Plain-language explainer
Reasoning models, or why some answers take longer
How do reasoning models actually work?
A reasoning model writes out a long private working-out before it answers. That draft is generated the same way as any other text, one token at a time, but it gives the model somewhere to break a problem into steps, try an approach and correct itself. Spending more tokens at answer time reliably improves hard maths, code and multi-step logic. It costs more and takes longer, and on simple questions it buys nothing.
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Then go deeper: The model thinks before it answers Locked
What people get wrong
- The model is thinking in some new way. It is generating tokens exactly as before, just generating far more of them before it commits to an answer.
- The visible summary is the real reasoning. Most products show a cleaned-up sketch, not the raw trace, so it is a rough guide and not an audit trail.
- Reasoning mode is always better. On lookup and formatting tasks it adds latency and cost for no gain, which is why products let you switch it off.
Where you see it in real products
- The thinking or extended reasoning toggle in a chat assistant.
- Per-answer effort settings in developer APIs, priced by the tokens the model thinks with.
- Coding tools that plan a multi-file change before touching any file.
Common questions
- What is test-time compute?
- Spending more computation when the question is asked rather than only during training. Longer reasoning, several attempts scored against each other, or a search over candidate answers. It is a second way to buy quality that does not require training a bigger model.
- Can I see what the model is thinking?
- Usually only a summary. Labs withhold the raw trace for competitive and safety reasons, and the shown text is a cleaned version. Useful for following the shape of an approach, not reliable as proof of how the answer was reached.
- When should I not use a reasoning model?
- When the task is retrieval, extraction, formatting or short chat. You pay for the extra thinking tokens and wait longer for an answer that a fast model gets right anyway. Save it for problems where a wrong intermediate step ruins the result.
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