Run your own bake-off before you pick a production model
Public leaderboards will not tell you which model should run your highest-volume prompt. I'd start with one narrow step you can score, a labelled set built from real production misses, and a side-by-side run against a frontier model plus one small language model candidate. Only after that bake-off would I commit to post-training or knowledge distillation . Which task is worth a bake-off? Pick a…
To determine which model should handle the highest-volume prompt, run a narrow bake-off using a labelled set built from real production misses. Compare a frontier model and a small language model candidate side-by-side. Isolate high-volume ancillary work like query summarization and canonicalization for testing. If the right answer format is stable, choose that candidate for the bake-off.
Do not test open-ended reasoning or multi-step workflows with a small model. If the bake-off shows the small model is close enough, replace the costly frontier setup with it.
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