Temperature in AI is a sampling setting that changes how a model chooses among possible next tokens. Lower values generally concentrate choices on more likely tokens; higher values allow a wider variety. It is not a factual-accuracy, safety, or intelligence setting.
Low and high temperature
| Setting | Typical effect | What it does not guarantee |
|---|---|---|
| Lower temperature | Less variation across repeated requests; more emphasis on high-probability choices. | Truth, safety, or identical output every time. |
| Higher temperature | More varied word choices and candidate responses. | Better ideas, originality, or a useful result. |
A low-temperature response can confidently repeat the same incorrect fact. A high-temperature response may vary its wording without improving the answer. Verify important claims at either setting.
Where can you change it?
Some model APIs and developer interfaces expose temperature. Other models or applications fix it, restrict its range, or do not support changing it. Do not assume that the ChatGPT chat interface has a temperature slider, or that asking “use temperature 0.2” changes a real sampling parameter.
The supported range and default belong to the specific API and model. A value that works for one model is not a universal recommendation for another.
A small comparison you can run
- Choose an interface that documents support for temperature.
- Keep the model, source material, and prompt unchanged.
- Request three candidate headings at a lower supported setting, then repeat at a higher supported setting.
- Compare relevance, repetition, and compliance with the brief. Record the settings and date.
Suggest three headings for a beginner guide about planning a weekly grocery shop.
Use plain language. Each heading must be under eight words.
Do not claim a price saving or a health benefit.This is a suggested comparison, not a report of tests performed here. A few outputs illustrate variability; they do not establish a model's reliability.
Temperature versus other controls
Learned model parameters are adjusted during training; temperature changes sampling during generation. Tone is usually requested in the prompt. Output-length limits control how much can be generated, not how random the choices are.
Use the idea-generation lesson to set selection criteria, and check for invented details before using an answer.