Just ran a couple of them through GPT 5.5, but this is a single attempt, so take any of this with a grain of salt. I'm on the Plus tier with memory off so each chat should have no memory of any other attempt (same goes for other models too).

It seems to be getting more of the impressive insights that Gemini got and doing so much faster, but I'm having a really hard time getting it to spit out a proper lengthy proof in a single prompt, as it loves its "summaries". For the random matrix theory problems, it also doesn't seem to adhere to the notation used in the documents I give it, which is a bit weird. My general impression at the moment is that it is probably on par with Gemini for the important stuff, and both are a bit better than DeepSeek.

I can't stress how much better these three models are than everything else though (at least in my type of math problems). Claude can't get anything nontrivial on any of the problems within ten (!!) minutes of thinking, so I have to shut it off before I run into usage limits. I have colleagues who love using Claude for tiny lemmas and things, so your mileage may vary, but it seems pretty bad at the hard stuff. Kimi and GLM are so vague as to be useless.

- One problem on using quantum mechanics and C*-algebra techniques for non-Markovian stochastic processes. The interchange between the physics and probability languages often trips the models up, so pretty much everything tends to fail here.

- Three problems in random matrix theory and free probability; these require strong combinatorial skills and a good understanding of novel definitions, requiring multiple papers for context.

- One problem in saddle-point approximation; I've just recently put together a manuscript for this one with a masters student, so it isn't trivial either, but does not require as much insight.

- One problem pertaining to bounds on integral probability metrics for time-series modelling.