Reasoning Models in Production: When They Help and When They Hurt
· 9 min read
A team building a support triage system switched their classification pipeline from GPT-4o to o3. Accuracy improved by 2%. Costs went up by 900%. The latency jumped from 400ms to 12 seconds. They switched back.
This is the most common story in production AI right now. Reasoning models represent a genuine capability leap — o3 solved 25% of problems on the Frontier Math benchmark when no previous model had exceeded 2%. But that capability comes with a cost and latency profile that makes them wrong for the majority of tasks in the average production system. Knowing the difference is one of the more valuable things an AI engineer can internalize right now.
