How to Prompt Reasoning Models

“Reasoning model” is a product and research category, not one universal prompt specification. For its API reasoning models, OpenAI says to keep prompts simple and direct, avoid chain-of-thought requests such as “think step by step,” state constraints explicitly, and be specific about the end goal. That advice does not automatically apply to every model family, older checkpoint, or interface. Read the current guide for the exact model and API before reusing prompt recipes written for a different system. A practical prompt names the task, relevant context, boundaries, and what a successful response should contain. If format is important, specify it directly; a short example can help when the desired output shape is hard to describe. OpenAI suggests trying zero-shot instructions first and adding few-shot examples when complex output requirements justify them. Keep examples consistent with the stated instructions because contradictory examples can hurt results. Avoid assuming that a longer step list creates better reasoning: procedural details can narrow a model’s approach or distract from the actual objective. Reasoning effort, visibility of intermediate work, and support for tool or conversation state differ by provider and model. Do not claim that every reasoning model exposes a hidden chain, follows the same training recipe, or accepts one common “reasoning budget” control. For OpenAI API reasoning models, consult the current reasoning guide for model-specific behavior and use documented controls where available. Evaluate prompts against representative tasks and success criteria; an explanation or confident answer does not establish correctness. For consequential analysis, check claims against source material and qualified judgment.