OpenAI's Reasoning Models Guide: When & How

Why reasoning models changed the game

OpenAI's o1/o3 series reason internally before answering. The official guide makes a counterintuitive point: you no longer need to prompt 'think step by step' — the model does it. The real levers are reasoning effort and knowing when to use a reasoning model at all.

Core concepts

Reasoning effort: low (fast, simple tasks), medium, high (deep reasoning). Set per task type — classification and rewriting at low; hard code and math at high.

Structured outputs & tools: reasoning models pair well with JSON output and function calling for plan-then-execute tasks. Reasoning tokens cost money; long reasoning is expensive.

When not to use: simple Q&A, translation, formatting — regular models are faster and cheaper.

Checklist

• Set reasoning effort per task type.
• Validate the accuracy-vs-budget knee with small samples.
• Combine reasoning with structured outputs so downstream systems can consume results.

Official source: OpenAI Reasoning Models guide.