> How these models perform in those tasks is paramount,
Then you have no choice but to use your own hosted model because that's the only way you can be sure the model isn't going to be removed or quantized/nerfed behind your back.
If your system can widstand the fluctuations of closed models over time, then it can most likely support switching between multiple providers in the first place.
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.