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.

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.

Last reviewed July 25, 2026

Reading is the slow way. Start with a free lesson you can operate right now.

Start free: Predict the next word →

Free, no code, no signup.

Then go deeper: Tuning the model's creativity Locked

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.

Related explainers

Softmax Hallucination How LLMs work

One idea at a time, in your inbox

New lessons and explainers, written the way these pages are. Now and then, not daily, and never a sales sequence.

We email you a confirmation link first. Unsubscribe in one click, any time. Privacy.

Part of See How AI Works, a free interactive course, where you learn how modern AI works by operating it, not watching videos.