Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
It goes beyond the problems introduced by the model router, though. I have to work with GPT 5.2 for my job (along with Claude, Gemini, and a few others), and we have enterprise API access to it. So when I select GPT 5.2 as the model to use, it’s spending tokens to actually use it.
And it’s pretty bad. It’s noticeably worse than the 4.x series. I find myself having to fix its mistakes far more often.
I’ve struggled to reason out an explanation, and model collapse really seems like a contender, especially if you follow information theory and why training these things is so hard.
As it happens, there’s a new talk about exactly this from George D. Montañez. You might find it interesting: https://youtu.be/ShusuVq32hc
OK that’s a fair observation. Honestly my naive guess would be that they simply do not optimize mainline gpt models for the kind of use case you generally have on Api (tool use, multi-step actions, etc…). They need it to be a perky every day assistant not necessarily a reliable worker. Already on gpt-4 i found it extremely mediocre compared to the Claude models of the same time.
I think that’s a more likely explanation than model collapse which is a really drastic phenomenon. A collapsed model will not just fail tasks at a higher rate, it will spit garbled text and go completely off the rails, which would be way more noticeable. It would also be weird that Claude models keep getting better and better while they’re probably fed roughly the same diet of synthetic data.