---
title: Temperature
description: 'A decoding setting that rescales token logits before sampling, making the next-token distribution sharper or flatter.'
canonical_url: 'https://darkfactory.dev/glossary/temperature'
markdown_url: 'https://darkfactory.dev/glossary/temperature.md'
collection: glossary
date_published: '2026-08-03T00:00:00-04:00'
date_modified: '2026-08-26T00:00:00-04:00'
---

# Temperature


## Definition

Temperature rescales a model's next-token logits before they are converted to probabilities. In the common formulation, each logit is divided by a positive temperature `T` before softmax. `T = 1` leaves the distribution unchanged, values below 1 concentrate probability on the leading candidates, and values above 1 flatten the distribution so lower-ranked candidates are more likely to be sampled.

In common generation APIs, temperature is a sampling control. Positive temperatures do not change which token a simple greedy decoder ranks first, although another search procedure may use the rescaled scores. A low value usually reduces variation when sampling, but it does not make the model more knowledgeable or guarantee repeatable output. Provider behavior at exactly zero also varies: some APIs treat it as greedy decoding, while others approximate the limit or apply additional nondeterministic serving behavior.

Use temperature as an experimental control, not as a dial labeled "creativity." Record the model version, seed support, top-p setting, and other decoding parameters when comparing runs. Changing several sampling controls at once makes the result hard to interpret.

## Distinguish it from nearby terms

- Top-p changes which candidates remain eligible by cumulative probability mass. Temperature changes their relative probabilities.
- Calibration temperature scaling is a post-training method for making reported confidence better match observed outcomes. It is not the same operation as choosing a decoding temperature for generated text.
- Greedy decoding selects the highest-probability next token instead of sampling. It can still vary across model versions or serving infrastructure.

## Check your understanding

Two model configurations differ in temperature, top-p, and model version. Which variables would you hold fixed before attributing an output change to temperature?

## Related factory areas

- [Model selection, routing & budgets](https://darkfactory.dev/factory/model-routing-budgets)

## Evidence and further reading

- [Google Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)
- [Hugging Face: Generation strategies](https://huggingface.co/docs/transformers/generation_strategies)
- [On Calibration of Modern Neural Networks](https://arxiv.org/abs/1706.04599)