LLM Temperature

What Is LLM Temperature?

LLM temperature is a sampling parameter that controls how random a language model's output is. At low temperature the model almost always picks its highest-probability next token, giving consistent, predictable responses. At high temperature it samples more freely from less likely tokens, giving varied and sometimes surprising output.

Key Takeaways

• Temperature reshapes the probability distribution the model samples from; it does not change what the model knows or how it reasons.
• Low temperature (0 to about 0.3) suits agents, extraction, and code, where consistency and debuggability beat variety.
• Temperature 0 still does not guarantee identical outputs: serving-side nondeterminism means byte-for-byte reproducibility is not promised by most APIs.
• Reasoning models often ignore or restrict the temperature parameter, so check the model docs before tuning it.

How It Works

At every generation step the model produces a score for each token in its vocabulary, and those scores become a probability distribution. Temperature divides the scores before that conversion. Values below 1 sharpen the distribution, concentrating probability on the top candidates; values above 1 flatten it, giving weaker candidates a real chance of being picked. At temperature 0 (or very close), sampling collapses to greedy decoding: take the single most likely token every time. Nothing about the model's internal computation changes. The same knowledge and the same reasoning produce the scores; temperature only decides how adventurously you draw from them.

For engineers running agents, the setting interacts with everything downstream. A tool-calling loop at high temperature occasionally picks an oddball token inside a JSON argument, and one bad token can derail a whole trajectory, which is why agent frameworks default low. Low temperature also makes failures more reproducible, so debugging a prompt regression is far easier at 0.1 than at 0.9. The cost is mode collapse on generative tasks: ask for ten headline options at temperature 0 and you get near-duplicates. Temperature is usually paired with top-p (nucleus) sampling, which truncates the candidate pool to the smallest set covering a probability mass; most providers advise tuning one, not both, because their effects overlap.

Example

A team ships an agent that reads bug reports and files structured tickets through a tool call. In staging, run at the default temperature of 0.7 for weeks, it works well. In production, one ticket in roughly two hundred arrives with a mangled priority field, and the failures never reproduce when replayed. An engineer drops the temperature to 0.1 for the tool-calling step and the corruption disappears: the model had occasionally sampled a low-probability token inside an enum value. They keep a second, higher-temperature call for the one genuinely creative step in the pipeline, drafting a user-facing summary, where variety reads as natural rather than broken. Two calls, two temperatures, matched to two different jobs.

What People Get Wrong

The stubborn myth is that temperature 0 makes an LLM deterministic and therefore correct. It does neither reliably. On determinism, production serving stacks introduce nondeterminism through batching, hardware, and mixture-of-experts routing, so identical requests can still yield different outputs at temperature 0. On correctness, temperature only changes which token gets picked from the distribution the model computed. If the model's most probable continuation is a hallucination, temperature 0 selects that hallucination with full confidence. A 2024 study by Renze and Guven across nine popular LLMs found that moving temperature from 0.0 to 1.0 had no statistically significant effect on problem-solving performance, and the result held across models, prompting techniques, and problem domains [1]. Accuracy problems live in the model, the prompt, and the retrieved context, not in the sampling knob.

FAQ

What temperature should I use for coding and agents? Start at 0 to 0.2 for code generation, extraction, classification, and any tool-calling loop. Raise it only when outputs feel stuck in a rut and the task genuinely benefits from variation, such as brainstorming or copywriting, where 0.7 to 1.0 is a common range. Provider guidance points the same way: Anthropic's Messages API accepts temperatures from 0.0 to 1.0 with a default of 1.0, and its documentation recommends values near 0.0 for analytical or multiple-choice tasks and near 1.0 for creative work [2].

What is the difference between temperature and top-p? Temperature reweights the whole token distribution; top-p cuts off the unlikely tail and samples from what remains. Both trade consistency against diversity. Standard advice is to adjust one and leave the other at its default, since tuning both compounds unpredictably.

Why do I get different answers at temperature 0? Greedy decoding removes sampling randomness, but the serving infrastructure is not perfectly deterministic: floating-point operations reorder under batching, and expert routing can shift. Treat temperature 0 as "highly consistent," and build evals that tolerate small variations rather than asserting exact strings.

Sources

1. Renze & Guven (arXiv, EMNLP 2024 Findings). "Changing sampling temperature from 0.0 to 1.0 has no statistically significant impact on LLM problem-solving performance across nine models." https://arxiv.org/abs/2402.05201. Accessed August 2026.
2. Anthropic API documentation. "Messages API temperature range 0.0-1.0, default 1.0, with low values recommended for analytical tasks and high for creative tasks." https://platform.claude.com/docs/en/api/messages. Accessed August 2026.