Temperature changes how strongly the model favors its most likely next tokens. A low value makes common choices more dominant, which usually produces more consistent output. A higher value gives less likely choices more chance, which can add variety but also increase mistakes or drift.
Use lower temperature for extraction, classification, and strict formatting. Moderate values can help with brainstorming or creative drafts. Temperature does not control truthfulness: the model's most likely answer can still be wrong.
Some models, especially reasoning models, do not expose temperature or support only a default value. Treat sampling settings as model-specific. Change one setting at a time and compare results on an evaluation set rather than choosing a value by intuition alone.
Rewriting in plainer words…
This answer doesn't lend itself to a diagram - it reads best . No credits were charged.
