Reasoning effort

Low, High, and Max are settings, not guarantees

Reasoning effort is useful only when its model and route stay attached. The label is not a portable unit of intelligence across providers.

Short answer

Compare effort levels inside the same model and route. Move one step at a time, keep the comparison standard fixed, and stop increasing effort when quality no longer improves enough to justify the extra time or cost.

The labels are not standardized across providers

One API exposes an explicit reasoning-effort field. Another may encode a tier such as Max in the model ID while returning a default effort value. Those two representations should not be treated as the same control.

This is why ModelDial keeps the raw model identity, normalized display name, effort, and route in the configuration record instead of flattening them into one brand row.

Test the effort curve within one configuration family

Hold the model, route, and comparison standard fixed. Compare Low with Medium, then Medium with High. If the route changes at the same time, you cannot tell whether the result came from reasoning effort or transport behavior.

  • Check total score and the five-question profile; a flat total can hide a changed weakness.
  • Compare successful elapsed time, not only time to first token.
  • Use repeated comparable batches before treating a small gap as stable.

Higher effort can lose on the decision you care about

A higher setting may take longer without improving the relevant question, and ordinary model variance can still reorder adjacent efforts in one batch. Higher effort is therefore a hypothesis to test, not an automatic upgrade.

For a main agent, keep the lowest effort that consistently clears the quality floor. For bounded worker tasks, measure the task-specific floor separately instead of inheriting the main agent setting.

Record the exact setting you can reproduce

Save the provider, raw model ID, effort, service tier, route type, and evaluation time. A label such as High is not enough to reproduce a result after an endpoint or provider configuration changes.