What temperature should I actually use?

Near zero for anything with a right answer: extraction, classification, code, structured output. Around 0.7 to 1.0 for brainstorming and drafting where variety helps. If you are unsure, start low, since you can always ask again for more options.

Does temperature 0 guarantee the same answer every time?

It makes it very likely, not guaranteed. Providers batch requests and the arithmetic can differ slightly between runs, so answers can still vary. Treat repeatability as a strong tendency rather than a contract.

What is the difference between temperature and top-p?

Temperature reshapes the whole distribution. Top-p first throws away the long tail, keeping only the most likely options that add up to p, then samples from what is left. They are often used together, and changing both at once makes results hard to reason about.

Why do I get different answers to the same prompt?

Because the final step is a random draw from the model's next-token probabilities, and at any temperature above zero the draw can land on a different token each time. Setting temperature to zero makes the top token win every time, which makes answers repeatable but not more correct.

Is temperature the same across providers?

The formula is the same, dividing the scores by the temperature before softmax, but the useful range differs by model and the defaults differ by product. Most APIs accept 0 to 2 and default near 1 or 0.7. Treat the number as a dial to tune per task, not a universal constant.

Plain-language explainer

Temperature, explained

Temperature is a sampling setting that reshapes a language model's next-token probabilities before one is chosen. Low temperature sharpens the distribution so the top choice almost always wins, giving consistent answers. High temperature flattens it so less likely tokens get real chances, giving variety and more errors. It never changes what the model knows.

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.

Last reviewed September 6, 2026

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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.

Common questions

What temperature should I actually use?: Near zero for anything with a right answer: extraction, classification, code, structured output. Around 0.7 to 1.0 for brainstorming and drafting where variety helps. If you are unsure, start low, since you can always ask again for more options.
Does temperature 0 guarantee the same answer every time?: It makes it very likely, not guaranteed. Providers batch requests and the arithmetic can differ slightly between runs, so answers can still vary. Treat repeatability as a strong tendency rather than a contract.
What is the difference between temperature and top-p?: Temperature reshapes the whole distribution. Top-p first throws away the long tail, keeping only the most likely options that add up to p, then samples from what is left. They are often used together, and changing both at once makes results hard to reason about.
Why do I get different answers to the same prompt?: Because the final step is a random draw from the model's next-token probabilities, and at any temperature above zero the draw can land on a different token each time. Setting temperature to zero makes the top token win every time, which makes answers repeatable but not more correct.
Is temperature the same across providers?: The formula is the same, dividing the scores by the temperature before softmax, but the useful range differs by model and the defaults differ by product. Most APIs accept 0 to 2 and default near 1 or 0.7. Treat the number as a dial to tune per task, not a universal constant.

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