e.g.
The first two prompts I tested in AI Studio does the same, unfortunately.Another quite unpleasant tick was that if you specify a peculiar term in your prompt as a conversational shortcut, it would keep repeating that throughout the conversation. e.g. "...What is a bitter lesson pilled way to do this?", then you'd see it will quote "bitter lesson pilled" at like 7 places in its response. This one seems to have eased off a little.
I feel like this is a quirk of LMSYS-style optimization where humans in SxS preference evals will pick based on formatting more than actual content.
Does this solve the 2 year old bug of recitation hell?
The free version now uses Gemini 3.5 Flash-Lite rather than Gemini 3.6 Flash.
Not available in my browser/app yet (pro), but I'm confident it'll roll out soon.
That was a long wait.
Does this mean Googlers will stop using Claude internally?
Can’t even get it on pro tier in antigravity?
What’s the point lol. I’m not subbing to ultra to have it
Going to take a lot for me to move from subsidized Claude opus 5.5 … this Google model is very cheap though.
at this rate we're gonna run out of stupid names for these things
"Hans, we're doing a new point release! What isn't crossed out on the periodic table yet?"
Since they are going alphabetically, they're already going to be fucked before getting to 10 (there's no element starting with J).
Not available for me on Pro in the UK.
As with the previous Gemini releases, rollout is batched.
So like... do I get access?
Is this supposed to be the new Gemini Pro?
isn't harness as important as model these days?
Just switched, and it seems to be much better. I'm hoping it stops lying. I have Pro max subs for Claude, Codex, Grok, and Google AI... I was forced into using Google by running out of credits elsewhere.
I have found that Gemini often claims it did something, but didn't actually complete the task.
I just had 3.8 Flash do a major refactor -- that never should have needed to be done in the first place -- and switched to Argon 4, and had it review it.
Almost everything had been stubbed out by 3.8, and none of it was properly integrated into the codebase. :facepalm:
Unlike most people it seems, I like all the progress in AI but I really do wish they would reign in the over-the-top marketing. I mean, can we just get this stuff to the point where I can reliably work with it to design a well-architected software project without having to constantly struggle against bloat and nonsense directions no experienced or sane developer would ever take.
fyi: argon is an interesting choice of name, coming from the Greek argos meaning "lazy or living w/out labor".
I think in this case it’s from the element argon
Yes probably but that's still from Greek (see: https://en.wikipedia.org/wiki/Argon#History)
Now when another uninteresting frotier model been released Google can finally start doing what we actually waiting for:
Start preparing Gemma 5 release.
Are they gonna go to Krypton or Neon next? Kinda started in the middle there.
I've asked Argon to do 3D model for me. It said that it can't do it. I've linked promotional materials and said that it actually can. It agreed and got me a model (not a great one). Man, why is Gemini like this? Every time I fall for the hype I get disappointed.
Glad to see them abandon the absolute train wreck of their model naming "strategy".
Gemini Live vs. Gemini Lite vs. Gemini Pro vs. Gemini Flash vs. Nano Banana Lite (that one apparently has no Gemini prefix?)... Please, just pick a metaphor and stick with it without wildly mixing reasoning modes, model sizes, and input/output modalities in the model name!
I use Gemini all day at work via the chat interface. It's rock solid. I find it hard to understand which model it's using though. Am I already using Argon, or does it switch models according to the subject?
can for the love of god we get some usage in cheaper subscriptions being Ultra tier only is annoying. I want to just test it out here, well will burn some money via api I guess.
Fable 5.5 about to drop the hammer on them and then another Astra version goes out lol.
Fable is very expensive though.
And so the game of cat and mouse goes on. Your point being?
>"Argon helped the Chrome team separate core webpage handling from a ~6 million line legacy folder of browser features held within Chromium’s 40 million-line codebase. Piping multiple clean code changes at a time, Argon helped the team finish an architectural refactor across hundreds of files, completing months of work in weeks."
Now that needed to be done for some time!
Regardless of if an AI helped or not, regardless of whether that was Google's or some other AI or not (though Argon does sound pretty cool!), regardless of whether an AI cut the time from months to weeks or not, refactoring Chrome -- decomposing it into individual clearly demarcated non-tightly coupled software parts with core webpage handling at the center -- that needed to be done for some time!
Now all of that being said, Gemini 4 Argon does sound pretty cool, and we congratulate both teams at Google -- the team that created Gemini 4 Argon, and the team that refactored Chrome with it!
But all of that being said, while the advancements made for Gemini 4 Argon are important and impressive (and they are!)... refactoring Chrome properly (IMHO) is slightly more important and impressive!
(You know, one man's humble opinion! That is, I am not the guy who is responsible for awarding performance-based bonuses at Google at the end of the year, so take everything I say with the proverbial "grain of salt"! :-) I am probably biased, one way or the other! :-) )
Why can't the Gemini model code? I'd guess Gemini probably used the best dataset. Even Google is the place where the world's best coders are gathered, isn't it? Yet the GPT code from OpenAI, which collaborated with Microsoft, is beautiful, while the Gemini model's coding is trash.
I have no problem with Gemini Flash 3.8 for coding, though I only use Elixir which is one of the better languages for LLMs. Where does it fall short elsewhere?
not available in our org (EU)
Meanwhile I paid a subscription and they only provide access to 3.6 Flash. Consequently, I have moved on to open-weight models. It is what happens when you screw your customers over.
You can probably get 3.8 via ai studio interface.
God knows why the server different things to the same people on different interfaces
3.1 Gemini was amazing for anything non-agentic. I switched to it for my primary chatbot purposes.
I never touch 3.8, it's awful. Curious to try this, but I've yet to use any Gemini that is good for agentic work.
Also, unrelated but this made me look at Artificial Analysis to see where it lands on Cost per Task, and it ranks Opus 5.5 at almost double Astra 6. That does not align with my experience.
Now let me use Pi on the plan.
That's why Gemini 3.1 Pro has been shit for weeks now.
very bad
baaad
why they wait so long ?????
It may be a good model, but I'm not reading scrollslop. It annoys me to much when I'm just trying to get information about something.
Not available for AI Pro subscribers in Antigravity. What a disappointment.
Google...why. You keep getting so close but constantly fumble at the last moment. I can use Opus 5.5 and Sol/Astra on the $20 Anthropic and OpenAI plans.
You are already playing from behind. You need to GET YOUR MODELS INTO MORE PEOPLES HANDS by any means possible. Continuing to make it difficult to use your products DOES NOT HELP.
This model might be great but the majority aren't going to subscribe for $100/month just to try it.
Have they announced that it definitely won't be? If not, it might be just a roll-out hiccup, because Argon is available to me as an agent in Xcode and I only have a Gemini Pro subscription. (Whether or not I'll ever use Argon in Xcode is a different question. I've been so impressed by Claude that I rarely even use my Codex sub. I'll be very interested to hear from others how well Argon does with Swift and SwiftUI.)
Their official documentation says it's available to Pro subscribers in the Gemini app only, and available to Ultra subscribers in Antigravity.
so this is prob why antigravity is not working well at the moment lol
Blatantly obvious that models will not slow down. For any task, if we can build a dataset or environment of verifications, it can be automated. This will eat all of human tasks.
The fact that multiple companies can breach any benchmark means there is no ambiguity in terms of how to build AI, its just a matter of time.
Certainly Meta and Google are benchmaxxing to please public markets, but it still proves the above
but they have slowed down. the delta in capability between model releases now is smaller than ever before and i expect it to continue to diminish until we reach some kind of asymptotic maximum, which hopefully will mean turn towards optimization and local model improvements. The current frontier models running at 10x the tok/s would be incredible for my use cases
The delta isn't really slower, we're just unlocking capability bundles in batches. The capabilities bundles you care about might have mostly been unlocked already, and just need refinement, which is coming slower than the initial capabilities came, but don't discount the breadth of advances being made across all possible tasks.
We're not living in the same universe. Look at capability year-over-year. The asymptotic maximum is wishful thinking. Someone needs to step in before this gets wildly out of control.
> Someone needs to step in before this gets wildly out of control. This sounds like a paid bot, lmao.
I wonder how much is actual improvements in the model and how much comes from better harnesses. I'd be willing to bet that the combination of better harnesses + the models being trained around agentic workflows (i.e the harnesses) has made it significantly better in many things, but it's hard to find other improvements imo.
You can see the difference between GPT 3, 4, 5, 6 each year. Same with Gemini and Claude. Look at image and text generation. Code generation. Math ability (Navier Stokes, Quasi-Riemann.)
Year-over-year, yes I would agree with you, but that is not the cycle time of models. And while I share the same hunch as GP, I'm waiting another cycle before putting more confidence behind it.
A little over a year ago they were winning their first gold in an elite high school math competition. Have you not seen what they are doing in math now?
opus 5.5 is a step change, you arent using the models enough if you think there is a slowdown
i burn through a $200 claude and $200 codex plan every week. opus 5.5 is not "step change" it's just a bit better, and the benchmarks show this. Honestly with my workflow I cannot really tell the difference between the top models, they're all pretty much the same in capability, just a bit different. You can build software with all of them, and with all of them you have to validate the change thoroughly to not ship stupid bugs
I do not see the current training regime of "relentlessly try anything to get the task done" as a true step change in intelligence. This is one of the reasons I suspect capability acquisition is slowing.
A more intelligent model would not default to spaghetti throwing, it would pause to consider the problem fundamentals, the accuracy of assumptions, and ask questions that remove ambiguity. This however does not lend itself to increasing token sales while prices remain inflated from market and brand dominance.
Models just solved a Millennium price and you think their progress is slowing down? I will concede that their capabilities vary a lot from one field to another, but still.
People use Antigravity still?
It's alright.