Reasoning effort

The reasoning.effort parameter guides the model on how much to think when performing a task.

Supported values are model-dependent and can include none, minimal, low, medium, high, xhigh, and max. Lower effort favors speed and lower token usage, while at higher effort the model thinks more completely to provide higher quality responses. The models also reason adaptively across reasoning efforts, using fewer tokens for simpler tasks and thinking harder for complex tasks.

GPT-6 Astra does not support none reasoning effort. Setting reasoning.effort (Responses) or reasoning_effort (Chat Completions) to none returns HTTP 400.

Use the Responses API for function calling. Chat Completions does not support function calling with GPT-6 Astra.

Defaults are also model-dependent rather than universal. gpt-5.5 defaults to medium reasoning effort. This is the best starting point for gpt-5.5’s full balance of quality, reliability and performance.

Effort	Best for
none	Latency-critical tasks that do not benefit from any reasoning or multi-chained tool calls. For latency-sensitive use cases with gpt-5.5, we recommend trying low to begin with and then moving to none if required. Common use cases include voice, fast information retrieval, and classification.
low	Efficient reasoning with a modest latency increase. Ideal for use cases requiring tool-use, planning, search, or multi-step decision making, while optimizing for speed and cost. Common use cases include data analysis, drafting, execution-oriented coding, and customer support / chat assistant workflows.
medium	When quality and reliability matter, and the task involves planning, complex reasoning, and judgement. Default configuration for most workloads, and a well-balanced point on the pareto curve of latency, performance and cost. Common use cases include agentic coding, research, working with spreadsheets & slides, and delegating long-horizon work.
high	Hard reasoning, complex debugging, deep planning, and high-value tasks where quality and intelligence matters more than latency. Recommended for complex workflows and agentic tasks. Common use cases include agentic coding, long-horizon research, and knowledge work. Depending on the complexity of the task, evaluate both medium and high.
xhigh	Deep research, asynchronous workflows and agentic tasks that require long runs. Only use when your evals show a clear benefit that justifies the extra latency and cost. Common use cases include security and code review, enterprise productivity, deeper research tasks, and challenging coding workflows.
max	Maximum reasoning for your most complex tasks. If you are currently using xhigh, evaluate if max results in stronger performance

For faster time to first visible token in latency-sensitive applications, ask the model to generate a short preamble before continuing with deeper reasoning.

Some models support only a subset of these values, so check the relevant model page before choosing a setting.