Comment by JoshuaDavid

I suspect what's happening is that lots of people have a collection of questions / private evals that they've been testing on every new model, and when a new model comes out it sometimes can answer a question that previous models couldn't. So that selects for questions where the new model is at the edge of its capabilities and probably got lucky. But when you come up with a new question, it's generally going to be on the level of the questions the new model is newly able to solve.

Like I suspect if there was a "new" model which was best-of-256 sampling of gpt-3.5-turbo that too would seem like a really exciting model for the first little bit after it came out, because it could probably solve a lot of problems current top models struggle with (which people would notice immediately) while failing to do lots of things that are a breeze for top models (which would take people a little bit to notice).