I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration. > In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a s…
Seems like they will do Sol first while capacity constrained? I can't imagine the margins they'll be charging
Whoa. This looks both powerful and expensive. My prediction is that, this time next year, top developers outside ai labs will be spending 50k USD+ on inference. Within labs, I've heard spend is already far beyond this per developer.
not sure how "top developers" are defined here, but there is huge diminishing return curve starts kicking in after $200/month price point for typical eng work.
Unless I have read over it, besides the animation in the intelligence vs speed graph which only mentions internal data and not whether they truly reran the AA suite, there is no actually solid statement on the important aspect of performance. Neither the Cerebras or OpenAI post [0] outright state that this performs exactly the same as regular 5.6 Sol. I feel if this was 1:1 just Sol but much faster, they'd (rightfull…
"delivering up to 750 output tokens per second and without any quality compromise" seems pretty definitive.
This just means, we once saw it run at 750 tokens per second, in this insane test config :)
I'd just like to point out that the largest model Cerebras has ever served is Kimi K2.6 which is 1T parameters, so that either means that theyve had a breakthrough on the hardware engineering side of things, or GPT-5.6 Sol is likely a lot smaller than people think. If it truly is only ~1-2T parameters, then this kinda kills 2 narratives for me. 1. all the handwringing about open source catching up via Kimi K3 (3T par…
Would be extremely interesting if some of the closed models would be that small. Means maybe in future they could run locally.
I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration. > In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a s…
Answering 2,500 independent questions is an embarrassingly parallel workload, all it needs is scale out. It would be more meaningful to know how much time was required for a single complete answer to a difficult HLE question.
No. This conveys a fundamental misunderstanding of how anything pertaining to programming works. This will never happen, ever . For example, take... printf("Hello, world"); vs. a plausible illustration of how it might be compiled down to machine code... 48 65 6C 6C 6F 2C 20 77 6F 72 6C 64 48 83 EC 28 48 8D 0D F5 0F 00 00 E8 F0 00 00 00 33 C0 48 83 C4 28 C3 The latter now takes up 10x as many tokens (= 10x the cost/ti…
I mean you're right, but what about a different tokenizer and some guardrails... trained on compiled code... I just learned that gpt-5.6 will talk to you in base64 without tool calls so maybe it's possible.
Still, what'd be the advantage? There is a lot, a massive delta, a chasm still to improve before there is even a theoretical potential re. performance. GPT-5.6-Sol still writes JS in inefficient and roundabout ways that leave a lot on the table and even if we got to a point were models provide the highest performance output possible, there are hundreds of languages far more performant before machine code should be considered. Rust, C, Assembly if need be (at which point performance vs binary is, unless I am mistaken, pretty much moot). All machine code would gain us is lock in and no auditability.
> Compared with output speeds reported by Artificial Analysis GPT-5.6 Sol on Ultrafast mode runs 11x faster than Fable 5, and 5x faster than Opus 4.8 on Fast mode. Awesome work. I'm personally very excited for faster models/inference. I think speed is underrated to some degree in the current conversation. For a while, I was using Cursor's Composer quite a lot, even over frontier models, just because of how darn fast…
What do you need speed for? That's a genuine question, I feel like the limiting factor already is my creativity, attention span and budget. And I'm not even yet optimizing cost by batching things like review to slow local models over night, or schedule tasks to take full advantage of my subscriptions.
Fast mode is already 1.5 times faster and 2x more expensive in the Codex subscription plan. If this thing is 14 times faster, then I can imagine running out of my quota in one session.
There is zero chance this will be offered to subscription users.
Can anyone explain why Cerberus needs to be _fast_ instead of _cheap_? I don't think I understand why they aren't leveraging the increased speed to do batching to serve more customers at a "normal" tok/s. Is the limitation, even on cerberus, still that the cache can only serve so many concurrent sessions over time? Is there no scaling advantage? I genuinely do not understand how any of this works.
There are many industries and situations that will pay absurdly high multiples on API rates for low latency access. Anything remotely related to finance, deal flow, or trading, including the development and checking of Excel financial models. Live analytics that can be delivered mid-meeting to C-suite executives, or ultra-luxe dealmakers who need answers while on a live call. Intelligence and defense, where milliseco…
I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration. > In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a s…
Answering 2,500 independent questions is an embarrassingly parallel workload, all it needs is scale out. It would be more meaningful to know how much time was required for a single complete answer to a difficult HLE question.
The animation in the article shows GPT-5.6 Sol Ultrafast solving a question at 3 secs vs Fable's 27 secs. The "11 hour vs 78 hours" animation implied that they were solved in sequence and not in parallel