OpenAI's Reasoning Models Guide: When & How
Reasoning models (o1/o3) changed prompting rules — no more 'think step by step'. A guide to reasoning effort, structured outputs, and cost control.
Open the official tutorialWhy 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.