Reasoning effort is a request-level dial that tells a reasoning model how much thinking to spend before answering. Lower settings return faster and cheaper responses; higher settings let the model work through a problem more thoroughly, at more latency and more tokens.
How does the reasoning effort dial actually behave?
The accepted values differ by provider, and some expose a token budget for thinking instead of a named level, so treat the specific names as implementation detail and read the current API reference. What generalizes is the shape of the trade: effort buys accuracy on hard problems and buys nothing on easy ones. It has also displaced the older sampling controls as the primary way to steer a reasoning model, since some of them no longer honor temperature at all.
Why is reasoning effort a cost decision?
Every level up increases thinking tokens on every call, which makes this one of the largest cost levers in an agent, alongside choosing the model itself. Set it per task rather than per application, measure whether the higher setting changes the outcome, and let a model routing layer pick both model and effort from the difficulty of the request. Where a response has a user waiting, the setting is also bounded by your latency budget, and the aggregate belongs in AI FinOps reporting.
Frequently asked questions
Is reasoning effort the same as temperature?
No. Temperature shapes how a model samples among likely next tokens. Reasoning effort controls how much internal work it does before producing an answer at all. On some reasoning models the sampling parameters are deprecated or ignored, leaving effort as the main steering control.
Should we just set it high everywhere?
No, because you pay for it in both latency and tokens on every call. The gain concentrates in genuinely hard tasks. Classification, extraction and routine drafting usually show no measurable improvement, which makes effort a per-task setting rather than a global default.
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