Top-p (nucleus sampling)

A sampling setting that limits the model's next-word choice to the smallest set of tokens whose probabilities add up to p.

Last updated: November 9, 2026 · Markdown version

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How to apply it in prompts

Use this concept to reduce ambiguity and improve consistency. If you need strict formatting, combine it with a JSON guide and a per-model template.

Related terms:Temperature Tokens

Strict JSON Models Packs

What it means

Definition: with top-p (also called nucleus sampling) at 0.9, the model considers only the most likely tokens whose combined probability reaches 90% and ignores the long tail of unlikely ones.
Why it matters: it is another way to trade steadiness for variety. Lower values make answers more focused; values near 1 leave the full range open.
How to apply it: providers commonly advise adjusting temperature or top-p, not both at once, so you can tell what changed the result. Many chat products do not expose either; check the API or playground documentation of the model you use.
Setting it low does not make an answer true — it only narrows word choice. For reliable structure rely on a precise prompt and validation.

Copy-paste example

I will run this prompt with a low top-p for consistency.
Task: write a 2-sentence product description for [product].
Constraints: plain language, one concrete benefit, no superlatives.
Return only the two sentences.

Opens the analyzer with the prompt prefilled.

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FAQ

What is the difference between top-p and temperature?

Temperature reshapes the probability of every token; top-p cuts the list of candidates to the likeliest ones. Both control randomness, in different ways.

Do I need to set top-p?

Rarely. The provider's default works for most prompts. Reach for it when you call an API and need a specific balance of consistency and variety.