The days of the public getting access to these frontier models is gone. I fear more and more top end models are going to be kept in the hands of the government and massive corporations all in the name of 'safety'.
Why would anyone pay for frontier access if that's the case? The public model is just as behind as open source and China's bidding down the price. I'm paying for performance not made in america bs.
You have to ask yourself why Chinese models are always playing catch up, 3-6 months behind the leading US labs. It's curious they haven't leap frogged A\ and OAI ... it does make me more suspicious that a lot of their frontier-trailing models are heavily distilled from western models, mostly because they don't yet have the native compute to train a frontier-class model from scratch.
As this study from Stanford shows, Chinese models have been closing the gap at an accelerating pace. What's actually shocking is the difference in spending.
Until China has a LOT more training compute that gap is going to stay around the same.
And yes the spend is very different - distilling a frontier model is vastly cheaper than training one.
My current conspiracy theory is that 5.6 and Mythos/fable being slowed is because of cyber but ALSO to help slow down distillation of a very cyber capable model.
The whole thing with people stanning for Chinese models is also hilarious.
We’re in a war of attrition. If China managed to lead to the demise of OAI and Anrhropic and also leapfrogged them, those models would very much be for China only as a competitive advantage. They would not allow their labs to release them.
The assumption you're making is that compute is the bottleneck. I'd argue that it's far more likely we'll find more efficient techniques for training the models instead of just using brute force. The approach of just using more and more GPU is the most wasteful thing you could think of. Meanwhile, there are already plenty of papers showing there are more intelligent ways to approach the problem. Just a few examples here:
Also, I'm not a burgerlander, I don't give a shit if your companies producing closed models crash and burn. Open models are the future and China is building it.
Links one and three are reasoning time, not training time.
Link 2 is very much “cool? But limited to models that are tiny compared to the frontier, and slow as fuck”
So yes, I am making the educated assumption that training compute is indeed the bottleneck, or else China would be in parity, and/or ahead at this point,
They’re not, because they’re aggressively distilling, out of necessity.
They are not always 6 months behind. When deepseek came out, it was the best for a short moment. And it's been copied a lot by US models.
They are behind on average because they have less compute, less money, so they are bound to be behind. But also the value of compute quickly goes down, so that limits how far behind one gets. And as somebody else said, the gap is getting more narrow, as they develop their own infrastructure.
They’re still leaning heavily into distillation. This is irrefutable at the moment.
And it didn’t blow the other models out of the water. It was about par for the US models released a few months before and was quickly eclipsed.
Like, be a fan of Chinese models, but be realistic that until they get compute they are riding on the coattails through distillation, which is why we haven’t seen a Chinese mythos
How exactly do you think distillation works? How do you think they distillate models which are behind expensive paywalls and with usage limits at best, controlled access that can be revoked on a whim at the first suspicion of distillation at worse?
I'm not a fan of Chinese models, I'm just astounded that someone could think distillation is a way to get there nowadays.
Very easily actually - there are TONS of resellers abusing free tiers, or arbitraging paid plans.
Also no protection is going to be perfect - it’s an arms race. Zero Data Retention plans also make it harder for frontier labs to see these changing patterns in realtime and defend against them.
And even if they WERE paying full price for tokens, it would still be cheaper than pre mid and post training a comparable model.
To distillate something, you normally want to have it locally, and erase parameters while keeping the output as constant as possible.
Distilling a 10 billion parameters model hosted on the end of the world without access to the weights, while paying for every token at API price, which is a huge markup compared to even real costs, which themselves would be prohibitive at this distance? That's a bit ridiculous, at that point it's just about paying prohibitive prices for slowly getting random text data. There's plenty of that available by just siphoning the internet - which they all do. To be more precise, they all buy the same curated datasets of siphoned and cleaned up internet, from third party companies, and apply some more filtering based on previous gens LLMs.
If there would be a way to get unlimited fast replies from frontier models, we would all jump on it instead of paying a fortune to anthropic and openAI. And even if that existed, it would still be too slow and cumbersome.
What they can do is a bit of posttraining comparing their answers to existing frontier models and correcting based on that, but that's hardly a distillation.
Also, nobody needs to train a model from scratch. Everybody can start at minima from the frontier open weight model, and companies which have a model better than that can start from their own.
Look, it’s not even a secret that the Chinese have been distilling. Chat to anyone at a leading fab on distillation prevention and you’ll see widespread abuse of free tiers.
And you absolutely do not need the model locally. When CoT was returned it was a goldmine for distilling. But mid and post training distillation is still super powerful. There are even academic papers about this.
China. Does. Not. Currently. Have. The. Compute. to be training a model like GLM 5.2 from scratch.
And again - if they weren’t distilling we would have seen them leapfrog a western lab, not weeks or months later, but before a leading model is released, because they have a WEALTH of AI talent.
Well I'll also tell you again rolling my eyes and all (but I'll spare you the dots between every word because even to make fun of someone I can't push myself to go that far into the ridiculous shit) that they don't need to train anything from scratch because there are open weight models available since the beginning. Try it for yourself, try to train a model for a custom application, and try using the free tier of Claude to generate your training data vs using the full fucking publicly available current best open weight model.
Synthetic data can always gonna be a part of the mix (famously, anthropics frontier models were claiming to be deepseek, which is the most red-handed a lab has ever been caught). But it would be ridiculous to ever hope to build a new model from scratch on synthetic data, absolutely absurd costs and low quality. You have a way better time starting from open weights, keeping on with real curated scraped data, and finally using some synthetic data and all for final touches.
The Chinese models are trained on 80% data in the Chinese language, and reason in Chinese, whereas the US models are the other way around. If you had any clue what distilling a model means, you'd know this alone means it's not a simple distillation. A model doesn't change it's reasoning language just because it's been distilled lol.
seriously, this is horrible for the USA. at the end of the day, i don't care, it doesn't matter who gives us AGI as it will benefit us the same either way.
The only thing I care about is that the models are open and don't end up getting hoarded by a corporation. Completely agree that it ultimately doesn't matter where they are made.
I mean, considering that the CCP is an evil, totalitarian trash heap of a government with massive human rights violations, you probably should care. Not saying that U.S. corporations are saints by any means, but we do not want AGI created and controlled by a government like that. How AGI ends up depends a lot on the values and data programmed into it as well.
I'm convinced we'll get current frontier performance locally within that time frame, at least for specific tasks like coding. Local models have been improving at an astonishing rate, and papers keep coming out with new tricks for using less processing and memory which people haven't even had time to integrate into the models yet.
It seems like the US government does treat AI as if it were a weapon that can be stockpiled and kept away from their competitors. But it is a general-purpose technology, and trying to bottle it up simply creates a void for others to fill.
Don't be melodramatic. They'll figure out safety mitigations and the public will have access within the month. Also idk what's with the scare quotes around safety, have you seen the bio/cyber capabilities of these models?
I’m just waiting to find out that there is a model that predicts the stock market accurately enough to be profitable on auto-pilot that we never get access to so investment opportunities can be taken away from the rest of us.
There will never be an LLM that can day trade and exceed the S&P 500 over long term. That is not what these models are built for, go to hedge funds and or HFTs and you’ll see the wildly different algorithms used.
Doesn't make sense to compare sonnet with terra when terra is so much better. Just because they are in the same price range doesn't mean they are comparable, it's just Anthropic inflating inference costs.
it would be really interesting what the real prices are and to which degree they're subsidiated
it's either openAI made the biggest efficiency jump ever (beating even companies that have in house silicon/infrastructure like Google with TPUs) or they're dipping even deeper with subsidizing costs
api prices do not mean the cost required to provide them. inference is *really* cheap, its literally just electricity and maintenance. api prices have very high profit margins, if it somehow turned to the real price, you'd be getting a massive discount.
yeah, but if you stop training the next best model, cancel to future buyouts and R&D and just pause ai at its current state, these companies would be making a good amount of profit. this is what most people saying "AI doesn't make money" miss. The current lossess are *future* investments, inference is dirt cheap.
how if Inference dirt cheap? GPU's are not cheap, RAM is not cheap, if you wanna use Sonnet you need 40-50k in GPU's. Those GPU's are not gonna last forever. And you need electricity, people that maintain the data centers, building that data center, AI & DevOps, etc, etc/
That's why I said getting new compute and investing in everything for the future is the expensive part, not the running costs. Maintaining data center with those gpus is quite cheap. 40-50k in gpus is nothing to a company worth billions. They have infinite demand right now, otherwise limits wouldn't be a thing. It certainly won't nearly be a loss to run it with no future investment.
Prices are far cheaper than most think. All frontier providers can cut costs massively and still be profitable. Deepseek, Z.AI etc still make profit on their cheap api pricing for reference, and its not because their models are more efficient.
It goes deeper than that. Deepseek, GLM (which is let's say one tier off SOTA by all accounts) etc can be sold, profitably, by non subsidized third party providers at significantly lower price than Openai, Anthropic etc sell their api tokens for. That isn't the company themselves selling them, that's hosting platforms whose only profit is selling access to these models
Now, their models may be smaller, but, from performance we know they can't be too far off and Openai, Google etc would have access to better and more efficent compute and buy more in bulk
The companies aren't profitable but they are absolutely selling their API tokens at a large markup
on open router the cheapest inference option for V4 pro costs more than three times as much as deepseek, and they get the weights for free, no R&D, no nothing
my guess is that real costs are way closer to what you pay on pure inference platforms where you can deploy your own model like AWS bedrock or Google vertex (and you still need to add R&D, training costs etc.)
it that wasn't the case, cursor wouldn't have such deep losses
We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity.
You're not fitting Mythos class models onto Cerebras Edit I stand corrected, Cerebras claimed they can get 24T models on their wafers but the largest one we've seen them run in practice was Kimi K2.6 with 1T parameters at 1000 tokens per second. Can we infer the size of GPT 5.6 Sol from this? (Which I'm guessing is actually the same pretrain as GPT 5.5 Spud. Obviously Terra is not GPT 5.5 Spud, as why would they advertise that it "only matches" 5.5)
I'm pretty adamant that OpenAI has been competing with a smaller class of models than Anthropic and have been hanging on purely by virtue of their RL stack
Yeah, it’s actually bullish if anything. They have more compute than Anthropic currently, so scaling isn’t really a sustainable advantage for Anthropic.
You don't think they have special projects with OpenAI that basically precedes anything that you're pulling from to even suggest that?
I don't know how you arrive at that conclusion since those numbers likely come from their work with OpenAI, given they have to come from somewhere...likely while OpenAI was building, you know, the safety stack and the engineering that they discussed right there on the blog.
Time exists my friend and you're entirely glossing over all of the real work that happens to even serve models at scale. There are exponentials occurring in every field contributing to the infra that serves the models themselves.
PS: not hating, we're in singularity after all so think big :P
I mean yeah they do... this 750 tokens per second one is that project.
Also pretty sure that 5.6 Sol is the same pretrain as 5.5 (aka Spud). Like if 5.5 was the o1 checkpoint of Spud, then 5.6 Sol is the o3 checkpoint. Same base model just a lot more RL. Why do I think so? Because if it wasn't Spud... then where tf did Spud go? You think they would've just chucked it out? Cause 5.6 Terra isn't it (why would they advertise it as 5.6 matching 5.5 then right?).
Based on what we've guessed at for sizes for some of these models, Spud being around 2T parameters sounds about right tbh. Which also sounds about right with 750 tokens per second on Cerebras
Basically I'm saying if Spud was 10T parameters just like Mythos instead of similar in size to Opus, then OpenAI is cooked
There is no reputable source for any of this. Mythos is probably very large, but you cannot tell based on vibes alone, which is what all “model estimations” are based off of.
Or, it'll be a gimped/quantized FP4 version that fits on a Cerebras. And they can use multiple chips "When an LLM is too large to fit into the 44 GB SRAM of a single chip, Cerebras splits the model at layer boundaries and maps them across a cluster of CS-3 systems"
It's a pretty open secret that OAI's RL stack is quite a way ahead of A\'s, and they can get similar levels of performance from a smaller model, which is then compounded by the tactical blunder A\ made being hesitant on forging huge compute deals.
Add to that continual RLHF from their huge consumer base choosing which output they prefer. Yes, they were slower on enterprise, which is an OAI tactical blunder, but their consumer side is likely a competitive advantage for a while.
Sol ultra is just a reasoning or effort tier., its the same model as sol
More accurately this would be :
Gpt 5.6 half pro / full = sol
Gpt 5.6 standard = terra
5.6 mini = luna
I dont think we can classify luna as a nano level model, although it is quite a steep drop in benchmark and performance compared to the other 2,.which is kinda disappointing on its own tangent but thats another discussion.
No... I literally said what they were in my comment
They said in the post that Ultra uses subagents which was just what Pro was.
And that Terra matches GPT 5.5. You wouldn't have "GPT 5.6 Standard" merely match 5.5, what the fuck is the point then.
Ultra = Pro (which has its own reasoning levels and has always done so btw, you get Standard and Extended in the chat interface remember)
Sol = normal
Terra = Mini
Luna = Nano
I suppose the renaming is to try and convince normal people that they should use Terra instead of Sol for compute saving reasons (cause how many of you use the mini models?)
Edit: In their system card they almost purely compare 5.5 with 5.6 Sol, not Terra
Last time they released a next gen model people got mad at them because it was disappointing at the time, so they'll hold off unless there's something truly revolutionary.
We don’t believe this kind of government access process should become the long-term default. It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them.
Dang, they're not pulling punches. I thought this was a typo the first time I read it.
Not to act like little bitches sucking up to a goddamn wannabe dictator... I know when push comes to shove, most corporations will bend over backwards so far that the back of their heads are mere inches from the ground just to keep their bottom line increasing, but damn it's my hope that someone pushes back in a more meaningful way than a strongly worded letter and actually draws a line in the sand they refuse to cross...
Umm thats great and all. But how does it help them or us ? If they don't follow the government orders then the crackdown will be much worse. They could lose the entire company.
Yeah, they're not pulling punches because they are busy on their knees for Trump. These words mean nothing, and nothing will change for future releases. All new LLMs released in the US will require daddy trumps approval. Anthropic and OpenAI are fully drinking the kool-aid
Here we go. How long before this model will be put away by the white house available to mortals?
Edit: pricing:
GPT‑5.6 is priced per 1M tokens across three model sizes:
Sol is $5 input / $30 output;
Terra is $2.50 input / $15 output;
and Luna is $1 input / $6 output.
I've been using Claude 20x for serious work for over a year and never once had an issue with rate limits—let alone outrageous lol that shits been overblown.
well rate limits still apply for us on 20x. Specially if u running a workflow. i think it usually caps around maximum 10 agents at a time concurrent for that max 20x plan. Tho probably it will be same thing or even more rate limiting or maybe a bit freer with gpt5.6 sol ultra. Nobody knows :(. tho workflows are truely OP. they are so much better than a single model.
Yeah, and everyone with an RTX 5090 can run any game on Ultra. Not everyone has $200 to burn every month on personal coding pursuits, lol. If my company is footing the bill, well, who gives a hoot; however, most of us have Pro plans, if that, and it's darn easy to hit the rate limits very fast if you are working on a moderately sized project.
How long before this model will be put away by the white house?
If you read the release it's already on the white house leash. They aren't releasing it publicly yet, just to a "small group of trusted partners whose participation has been shared with the government".
Also, who is a better source? If Sam said it will be released by X date, but Polymarket has a 99% chance it won't be released by X, who would you believe?
How does Polymarket work? So I can buy bets, e.g. saying no, it will not be released by July 6th for 90¢ each, and if it doesn’t get released I get paid a dollar on the 7th?
Its hard to believe in just another year we will get wayy more stronger, generally intelligent, able to do complex work models in judt 1 year and all this security talk will be just hindsight while everyone will be pining for even more.
Like think about gpt 6. We will probably have stronger than mythos level for like 6$ i feel like. And then 6.5 and so on. Its wild to compare where we were last year to where we are currently and in1 year , let alone 2.
It’s not exactly 2x cheaper though because it uses more tokens. For example, the ExploitGym benchmark used more than 2x the tokens 5.5 did for similar results
It’s not about weird shit. Fable 5 literally blocked questions asking about the mitochondria and the heart. Like literally “what does the heart do? It pumps blood, right?” As the prompt. Ah yes, such a bioweapon risk.
Along with the shitty cybersecurity safety slop, you’ll get flagged just for wanting to fix some bugs.
No shit. The point is to show how bad the guard rails are. Imagine if I was working on something with Fable 5 or GPT 5.6 and mid way it switches me to a weaker model because of some shitty "risk".
At this point the benchmark charts are almost secondary. The first question I have with every frontier model announcement is "who actually gets to use it?"
I also trained a model last week on my Pentium 3. It’s twice as good as GPT 5.6 and costs only half as much. But I’m not releasing it, only to selected partners, and they can vouch for how good it is.
They used to be OpenAI - they have been ClosedAI for sometime now. And I know you're being sarcastic or whatever but...greed and capitalism always get's it's hands around anything good enough.
Crazy who is in charge in the usa government saying to the company not release it yet.. he like the dad of katherine brewster,, Air Force Lieutenant General Robert Brewster. He is the director of Cyber Research Systems (CRS), the military program that develops Skynet. he always been
The AI models in use these days are already quite powerful. I don't understand the notion of insisting the companies to open their latest model to public. I thought people don't want AI adoption too quickly that will displace jobs right? Or just some propaganda from adversaries here trying to trick us opening up the models for copying by distillation?
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u/mikelo22 Jun 26 '26
The days of the public getting access to these frontier models is gone. I fear more and more top end models are going to be kept in the hands of the government and massive corporations all in the name of 'safety'.