Reasoning models

For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

Reasoning models use internal reasoning tokens before producing a response. This helps the model plan, use tools effectively, inspect alternatives, recover from ambiguity, and solve harder multi-step tasks. Reasoning models work especially well for complex problem solving, coding, scientific reasoning, and multi-step agentic workflows. They're also the best models for Codex CLI, our lightweight coding agent.

Start with gpt-6-astra for most reasoning workloads. For lower cost, consider gpt-5.6-terra, or gpt-5.6-luna for the lowest cost and latency. If you're using a GPT-5.6 model, see reasoning mode for its pro option.

Reasoning models work better with the Responses API. While the Chat Completions API is still supported, you'll get improved model intelligence and performance by using Responses.

本篇目录

• Get started with reasoning
• Reasoning effort
• Reasoning mode
• How reasoning works
• Controlling costs
• Preserve reasoning across calls
• Change reasoning mid-conversation
• Reasoning summaries
• phase parameter
• Advice on prompting
• Use case examples