Benchmarks on narrow tasks, and what the cost difference looks like at production volume.
For a narrow, repetitive task, the question is not which model is smartest. It is which is good enough at the required quality for the lowest cost and latency at your volume.
Narrow tasks close the gap
On a well-defined task with a good training set, such as classification, extraction or formatting to a fixed schema, a small tuned model frequently matches a frontier model closely. The frontier model's advantage lies in generality and reasoning, neither of which the task requires.
The economics at volume
The difference is not marginal. At millions of requests a month, a small model can cost an order of magnitude less to run and respond several times faster, which changes what is feasible in a live interface. At a thousand requests a month, none of that matters and the frontier model is simply easier.
The hidden costs of tuning
Dataset creation, evaluation infrastructure, hosting, monitoring and periodic retraining as your data shifts. These are ongoing and real, and they are why the break-even point is a volume question rather than a technical one.
Prototype with the frontier model, measure quality and cost, then decide whether a tuned model is worth building. Doing it in the other order optimises something that may never ship.
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