> Of course, model size doesn't capture all variation. Models that are trained later might have new data or new techniques or new architectures that earlier models lack. You can see this with models from all providers - Sonnet 5.5 outperforms Fable 5.1 in ways, and Gemini 3.8 Flash outperforms 3.1 Pro in ways.

Is this really so/could you give a rough example please? I've not used a lot of Claude or ChatGPT, but 3.1 Pro always feels most grounded and intelligent. 3.8 Flash (in my very limited testing) isn't bad, but it feels less nuanced.

(Fwiw I don't use it for code, but ask maths and real world logic based questions.)