Why AI leaderboard rankings are a poor guide for production model selection

Choosing an AI model based solely on public benchmark leaderboards can create a false sense of certainty, as top-ranked models may not suit a product's specific latency, cost, or reliability requirements. Engineers are advised to build a model selection logbook that records key metrics such as token counts, latency, charges, retry behavior, and pass/fail outcomes for each tested model. A small fixed test set of around ten representative prompts is considered sufficient to surface bad assumptions before committing to a model in production. Different product tasks — such as classification, support drafting, code transformation, or agent loops — have distinct operational needs and should not default to the same popular model. The practical recommendation is to run a controlled comparison across a few candidate models on a single product path before making any broader deployment decisions.