Temperature vs. top_p: A Practical 2026 Sampling Guide
Temperature and top_p control how a language model selects its next token. They influence variation, repetition and consistency, but they do not add knowledge or guarantee factual answers. In 2026, the most useful approach is to choose a baseline, confirm which controls your model supports, and test changes against representative tasks.
This guide explains how sampling works, when to adjust each setting, and how to compare results without wasting API spend.
How token sampling works
At each generation step, a model assigns scores called logits to possible next tokens. Tokens can be words, word fragments, punctuation or other text units. The generation system turns these scores into probabilities, applies supported sampling controls, and selects a token. It repeats this process until the response ends or reaches a configured limit.
Suppose four possible next tokens have probabilities of 60%, 25%, 10% and 5%. Sampling can select any eligible token, although higher-probability options are more likely. Each selection changes the context used to generate the following token, so a small early difference can produce a substantially different response.
Temperature changes the probability distribution
Temperature typically scales logits before they become probabilities. Lower values concentrate probability on already likely choices. Higher values flatten the distribution, giving less likely choices more opportunity. Temperature 1 generally preserves the original distribution before other sampling controls apply.
- Low temperature: A useful starting point for extraction, classification and narrowly specified answers, when supported.
- Moderate temperature: Useful for explanations and drafts that need some variation.
- Higher temperature: Useful for exploring alternative slogans, story directions or brainstorming candidates, with more review required.
Temperature 0 commonly requests greedy decoding: selecting the highest-scoring token at each step. It does not guarantee identical results across requests. Backend changes, numerical differences and implementation details can still affect output. Also, accepted ranges and the meaning of defaults vary by model and endpoint.
top_p limits the candidate pool
top_p, also called nucleus sampling, keeps the smallest set of highest-probability tokens whose cumulative probability reaches the chosen threshold. The remaining probabilities are renormalized before selection.
Using the example above, top_p of 0.8 retains the 60% and 25% candidates because their combined probability reaches 85%. The 10% and 5% candidates are excluded. At another generation step, reaching the same threshold might require dozens of tokens.
top_p of 1 generally disables nucleus filtering. It does not make every token equally likely. Lower top_p values narrow the available choices, but overly restrictive settings can make writing repetitive or exclude useful alternatives.
Choose a baseline, then change one control
Temperature reshapes probabilities; top_p filters candidates using cumulative probability. They interact, so changing both together makes results harder to interpret. Providers can also differ in processing order and implementation.
- Check support first. Read the documentation for the exact model and endpoint. Some reasoning models restrict, ignore or reject sampling parameters. An OpenAI-compatible request format does not guarantee identical parameter support.
- Start with defaults. Save the prompt, model name, settings and responses before making adjustments.
- Test temperature separately. If supported, compare values such as 0.2, 0.5 and 0.8 while leaving top_p at 1.
- Evaluate nucleus sampling separately. Restore the baseline temperature, then compare supported top_p values such as 0.9 and 1.
- Score repeated runs. Use several generations per test case. Measure factual errors, task completion, format validity and useful variation.
For structured extraction, use schema-constrained output where available and validate the result. For factual answers, provide reliable source material and check claims. Sampling settings alone cannot enforce either correctness or valid JSON.
Compare models without overspending
59API offers low-cost, pay-as-you-go access to models including Claude Sonnet 5.5 and Gemini 3.1 Pro, making repeated sampling experiments more affordable. Its API base URL is https://api.59api.com, and it supports OpenAI SDK workflows alongside Claude Code, Codex and Gemini CLI. Confirm each model's supported parameters before reusing a configuration.
Temperature is not itself a token-price setting. Track input tokens, output tokens, applicable reasoning charges and retries to compare cost per successful task. If you want an affordable place to run these experiments, sign up for 59API and begin with a small evaluation set.