OpenAI Reasoning Effort
reasoning.effort is an OpenAI API setting for how much model work a request should use before answering. Supported levels and defaults vary by model, and higher effort does not promise a correct answer.
reasoning.effort → eval + latency + costsame OpenAI model · same promptA coding app tests three supported levels on 100 saved tasks. It compares pass rate, wait time, tokens, and cost.faster · lower passbest measured fitslower · no gainOpenAI model + reasoning.effort → eval, latency, and costOPSAnother example
A coding app tests three supported levels on 100 saved tasks. It compares pass rate, wait time, tokens, and cost.
Main parts
- 01Supported OpenAI model
- 02Allowed effort values
- 03Request setting
- 04Reasoning usage
- 05Latency and cost
- 06Evaluation result
Use it when
Use it only with an OpenAI model that documents the setting. Choose a supported level with evals, latency goals, and cost limits.
Do not use it when
Do not copy the setting to another provider or unsupported model. Do not use it as proof of quality.
Name used in code
OpenAI model + reasoning.effort → eval, latency, and cost Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate OpenAI Reasoning Effort to make AI behavior measurable and tied to evidence. User context: A coding app tests three supported levels on 100 saved tasks. It compares pass rate, wait time, tokens, and cost. AI method or concept: OpenAI Reasoning Effort. Why it fits: Use it only with an OpenAI model that documents the setting. Choose a supported level with evals, latency goals, and cost limits. Do not use it when: Do not copy the setting to another provider or unsupported model. Do not use it as proof of quality. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of OpenAI Reasoning Effort. Definition: reasoning.effort is an OpenAI API setting for how much model work a request should use before answering. Supported levels and defaults vary by model, and higher effort does not promise a correct answer. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.