LLM Temperature, Explained Simply
Temperature is one of those AI parameters that sounds more mysterious than it really is. But it boils down to how adventurous a language model is when choosing the next token.
A low temperature makes the model stick closer to the most likely answer. A high temperature gives less likely words more room to show up.
The Next Word Problem
A language model writes by repeatedly answering one question:
Given everything so far, what token should come next?
It does not usually have only one option. It has a ranked list of possible next tokens, each with a probability.
For example, after this phrase: The best way to learn is to ___
The model might score words like practice, read, build, experiment, or teach. Temperature changes how strongly the model favours the top options.
The Useful Mental Model
Temperature changes the shape of the probability distribution.
- Low temperature sharpens it. The top token becomes even more dominant.
- High temperature flattens it. More tokens get a meaningful chance.
In more technical terms, temperature is applied to the model's raw token scores before they become probabilities.
adjusted score = raw score / temperatureThen the model samples from the adjusted probabilities. You do not need to memorize the math to use it well. Just remember this:
Low temperature is predictable. High temperature is exploratory.
Move the temperature
The words are illustrative token candidates. The important part is the shape: low temperature sharpens the bars; high temperature spreads probability around.
Temperature Ranges
Here is the practical table I keep in mind:
| Temperature | Common use cases | What it tends to do |
|---|---|---|
| Low: 0.0–0.3 | Factual responses, coding assistance, data extraction, content moderation | Prioritizes consistency, structure, and the most likely tokens. |
| Medium: 0.4–0.7 | Summarization, educational content, problem-solving, constrained creative writing | Balances reliability with more natural variation. |
| High: 0.8–1.0 | Brainstorming, creative writing, marketing copy, joke generation | Encourages variety, surprise, and less predictable wording. |
These ranges are not laws. Different models and APIs can feel slightly different at the same value.
When I Lower It
Correctness, structure, and repeatability matter more than creativity here. I want a solid, deterministic output.
Examples:
- extracting fields from a document
- generating JSON
- writing a migration script
- debugging a specific error
- summarizing something sensitive
- following a strict format
In those cases, creativity is usually not the goal. I want the model to be boring in the best possible way.
When I Raise It
I want more options, and creativity matters more than precision.
Examples:
- brainstorming article titles
- generating product names
- writing fiction
- exploring different explanations
- coming up with jokes
- producing multiple angles on the same idea
- problem solving
Here, the first obvious answer may not be the best one. A bit more randomness can help the model wander into more interesting territory.
What I Actually Do
For everyday LLM use, I rarely touch temperature. Modern models generally perform well with their defaults, and prompt quality, model choice, reasoning effort, and output constraints usually have a much bigger impact.
When I'm building an AI feature for a specific task, though, I don't want to rely on intuition. I use evals. I improve the prompt and test it against representative examples. When the model supports it and temperature could plausibly affect the task, I also compare different temperature settings to see what actually performs best.
