Temperature and Top-p, Explained (2026)

At each step a model assigns a probability to every possible next token. Temperature rescales those probabilities before one is sampled. A low temperature sharpens the distribution so the most likely tokens dominate, making output more deterministic and repetitive. A high temperature flattens the distribution so less-likely tokens get a real chance, making output more varied and unpredictable.

Practically: low temperature for tasks with a right answer (extraction, classification, code, factual Q&A) and higher temperature for tasks where variety is the goal (brainstorming, fiction, marketing copy). Per the OpenAI API reference, the chat `temperature` parameter ranges from 0 to 2, with higher values producing more random output.

Top-p, also called nucleus sampling, takes a different route to the same goal. Instead of rescaling probabilities, it restricts the candidate pool: the model considers only the smallest set of top tokens whose cumulative probability reaches p, then samples from that set. A top-p of 0.1 means only the tokens making up the top 10% of probability mass are considered — very focused. A top-p of 1.0 considers everything.

So lowering top-p narrows what the model is even allowed to pick, which tightens output without flattening or sharpening probabilities the way temperature does. The OpenAI API reference describes top_p as an alternative to temperature for controlling this nucleus of candidate tokens.

Generally, pick one and leave the other at its default. OpenAI's guidance is to alter temperature or top_p but not both, because they both shape the same randomness from different angles and combining them makes behavior hard to predict.

A simple default: use temperature as your one dial. It is the more intuitive control — turn it down for precision, up for creativity — and most teams never need to touch top-p. Reach for top-p only when you specifically want to cap the candidate set regardless of how the probabilities are shaped.

Lower the value when: You need accuracy and consistency — extraction, classification, code, factual answers, structured output, anything you'll run repeatedly and expect the same result.
Raise the value when: You want range and surprise — brainstorming, story ideas, varied marketing copy, alternative phrasings, creative exploration.

The table below gives practical starting points, not hard rules. Begin at the suggested value, then nudge it based on whether output is too rigid or too loose. Defaults vary by provider, and not every model or endpoint exposes these parameters — some reasoning-oriented models fix sampling internally and ignore temperature and top_p entirely. Check your provider's reference before assuming a setting applies.

When a model does not expose temperature or top-p, control variety through the prompt instead: ask explicitly for one definitive answer, or for N distinct options. For drafting prompts tuned to either factual or creative work, our ChatGPT prompt generator and story idea generator are good starting points.