One Model Does Not Fit Everything
The best model for a task depends on the task. Using a giant reasoning model for a one-line classification wastes money and adds latency, while a small fast model will struggle with complex structured extraction. Choosing well needs more than reading benchmarks; it needs your own evaluation.
Build a Golden Test Set
A golden test set is a few dozen realistic inputs with known-good answers, drawn from the actual traffic your app will see. Include the tricky cases: ambiguous phrasing, edge inputs, and examples where other models have failed before. Twenty-five well-chosen cases beat two hundred generic ones.
Score for the Behavior You Care About
Define what “good” means for your task. For extraction, score exact field matches. For classification, measure accuracy on the labels you actually use. For chat, use a rubric for helpfulness and a separate check for formatting compliance. A model that scores well on one axis may fail the others.
Route Different Tasks to Different Tiers
Run the golden set against the cheap model first. If it passes, ship it. If it fails, escalate only the failing cases to the stronger model. This routing pattern is how production systems keep cost under control without sacrificing quality where it counts.
Revisit Quarterly
Model lineups change fast. A model that was mid-tier last quarter may now beat the premium option on your set. Re-run the same evaluation every quarter and let the data, not the marketing, decide which tier each task routes to.
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