I should be more clear. The "performance" I'm talking about is not entirely objective in these use-cases, and the bigger part is something you can't reliably measure through tests or evals. The value proposition of the frontier labs is their post-training taste, how reliably the model handles ambiguity and infers intent, while at the same time progressing at the leading edge. Self-hosting trades that for output stability, and absorbing minor drift within one provider is still far easier than switching labs when your workflow and UX are tuned around a specific model's quirks.