Plain-language explainer
Temperature, explained
What does the temperature setting actually do to an AI model?
Temperature reshapes the model's next-word probabilities before it picks one. Low temperature sharpens them, so the top choice wins almost every time and answers come out consistent. High temperature flattens them, so second and third choices get real chances, which reads as variety or creativity, and sometimes as nonsense. It changes nothing about what the model knows. It only changes how much risk the model takes when choosing among words it already considers plausible.
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What people get wrong
- Temperature makes the model more creative, like a mood. It is arithmetic on probabilities, nothing more.
- Temperature 0 means the answer is correct. It means the answer is consistent. A confidently wrong top pick stays wrong at every temperature.
- High temperature unlocks hidden knowledge. Same knowledge, riskier picks among the words the model already had in mind.
Where you see it in real products
- API playgrounds expose the dial directly, usually from 0 to 2.
- Writing tools' precise and creative modes are largely this one setting.
- The regenerate button gives a different answer because sampling rolls the dice again.
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