temperature

Temperature is a decoding parameter that rescales the model’s logits before softmax, thereby controlling how deterministic or diverse the output is.

Lowering temperature sharpens the distribution, pushing probability mass toward the top tokens and approaching greedy decoding in the limit. Raising temperature flattens the distribution, allowing more exploration and novelty at the cost of coherence or correctness. Even the low end doesn’t guarantee reproducibility, though. A temperature of 0.0 still won’t make results fully deterministic.

Temperature is implemented by dividing logits by the temperature constant before softmax, and it is often used together with top-k or nucleus (top-p) sampling. In practice, one chooses temperature per task: low for factual or precise generation, and higher for creative or open-ended generation.

Not every model exposes the parameter. Some current reasoning models reject non-default temperature values with an error, and some vendors have deprecated the knob outright. Anthropic’s Messages API, for example, marks temperature as deprecated and rejects any value other than 1.0 on models released after Claude Opus 4.6. Where the parameter is unavailable, prompting or a reasoning-effort setting steers the output instead.

Related Resources

Course

Leverage OpenAI's API in Your Python Projects

Learn how to use the ChatGPT API with Python's openai library to send prompts, control AI behavior with roles, and get structured outputs.

intermediate ai api

For additional information on related topics, take a look at the following resources:

• Prompt Engineering: A Practical Example (Tutorial)
• Practical Prompt Engineering (Quiz)