How do temperature and sampling settings affect model output?

Explains how decoding settings reshape token selection and why they must be tuned against task-specific evaluation rather than folklore.

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TL;DR

Temperature changes how sharply or broadly the model samples from its next-token probabilities. Lower values favor the most likely options and usually make output more consistent; higher values allow more varied choices. Other settings, such as top-p or top-k where supported, restrict the candidate set in different ways. These controls affect decoding, not the model's underlying knowledge. Lower temperature can reduce variation, but it does not make an unsupported answer true.

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Core answer
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Overview
Sampling settings shape how the model chooses among likely continuations; they do not repair missing knowledge or guarantee correctness.
Temperature changes how sharply or broadly the model samples from its next-token probabilities. Lower values favor the most likely options and usually make output more consistent; higher values allow more varied choices. Other settings, such as top-p or top-k where supported, restrict the candidate set in different ways. These controls affect decoding, not the model's underlying knowledge. Lower temperature can reduce variation, but it does not make an unsupported answer true.
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I choose settings from the task. Structured extraction, classification, and tool arguments usually benefit from constrained output and low variation. Brainstorming or copy variants may tolerate more diversity. I avoid changing several sampling controls without measuring the result because their interaction is model- and provider-dependent. Schema constraints, validation, good context, and task-specific examples often matter more than fine-tuning one number.

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Details

Saying a high temperature makes the model more intelligent.
Saying temperature zero guarantees factual or identical output.
Tuning several decoding controls without a fixed evaluation set.
Using creative sampling for a strict structured-output task without a reason.

How do temperature and sampling settings affect model output?

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