[Grimblewald]

My experience exactly. The more "real" the problems become, the more other models become unsuitable when compared to claude, with the sole exceptions being deepseek/kimi, which while speaking strictly w.r.t metrics and basic tasks are not better, they are more interesting and handle more odd and totally out of domain stuff better than the US models. An example being code i wrote for a hypercomplex sedenion based artififial neural network broke claude so bad it start saying it is chatgpt and cant evaluate/run code. similar experience for all US models, which are characterized by being extremely brittle at the fringes, though cladue least among them. Meanwhile chinese models are less capable for cookie cutter stuff but keep swinging when things get really weird and unusual. It's like US models optimize for the lowest minima acheivable, and god help you if distribution changes. Chinese models on the otgerhand seem to optimize for the flattest minima, giving poorer quality across the board but far more robust behaviour.