Temperature

An LLM writes one token at a time, and at every step it has a list of possible next tokens, each with a probability. Temperature is a number you send with your request that changes how the model picks from that list. The name is borrowed from physics, where heat makes particles move more randomly.

At a low temperature, close to 0, the model almost always takes the most likely option. Answers become focused, repetitive and very similar from one run to the next. At a higher temperature the less likely options get a real chance, so the text becomes more varied and surprising, and past a point also more rambling and error-prone. The range depends on the provider: Anthropic's API accepts 0 to 1, OpenAI's 0 to 2.

In practice: keep it low for tasks with one right answer, like pulling data out of an invoice, classifying a message or writing code; raise it for brainstorming, names or fiction where you want options. Even at 0 the output is not guaranteed to be identical every time, and some newer reasoning models fix the temperature and do not let you change it. Temperature is often combined with top-p, which cuts off the long tail of unlikely tokens.