Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5
artificialanalysis.ai
artificialanalysis.ai
I got it via the "Pro" plan, exclusively for personal task usage. So no agentic programming.
For starters, the limits are dreadfully low. Five messages on medium is already enough to trigger a 4 hour cooldown. Secondly, the best part is the integration with Google search and Maps. Makes it seamless for trip planning. Lastly it's the only "SOTA" model in a long time that has actively hallucinated details. I don't know if it's because the Gemini harness is not asking it to verify information, but I've had some real Deepseek V3-like lies in responses.
Either way, I find it difficult to get invested in Gemini because nobody will have access to Carbon for a few months anyways.
Honestly the fiction writing was pretty impressive!
But my experience is that the agent matters a bit more than the model. It is fun on ChatGPT because of its recent (?) bias to always be creating images for conversations around fiction.
eg im working on rigorizing a proof that a fun algorithm of mine is not only the first provably efficient algorithm for a particular problem, but its also the fastest in practice. ive found that for the sort of stuff i need to assume the model will get confused in the out of training distribution stuff from my personal library of ideas, so they will at best be a confused secretary intern mashup
Will have to give Carbon a try, but if it is anything like what I saw before it was entirely unusable.
Capabilities are also improving so fast that this fomo doesn't really keep people excited. If I can't run it, I'll sorta just click off. And given how they handled the Argon release, I've not even bothered look at this Carbon one. Fully expected it to not be usable yet.
You are assuming that Carbon is ready for release, deployed to their datacenters, that all scaling and other issues have been worked out, and they are confident in all of the above. Not to mention additional internal constraints like alignment testing or other safety procedures their own policies and guardrails require.
If they are smart, which they clearly have some smart people if they are making the best frontier model, they would account for the above needs in their timeline, and announce the model when they can prove they have it but also before they are ready to release it. This is so they can be at the top of the frontier model lists, boost their brand for both Gemini and Google, get the bump in stock price and investor confidence, steer adoption decisions for people making decisions for what platform they might adopt, etc.
This is especially true around earnings, not necessarily for some sinister reason, other than it's material information that can influence the market.
they don't really care how end-users will receive these models, they're just announcing for internal targets.
Same as it ever was. Google is really completing their IBM-ification. We need to release these models now because year end reviews are about to hit and I need a win.
The target is to make a celebratory announcement.
In my limited usage, it has fixed issues which has not been correctly fixed by sol-6.1 (medium) - https://github.com/auto-gnufied/markdown-modern/commit/a189a...
I am also using it via Google pro tier and did not immediately run into quota issues. But as someone else said in previous discussion of this - Google Pro tier already includes lot of stuff (including Youtube premium etc), the AI plans are just a nice addon.
I feel like you're being overly dramatic here. Argon was 13 - 15 days.
Argon was announced September 30, made available on October 13 to Ultra everywhere, and Pro in the Gemini App. Pro plans got Argon on October 15 in AGY.
This is the first time they've slow-rolled a model. Who knows how long they'll wait on Carbon.
The Ultra-lite plans ($99/month) get some pretty good quota where you can actually use Argon some more. Maybe Google shouldn't be grouping together plans, but it should also be noted that $20/month also gets you 5TB of storage on Drive. If you paid for Dropbox, you get 3TB for $18/month (without any AI model usage).
(googler, opinions are my own)
By the time Carbon comes out, I fully expect Anthropic, ChatGPT and/or a Chinese lab to have its answer ready to ship in less time than it took Google to approve a release.
Gemini cannot create "my maps". I like creating a map of stuff for travel and now I ask it to create an importable file (which it doesn't always get right) but it should honestly do that itself.
And I don't mean that from within the Gemini app, I'd like a shareable list of places, which you can do via a very outdated maps UI
You might as well be asking an Adobe employee to bring up improving integration with Canva.
Doesn't every Googler have weekly meetings with Tim Google?
As long as there is no usable customer service and/or ways to limit the blast radius of incidents this is a nogo for me.
my whole family of 5 people is using in total 160 GB. personally I use about 15 GB
That's the problem for me. Google can have the world's best model; it doesn't matter to me, because I effectively can't use it.
Both Opus 5.5 and Sol 6.1 are extremely good and provide a lot of inference on their $200 plans, and then there are the Chinese models - DeepSeek 4.1 Flash, MiMo, and StepFun all offer a lot of value per cost.
At the moment, I don't see a niche where it makes sense to use Google's models, regardless of how good they are.
for coding .. it works ok but i prefer the openai or anthropic models, much better results.
Since then I tried it for experimenting on diverse fields. For example, this week there was a discussion here in HN about the non-continuation of Python funding on the JIT, so I "played" again to removing the GIL and moving to the JIT in Python, it did it. I played with many other things, even the Riemann Hypothesis (it just removed their rational parameter rounding only moves 0.875 towards 0.87495702, nothing significant though).
Many other relatively great things were done this week in parallel. I have some budget for research.
Is that chat messages that involve tool calls (web search, file reads, etc) or actually just messages?
When I'm on the $20/month OpenAI plan, I can get about 3-5 turns out of Astra before the 5 hour limit is up.
When Fable was on the $20/month Anthropic plan, I remember getting about the same.
3-4 turns out of the $20/month plan would seem about par with other providers for their best model, but really Carbon should occupy that slot, not Argon.
If Argon's limits are that low, it wouldn't make sense to use that plan for anything when models of similar intelligence are available with much higher limits on other $20 plans.
Gemini so far has also had terrible computer-use capability or tool-use capability compared to ChatGPT (haven't used enough of Claude for these tasks lately to really compare). I tell ChatGPT to make some formatting changes to a LaTeX file I gave it and generate a PDF, it get's the PDF generated most of the time; occasionally the download link doesn't work, but that seems to be a server or link-generation issue, the actual PDF is generated and it quickly fixes the broken link when told. With Gemini, it's a long struggle and I use LLMs for formatting LaTeX a lot, so sometimes if ChatGPT is doing some long-running task for me, I have to begrudgingly give the document formatting task to Gemini, and it's dreadful every single time.
Before Argon was a thing, it once generated a PDF file, then started claiming PDF generation was not part of its functionality and the link that was previously working stopped doing so. I think I had inadvertently jail-broken a functionality because it refused to generate PDFs again and said the first time was a mistake. With Argon, it doesn't deny having the capability, but it takes much longer than ChatGPT, and messes up more formatting than it fixes. Its PDF viewer is also trash and seems like it gets built from scratch every time you want to view a PDF.
I don't know what it is about Google models, but they are the worst at hallucinations. I don't understand how they can so consistently be this bad.
Gemini models always suffered from this. They were almost always best in terms of intelligence, but one of the worst to use in practice, because they have a lot of failure modes on simple tasks that simply give factually incorrect answers, or completely break and output gibberish.
From Google saying that Argon would be 'available soon' (as they did just now for Carbon), it took them 13 days to release it. So... unclear that Carbon won't see the same trajectory.
It looks like the best sort of bang for your buck is still GPT Sol and GPT Astra. Claude and Gemini have just been going for that number one intelligence spot by sacrificing a lot of efficiency.
It does sort of show their hands a bit that their larger goals might be outside of just consumer sales but also seeking hyper intelligence that could bring value in new ways of discovery (if it ends up being a legit outlet for such things consistently)
Yesterday I used Google AI-Search (so Gemini Flash 3.5?) for opinions on a traffic accident and it continously constructed law-interpretations and then referenced the wrong paragraphs. It talked about rules on road-crossings and cited paragraphs pertaining to alcohol intoxication as sources.
Could you expand on this? I’ve never seen a frontier model that doesn’t hallucinate details fairly often, what do you mean by “actively” here?
The only thing Gemini has over those is integration with my Google account, which is genuinely useful. But I reckon Google could release open weights for their whole product line without affecting market share much.
the problem with gemini is the whole app itself, not the model.
when you use other chat app, like chatgpt or deepseek chat, the app will use web search for everything to verify. and if it can not find anything useful, or the current fact is disputed, it will try to call web search tool again and again until the consensus.
meanwhile gemini will refuse to do web search if possible, and only do it a single time if demanded. worse, it uses the same result from google search, so the top ranking answers are almost all SEO garbage.
since then I just stopped asking gemini again. and I don't think the newer model will change the situation.
Trip planning is the most often used use-case for agents/bots I see in marketing. I'm lucky to travel once a year, but if I did have a trip, I wouldn't feel comfortable letting an LLM take the wheel. Too many variables for wrong flight or room, closed locations in itinerary, etc.
Now, critical server infrastructure? Sure, have at it Claude, make no mistakes!
Plus, it's great at researching the history of places you're visiting, so you can find cultural experiences a bit easier.
I don't travel often, but I do live in Europe where it's easy to visit a new city or country in the weekend. This makes this functionality a quite common use for me.
https://www.youtube.com/watch?v=NO2I3ueVQrk&lc=UgxRXtipA3QbA...
tldr; it's not as good as opus 5.5
I used to defend Google's models months ago, but switched to Luna for less intelligent models.
But with this bs of not making it even public, pushing off release of newer models late, I lost faith in Google.
e.g. NotebookLLM was one that got me interested in Google's ecosystem. It was easy to use and explain even to my parents.
Now, their models (and NoteBookLLM merging to Gemini, making it extremely hard for non-tech people and other stuff) release very slowly, their models lie their ass off, very politically skewed, etc.
Also, their tooling still is complete shit. I'll not be forced to use Antigravity thing even if it is available and clearly better than Opus or Astra. Their subscription is useless. API can be used with anything you got but that's gonna cost.
I never use any google models for any agent work any more, in case they block me.
Started migrating gdrive, and gmail to other providers at this point after horror stories in HN, that they lost access to their gmails, etc.
Prove me wrong.
It's been provided to AA under clandestine circumstances, no you can't verify it yourself, this might not even be the model you get.
People on HN for some reason "wow this thing that I can't use is so great, really excited about all these ads I just looked at"
Having a great time with an ice cream brand at a store is no assurance against hollowing out the brand tomorrow.
Especially with the recent addition of graphic visualization widgets
It manages to strike the perfect balance between depth and style of prose that other Frontier models just can't quite seem to match
My experience with it is mostly using it against an actual, academic source, where I can easily tell if it starts making things up. I wouldn't rely on it for anything that I can't confirm myself.
I know it is common for people to try AI in domains they aren't experts in, but it is also common for people to use AI in domains they are experts in.
1. Sleep debts effect on muscle size and strength gain
https://share.gemini.google/Gvdb5Q02INrl
2. Fish oil and extra cellular vesicles
https://share.gemini.google/fem9EBCe4mfy
3. Pharmacology question about human-equivalent dose
Here is your first example, "corrected": https://share.gemini.google/DCSLT4PPap5q
Having an AI do a quick check of research studies is about as effective as just trusting a Vogue magazine article. You are just fooling yourself into thinking there is something useful there, there is not.
Classic "you're holding it wrong" (maybe enhanced by exercise/nutrition science being what it is with a study and expert representing every side of an argument)
I'd personally probably do it like
> Someone told me about a recent paper which showed that sleep deprivation had surprisingly little impact on muscle hypertrophy, and that mechanical stimulus seemed to be the dominant factor.
> Can you comprehensively fact-check this, and let me know what the general scientific consensus on the topic is?
Even then you can't be certain of course. Even reading the cited studies you can't be certain unless you know enough to interpret them. But you'll probably be a bit more informed than you would be if you were just going off news article titles you see on social media like the average person.
I'm trying to figure out how heat dissipation works. Specifically, I have a block of aluminum with extruded fins (as a heat sink) and I'm trying to understand how many 30-watt LEDs I can place on it. I have a fan as well, and I'd like to understand how fast I need to run it at different power levels (the LEDs are dimmable). I can gather measurements empirically (which I'll do), but I also want to understand the basic mathematical basis I should use for estimating these values.Its the first model ever that I have been worried about a successor losing the "feel" that it has.
Comparison with other models.
The benefit for Google is their chat interface has privileged access to their data (maps, etc.) but their model itself is low quality and, if I'm being honest, has the promotion-oriented measurable-output bias that FAANGs tend to have. The later Llamas had the same problem.
It is unfortunate that having such an amazing engineers that are capable to solve any leetcode exercise in 5 minutes on the spot with a white board, they aren't able to even deliver something useful on the AI space.
Daily usage: normal task, modify this document, do this do that basic office task... anything that requires precise context is doomed for any google-based llm, that is a well known issue of even the gemma models. Only time it has been useful is while using antigravity that makes sure everything has context on every turn therefore wastes more tokens than Fable on the same prompt. Any other LLM will do it faster, burning less tokens and with better results.
The benchmarks often have ambiguous instructions, and there is no interaction with the agent. So a docker image is started, the agent is called in non-interactive mode with a prompt saying to read the instructions and finish the task. The instructions are often somewhat ambiguous and the agent has no way to ask for clarification. If it picks the wrong option out of many seemingly equally likely options that test will be mark as failed.
I get that you're saying the model might get a higher score because of luck, but at the same time you want the model to be "lucky".
I wish Argon were as lucky as Opus.
The latest update knocked down only openweights models from the leaderboard.
Without us being able to access Carbon when it is announced, there's no way to know the Carbon we get a few weeks later is the same thing as what has been benchmarked here. I think benchmarks should verify what is benchmarked is exactly the same as what's been released.
[1] https://news.ycombinator.com/item?id=49901736* Definitively very good at domain specific knowledge. It can read sloppy 19th century danish gothic handwriting with faded ink just fine. Not perfectly, but probably better than I could have done with a day of struggle for every page.
* Expensive.
* Bad at actually doing what I said. I had told it in the prompt to be careful not to miss text, but I hadn't explicitly told it that some images may have text in double-column layout. So on those images - over half of my 300-page document - it produced an excellent transcription of the first column, and then considered itself done. Want to try again with a more careful prompt? That'll be another $20.
I also found that it ignores other instructions sporadically ("Don't use letter variants or ligatures in the output, use the normal Danish/Norwegian alphabet when at all possible", still a few Unicode tall s'es in the output.)
It's good, but it's so frustrating since "worse" models manage to follow such instructions much better.
To get the best performance you really need to present your problem as an iterative verification loop.
That seems arguably pretty cheap compared to the human alternatives.
I do get super annoyed at how specific you've got to be with these tools. They are like almost like malicious compliance geniuses lol.
They're like a monkey's paw or evil genie I think.
You'll get exactly what you ask for, but lots of weirdness you didn't expect.
To be fair to the model makers, deciding what to do with an under-specified prompt is hard, particularly in an RL environment where you can't just ask the user.
For stuff I want to run for a while, I'm defaulting to telling it to ask me the questions upfront to reduce the incidence of these kinds of problems.
Until it got lobotomised. Then it lost most of it's capability.
Similar situation happened to Anthropic's Fable. It was an amazing model until Opus 5.5 came out.
So now, I'm not even looking at a "new" model until it is a month or two old. By then we will know if they will destroy ability to make it cheaper to host.
It gets so stupid it's frustratingly useless when it does not follow instructions give just a few prompts back.
https://artificialanalysis.ai/models/qwen3-8-flash-next
https://artificialanalysis.ai/models/glm-5-3-flash/
edit to add: It's very disturbing, because these open models (GLM particularly) were among the best of all LLM's on their benchmarks. On the intelligence/cost frontier, specifically. Archive.org remembers that GLM Flash was in their green quadrant just a few days ago, next to Haiku:
https://web.archive.org/web/20261014031624/https://artificia...
It's completely gone now. AAI (as far as I've seen) haven't publicly commented about why they did this.
I also think Google should stop "releasing" models that aren't actually available. For their own sake, because by the time Gemini 4 Carbon becomes available, nobody will notice. There won't be a new press release or much excitement around it.
Since then none of the Google models have competed well with the others at their price points. Long context performance drop, instruction following issues, unnecessarily complex and sloppish outputs.
Argon is no different, it feels just an iteration better than 3.8 flash. I don't trust the AA ranking yet.
Also the guardrails were implemented in a very bad manner. By default the models used to raise a default guardrail error on basic queries.
My experience is that Google is aiming to create a generalist model – but a truly generalist one! In fact, I find it particularly accurate in general terms and even in long conversations, maintaining excellent consistency and a wealth of information in response to complex questions.
I’ve tested it both as a coding agent – though, precisely because of what I mentioned above about its ‘generalism’, I don’t think it measures up to Fable 5.1 or Astra – and I’ve really appreciated its qualities when supervising my agents’ activities. It was able to keep track of them, suggest alternatives, and monitor the entire lengthy process in parallel without ever straying too far from the request or losing the thread – as often happens with Claude and OpenAI (you only need to test it in Claude Code itself, in both Ultracode and the agent team mode – albeit experimental – to realise how, once the task gets underway, both of them lose their way).
- Two days ago AA announced their v5 Index which added a few new benchmarks and tweaked a few things.
- Today the newest model announced from a big lab tops the charts.
I'm not saying that AA is definitely taking money from the big labs and changing the benchmark to make the labs' new models look better, but it kind of looks that way. This pattern of behavior has happened before, where the index gets tweaked right around when a new model comes out, and the new model is on top. AA does not, AFAIK, provide a way to see how the new models would have scored under the older index versions. You could probably calculate it yourself, but I'm not going to do that much legwork just for an HN comment.
It's interesting because they publish the index methodology, so you can't really accuse them of not being transparent, but I don't really believe that the choices to update the index were completely independent of the model they happened to be evaluating at the time.
Related: The DXOMark camera scoring method also had similar behavior, where they would release new testing methodology right before flagship smartphones were released. Again, they are technically being transparent about the process, but it kind of smells like the "independent" testers may not be as independent as the average user would hope.
Example problems are:
- https://hub.harborframework.com/tasks/terminal-bench-science...
- https://hub.harborframework.com/tasks/terminal-bench-science...
I have no doubt that these are very relevant task for people in those respective fields, but I suspect they have next to no relevance to some 99.9% of model users. Hence why especially smaller labs with smaller models didn't really train for such use cases and you can see it.
Personally, it's not something that matters at all to me, as I don't work in cheminformatics or bioinformatics.
This is also arguably more of a test of a model's knowledge base than reasoning ability.
But most importantly, there is also the question to which extent the aggregate score then remains a meaningful result when you mix up too many too disparate benchmarks.
The coding agents subindex, in contrast, looks at least superficially plausible [1].
[1] https://artificialanalysis.ai/agents/coding-agents#coding-ag...
Check out vals.ai for an alternative. Not sure how independent they really are either, but it's at least good for a cross reference.
So ... why bother to cheat?
I think part of it is to justify capex. "Yeah, we spent x billions on this, but look, we have the best models, we're beating OpenAI and Anthropic, we're winning, it's all worth it!"
For specific benchmarks, I’ve downloaded and used basically every model out there under 35b parameters - especially the tiny models: ling-3.0-tiny, spark-x2.6-4b, nanbeige, etc. etc. some of those tiny models are ranked VERY high on benchmarks like deepswe or terminalbench but are obviously incapable when you try to use them for swe or terminal based tasks.
If that’s benchmaxxing or a bad benchmark, idk, but it’s made me not trust the benchmarks much. If it benchmarks poorly, it’s probably bad. If it benchmarks well, it might be good.
At the previous iterations they were accused of favoring OpenAI. Now they are accused of favoring Google.
I do think the test a new model and realize their index is broken- the new model isn’t on top, so they change the weights to make sure it’s on top.
The difference is that you seem to think they are doing so out of some innocent feeling that the new model is actually better, while I think they are doing so because they were paid to do so. I think my interpretation is more likely, but that’s just me.
Or are you implying that the Chinese can't afford the amounts paid by OpenAI/Google.
Do they work with frontier labs? Probably. They should work with them at least a little so that we can get an independent read on the model quality. Do they inject new tests that specifically favor one particular lab? Clearly no, as different labs have moved up over time. Do they inject new tests that help to provide some separation on scoring? God, I hope so.
Nota bene, another change they announced was dramatically increasing the amount of held back test data; this would be a simple explanation for seeing open weights models, all of which are known to distill from frontier labs and benchmarks, drop in the rankings.
I think what's happening is Chines models genuinely try to benchmax on it and hence they suffer when a new version comes out.
I read somewhere on Substack that a lot of this was driven by the inclusion of more knowledge work benchmarks, where the US labs are stronger, versus coding which is where most Chinese models appear to put their energies for now.
Here's mine for coding in Rust, and I definitely see the same pattern of "most chinese models do much worse than on public evals that have been around long enough to train on" that AA v5 surfaces: https://slopcop.com/power-ranking
I feel like the headline here is "the Artificial Analysis index is becoming an increasingly poor measure of capability, having previously been quite good".
My hunch is that Google will end up being best for non coding tasks. Google can concede coding to other vendors who pay Google for infra.
True, I use LLMs as glorified search engines to unearth material to read myself, but Gemini is much more useful if you decide to learn yourself. It also offers interesting expansions to your questions.
While I don't offload my work to LLMs (e.g.: Write this project, I'll be watching TV), I find them useful for closing my knowledge gap by collating information, and Google's Gemini is good at that.
I'm on $20 Claude plan, and it's also ample for my needs, for now.
They have top of the line research, I mean, they invented the thing, and they are still doing stuff that goes beyond making a bigger LLM, as it seems to be the only thing the others are doing, research-wise.
They also have hardware capabilities no one else has, they invented the TPUs, and have lots of them.
They also have the brand recognition, ecosystem, user base and lots of money.
So why are they lagging behind on the actual products, and by lagging behind? I mean they are always in the position where they are just behind the frontier, but still in the game.
It seems almost deliberate. Maybe they are playing the long game, waiting for the bubble to burst. Maybe they don't want to compete against the more profitable parts of the company. Or maybe their researchers are not interested in making an actual product unless being forced to.
Even if these numbers are correct, they don’t have any useful product for coding or search. Not sure what you’re referring to
I wouldn't even bet that Google is better than OpenAI and Anthropic for these categories, but they seem to be well used outside of our direct space.
So here's a meta-benchmark: at what point does the act of choosing an LLM product for a reasoning task become less reliable (in the sense of "predictive of future success") than hiring a human being for that same task? Have we already cross that threshold? I think we might have.
And once we do... what happens then? Will the truly elite models be hidden in a sea of mediocrities simply because the goofball "managers" doing the hiring are too dumb to recognize their genius?
"Hey we are launching this thing but we've got to let the safety folks see it first".
So they look like they care about alignment etc.
To be honest there is a lot of rage on here about it being announced before being able to access it. I don't understand why people are getting so upset about this - like no one gets into a rage because they see a movie trailer before it is released?
AGY never even update often, and only has gemini 3.8 flash (for Google model).
It's hard to understand the phased rollout of a model that isn't particularly sota, unlike Mythos a few months ago.
Let’s see, maybe they’ll surprise this time.
I take this opportunity to give feedback on Argon. The limitations are a problem. I have a Pro subscription and Argon already stops working after 3-4 prompts with long delays afterwards. It's virtually useless right now even when you pay monthly for it. It is better for some tasks than 3.1 Pro, though. Not much in my experience but it can sometimes fix errors that 3.1 has problems with. I never use Flash models since they often give abysmal results.
So I'm stuck with using Gemini Pro 3.1 for now.
Thats on a 22 bucks plan.
idk wtf Google is doing with their plans but really need to make an effort to put some distance between myself and that circus
Google's model release is pure PR puffery. It's no coincidence that it was released on the day of its earnings call.
Argon did not leave a mark or change anything
Why would anyone care about carbon as well
No, you can't use it. You can't even see it, and I won't provide you any information. But you must trust me that I am the genius who made the best model in the whole world.
I mean, that's the main reason. How would you expect to see the kind of hype that Claude or GPT can generate when nobody can use it? The hype of those models are primarily generated from people using them to build impressive things and share those results. Can't do that with an unreleased model, right?
Beyond that, it seems a lot of people have learned that these agent leaderboards really don't tell the whole story. They're a good litmus test, per se, and there's plenty of hype being generated around these scores as-is. But anybody using AI in practice knows that benchmarks can only say so much, and one model scoring better than another doesn't necessarily imply that that model is actually better in the ways those people care about. You can see that in this thread as people are pointing out that even these benchmarks seem to suggest that Opus 5.5 is still leading in coding (which is what a lot of people, especially on HN, ultimately care about).
>just not enough publicity or hype as the other labs do?
Well, it's effectively both. Google is certainly not that pressed to generate as much hype as these other companies. They can afford to take their time. It's also worth noting that Google is definitely targeting different consumers, investors, etc., so their strategy will definitely be different.
For what it's worth, I've been using Argon since it's been available in Cursor and I really like it (at least at its current price). I think Google really did close the gap a meaningful amount (maybe not all the way, but enough to prove they're still in the game). Carbon being significantly better while being the same cost is pretty exciting to me. But until I can use it, I don't really care about it.
Is this API-only or US-only for now?
"We're taking a deliberate approach to rollout and are actively engaged in the U.S. government’s voluntary process for pre-release model testing. Starting this week, we will make Carbon available to a subset of cyber defenders and will gradually phase access to additional trusted partners through our Fairwind Program. We’re continuing to gather feedback and iterate on guardrails before making Carbon available to developers, businesses, and consumers via our product surfaces as soon as possible."
Want a private benchmark and see where you'd rank? $$$.
Want to show up day 0? $$$$.
Want to show up day -0? $$$$$.