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Discussion (43 Comments)Read Original on HackerNews
> 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 single working day, achieving comparable accuracy nearly 7× faster.
This is actually insane.
Hopefully the release ultrafast of Terra and Luna too.
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 (rightfully) scream that off the rooftops. A line such as "this is the same performance, just faster, with no downsides" would go a long way in clarity and communication. Along with no pricing information, I'll hold out on further information.
[0] https://openai.com/index/previewing-ultrafast/
Same for massive performance differences in the way providers like Cerebras, Groq, etc. have deployed models including K2.6 on Cereberas specifically. Massive deltas in tool call and overall quality despite there being far more clarity in open weight vs proprietary model deployment.
That's why anything other than asserting full parity makes me question their phrasing. It "performs the same" is very different to "no compromise/degradation", the later allowing for a lot of wiggle room in what evals you use to assess that, the former meaning identical in all situations.
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 it was.
IME waiting for an agent to work through a problem is a detriment to attention span; your mind drifts to other things while you wait. Maybe you can steer several agents in a round robin instead, but then there's a cognitive tax from context switching. Faster models mean fewer gaps in focus.
It also spent almost 800k tokens on these lines…
There is no pricing info, which could mean it's "if you have to ask..." territory or they are simply gauging interest before deciding
Who knows if they will subsidizes it to mitigate sticker shock, but it's a safe assumption that it will be scarily expensive. However if you are in a "cost is no obstacle, speed is god" position, it will likely be pure magic.
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.
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.
If someone subsidize maybe, but if the companies need to pay no way, unless there is hard evidence of the return.
Given sufficient budget and scope, I could certainly productively burn a half million dollars in tokens a year or more. I think that's where we're headed anyway, buying a 2nd or 5th claude max subscription feels slightly excessive for personal usage, but at a corporate level...
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 params) is complete nonsense. All that matters imo for determining which labs are leading is intelligence per parameter. Anyone with a enough compute can train a giant model, but being able to squeeze capabilities into smaller models gives you a massive inference and training edge.
2. Inference margins are clearly insane, and this explains why OpenAI was able to lower the price of Luna by 80%. Id guess that thing is probably 120b params based on the TPS they are serving it at.
It is a bit outdated (scores ± 40% lower), but smart enough for a lot of coding tasks, and can cost under 1/10th of Sol.
https://mimo.mi.com/models/en-US/mimo-v2.5-pro-ultraspeed
Curious, what are some of the use cases?
I think it's only a matter of time before miniaturization can have a thumbnail sized user-replaceable accessory that contains the LLM built onto the hardware. I admit I don't know how any of that works, but would be amazing to experience. Fully local, fully offline, ultra fast local inference better than any personal computing product.
Compilation time will be a genuine bottleneck for slop coding if this becomes the standard generation rate over the next few years. Go, Zig or even C99 with TCC for dev builds, any language that can get you systems-level performance (or close to it) in a dev environment where you can iterate in ms rather than minutes is going to be immensely more appealing than generating a potential prototype in 10 seconds and waiting 15 minutes for it to compile.