What Is LLM Temperature?
What Is LLM Temperature?
Definition: LLM temperature is a setting that controls how random a language model’s word choices are. Low values make output more predictable and high values make it more varied. Values usually run from 0 to 1 or 0 to 2, depending on the provider.
Temperature is the answer to “what is temperature in an LLM?” It doesn’t change what the model knows. It changes how adventurous the model is when it picks each next word.
How Does Temperature Work in an LLM?
A language model writes one token (a word or part of a word) at a time. At each step it scores every possible next token and turns those scores into probabilities. Temperature reshapes those probabilities before the model picks one.
- Low temperature: the likeliest tokens become even more likely, so answers are more consistent and repeatable.
- High temperature: less likely tokens get a bigger chance, so answers vary more from run to run.
Temperature is often called a “creativity” setting, but research suggests the link is weak. A more accurate view is that it controls how closely the model sticks to its most probable patterns.
What Is the Difference Between Temperature and Top-p?
Temperature reshapes the probabilities so the model picks more or less adventurously. Top-p (nucleus sampling) cuts off the long tail: the model only chooses from the smallest set of likeliest tokens whose probabilities add up to p.
| Temperature | Top-p | |
|---|---|---|
| What it does | Sharpens or flattens the probability distribution | Limits the pool of tokens the model can pick from |
| How it works | Divides the model’s scores before they become probabilities | Keeps the likeliest tokens until their combined probability reaches p |
| Typical range | 0 to 1 or 0 to 2, depending on the provider | 0 to 1 |
| Lower value | More predictable, more repeatable | A smaller, safer pool of tokens |
| Higher value | More varied | A larger pool, including less likely tokens |
| Common advice | Change one of the two, not both. Anthropic’s guidance says to adjust temperature or top-p, not both |
Does Temperature 0 Make an LLM Deterministic?
Not fully. At temperature 0 the model always picks the single likeliest token, so the sampling randomness is gone. Outputs can still differ slightly between runs.
Research from Thinking Machines Lab (September 2025) traced much of this to how inference servers work. They group requests into batches, and the batch size changes the order of the floating-point math. That can flip a choice when two tokens are nearly tied. So temperature 0 removes randomness in how the model chooses, but not in the numbers it chooses from.
Why Do AI Assistants Answer Differently Each Time?
Temperature is only one cause. An answer can change between runs because of:
- Sampling: at any temperature above 0, the model picks from a spread of likely tokens, not always the top one.
- Wording and context: small changes in the prompt, or earlier messages in the chat, shift the answer. See context window.
- Live sources: assistants that search the web may pull different pages each time.
- Query fan-out: one question can be split into several related searches, each leading to different sources.
For how this affects visibility, see how AI answer variability impacts SEO.
Can Users Set Temperature?
Developers can. People using the consumer apps can’t.
| Where | Can you set temperature? | Checked |
|---|---|---|
| OpenAI API | Yes on models that support it. Some newer reasoning models accept only the default value | Nov 5, 2026 |
| ChatGPT app and custom GPTs | No | Nov 5, 2026 |
| Anthropic API | Yes. Range 0 to 1, default 1. Anthropic advises changing temperature or top-p, not both | Nov 5, 2026 |
| Claude.ai | No visible control | Nov 5, 2026 |
| Gemini API and Google AI Studio | Yes. Google recommends keeping the default for Gemini 3 models | Nov 5, 2026 |
| Gemini app | No visible control | Nov 5, 2026 |
Vendors change what each model accepts, so the “Checked” column shows when each row was last confirmed.
What Does Temperature Mean for Brand Tracking?
Because users can’t set temperature, and the assistants they use don’t give the same answer every time, one answer tells you very little about whether a brand is visible. Measure it across repeat runs:
- Run each prompt several times per engine, across days.
- Report a rate, such as “named in 6 of 10 runs”, not a single yes or no.
- Track mentions and citations separately. A brand can be named without a link, or linked without being named.
- Watch the trend, not a single day.
Wellows prompt tracking runs each prompt repeatedly across engines for this reason.
Conclusion:
Temperature is one of the settings that make AI answers vary, and users of ChatGPT, Claude.ai and the Gemini app can’t change it. Even at temperature 0, outputs can differ. For brands, the practical answer is to track visibility across repeat runs and report rates, not single answers.