Nope, strong disagree. The model is one small part of the process harness; behaviors are usually routable with expected propensities. Unexpected model changes avoiding change management messes with monitoring and observability thresholds. Stochastic controls are a real thing when you have your distributions defined; your workflows on a new model will throw that expected prior out the window.

I think it depends on the workload, which put you in the right (in some case, it's dangerous to do that), but for me even for very clean always correct path I always assume a model can, at time, have a vector brain fart (because it keeps happening).

Also in principle it's similar to Anthropic downgrading.

Personally I use the basis that if I don't self host (I include remote host, but that I pay per hosting nor per model or api), it can change behavior without me asking. But they shouldn't, but it doesn't matter that's what they do.