Accelerating GPT-5.6 Sol Ultrafast

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 single working day, achieving comparable accuracy nearly 7× faster. This is actually insane. Hopefully the release ultrafast of Terra and Luna too.

People underestimate the importance of speed on quality of thought, because people underestimate just how much quality is a result of simple iteration. When an LLM thinks, it typically just makes one pass. It outputs tokens from top to bottom, beginning to end, and then it's done. But when people think, especially strong thinkers, we typically iterate and revise our thoughts on the fly. We do numerous passes. We stop and restart, we reconsider, we review, we reevaluate. Sometimes we do this so quickly and automatically that we don't even realize we're doing it. I think a lot of what separates a highly intelligent or effective person from others has less to do with the quality of their first pass and more to do with just how many additional passes they're able to do in the same amount of time, and of course what kind of criteria they're habituated to consider during their review passes. Introspecting about this is difficult, but experimenting with LLMs is easy. First, simply ask an LLM to do something complex. For example, to come up with a new business idea, or to plan the next month of your life, etc. After it finishes, tell it: "Review what you just wrote, according to some appropriate list of evaluation criteria that you come up with first. And then, based on the results, iterate and generate a better response if warranted." It's insane how much better the next answer will usually to be. Often it'll catch and erase tons of hallucinations, logical errors, and inefficiencies. And you can simply copy-paste this again and again until you begin to hit diminishing returns. Or, in a harness like Claude Code, for example, I might shortcut this whole process by saying, "Use sub-agents to iteratively review and iterate on your work until convergence." The reason why most people don't prompt LLMs to do this (besides simply not thinking of it) is that it takes time. But what if it didn't? What if the LLM's response came back in milliseconds rather than minutes? Then there would be almost no reason NOT to do this. In fact, one could almost imagine it baked into the assistant/harness -- a massive step change in practical quality, enabled by nothing more than speed.

The corresponding OpenAI post https://openai.com/index/previewing-ultrafast/ There is no pricing info, which could mean it's "if you have to ask..." territory or they are simply gauging interest before deciding

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 (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/

> 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 it was.