Reasoning models: when the premium is worth paying
More expensive and slower, and still profitable in one specific case: when the downstream human error costs hundreds of dollars.
Reasoning models cost significantly more and introduce additional latency into the response path. On simple classification tasks they add nothing whatsoever that justifies either the cost penalty or the latency cost. Standard models perform identically on those narrow problems.
The business case for the premium emerges only in narrow, high-stakes domains where errors have expensive downstream consequences. Contract review and analysis. Reconciliation across multiple conflicting data sources. Complex rule application in regulatory or compliance contexts where decisions require justification.
On those high-stakes tasks, reasoning models halved the number of cases escalated to human specialists for review. The cost per individual decision rose but the total cost per error prevented fell dramatically because specialist time is extremely expensive.
The financial math is straightforward: compare the direct cost of one reasoning model call against the total cost of downstream specialist time if that decision proves wrong. If wrong answers cost more than the premium call, then pay for reasoning.
For routine classification and straightforward entity extraction, the fast standard model wins economically. For anything requiring reasoning justification or reconciliation of contradictory information sources, run the reasoning model first.
We shipped both in the same system in production: reasoning models for the difficult cases, standard models for handling volume and routine tasks.