Re:Closed AI models dominate the price-performance frontier
by rbehrends on Friday November 06, 2026 @02:45AM
(#50389359)
While the distillation argument is brought up again and again, I find it very dubious. This is not to say that it doesn't happen, but I have my doubts that it is a limitation. DeepSeek V4.1 Flash, for example, can reason itself through some pretty complex mathematical problems. Definitely not millenium-prize level problems and you'll still prefer frontier models for research math, but it can handle pretty much anything you'd encounter in day-to-day non-reasearch work [1, 2].
Where it differs from the larger models is that it has less knowledge; respectably as it performs for its size, 552B parameters are still a lot less than the multiple trillions that frontier models have. Sol/Astra/Opus/Fable may know the solution to the problem directly or to a very adjacent problem, while DeepSeek has to work its way through it from first principles and then sometimes fails (because LLM "reasoning" is really a heuristic-guided search). But this is also a failure mode that I have seen with Luna.
Where it differs from the larger models is that it has less knowledge; respectably as it performs for its size, 552B parameters are still a lot less than the multiple trillions that frontier models have. Sol/Astra/Opus/Fable may know the solution to the problem directly or to a very adjacent problem, while DeepSeek has to work its way through it from first principles and then sometimes fails (because LLM "reasoning" is really a heuristic-guided search). But this is also a failure mode that I have seen with Luna.
[1] As evidenced e.g. by ErdosBench. https://www.ulam.ai/erdosbench
[2] Using this as an example over software engineering tasks here, because it tests reasoning capability more than knowledge retrieval.