Temperature
In one sentence
Temperature is a sampling parameter that controls how varied a model output is, low producing consistent and predictable responses and high producing more varied ones, by reshaping the probability distribution over next tokens before selection.
Definition
Temperature controls how varied the output is. Low temperature gives consistent, predictable answers. High temperature gives more varied and creative ones.
It is a dial for randomness, not for accuracy. Turning it down makes the model more consistent, not more correct.
The model produces a probability for every possible next token. Temperature reshapes that distribution before a token is selected. Low temperature sharpens it, concentrating probability on the most likely options. High temperature flattens it, giving less likely options a better chance. At temperature zero the model always selects the most probable token, which is deterministic given identical input.
Related sampling parameters
- Top-p, or nucleus sampling, restricts selection to the smallest set of tokens whose cumulative probability exceeds a threshold.
- Top-k restricts selection to a fixed number of the most likely candidates.
- These are frequently adjusted together, and adjusting several at once makes the effect hard to reason about.
What to use for a business voice agent
- Low, generally. An agent answering questions about the policies of a business should be consistent, so the same question produces the same answer.
- High temperature introduces variation in phrasing and, more importantly, variation in what facts are included, which undermines grounding.
- Where variety is genuinely wanted, in a greeting for instance, it should come from designed alternatives rather than from sampling randomness. Write three greetings, do not use randomness.
The determinism caveat
- Temperature zero produces deterministic sampling, not fully deterministic output.
- Provider-side batching, hardware non-determinism and model version changes can all produce variation.
- A system requiring exact reproducibility for compliance cannot rely on temperature alone.
The relationship to hallucination
- Higher temperature increases the chance of selecting a less likely token, which can start a generation down a path unsupported by the retrieved context.
- Low temperature reduces but does not eliminate this. Temperature is a mitigation, not a solution, and grounding is the real answer.
Common misconception
That temperature controls accuracy or creativity in any meaningful sense. It controls randomness of selection. Lower temperature makes the model more consistent, not more correct.
Why it matters commercially
Consistency is a business requirement. A voice agent giving different answers about pricing on different occasions is a liability, and temperature is the simplest control available to prevent it.
In voice specifically
A listener cannot put two spoken answers side by side to reconcile them the way a reader can compare two web pages. Variation that a reader might skim past is heard across calls as an agent that is unsure of its own facts, so consistency carries more weight in voice than the same setting would in text.
Where AsqVox fits
A grounded agent answering from uploaded documents should be consistent across sessions, which argues for low temperature as the default.
Visual
Sharpening or flattening the distribution
It controls randomness, not correctness. Lower is more consistent, not more right.
Statistics
Every figure carries its source and year. Vendor numbers are labelled as vendor numbers, and where no reliable figure exists this page says so rather than borrowing one.
Temperature reshapes the probability distribution over candidate next tokens before selection. Values approaching zero concentrate selection on the most probable token.
-industry rangeSampling mechanism, standard across model providers, 2026 - A mechanism fact, not a measured figure. It is the reason temperature zero is deterministic sampling: the sharpened distribution leaves only one token in contention.
Top-p and top-k are related sampling controls frequently adjusted alongside temperature.
-industry rangeStandard sampling parameters across model providers, 2026 - Adjusting several at once makes the combined effect hard to reason about, which is why changing one parameter at a time is the safer discipline.
There is no published benchmark measuring hallucination rate as a function of temperature in retrieval-grounded systems, despite the relationship being widely assumed.
-no reliable figureThe direction is agreed, that higher temperature raises the chance of an unsupported token, but no public number puts a figure on it in a grounded setting.
Provider default temperature values differ and are not always documented, so behavior can vary between providers at nominally identical settings.
-no reliable figureTwo providers set to the same nominal temperature are not necessarily doing the same thing, which is a reason to test rather than assume parity.
There is no reliable industry-wide figure for hallucination rate in deployed business RAG systems at any temperature.
-no reliable figureCovered in the hallucination entry. Any single number quoted for it is a vendor benchmark on vendor data.
Examples
In practice
A compliance review finds a voice agent giving materially different answers to the same policy question across test runs. Temperature is at a moderate default. Setting it near zero produces consistent answers, and the remaining variation traces to retrieval returning different passages, which is a chunking problem rather than a sampling one. Fixing the sampling revealed the real issue.
The everyday version
Temperature is a dial for how much the AI varies what it says. Turned up, it gets more inventive, which sounds appealing and is exactly what you do not want when it is answering questions about your prices. Turned down, it gives the same answer to the same question, which is what a customer expects.
Usage
Who says it
- AI engineers and prompt designers say it routinely.
- It appears in every provider API and in configuration interfaces.
Where it turns up
- On a spec sheet it sits next to model configuration, determinism, consistency guarantees and reproducibility.
- A compliance requirement for reproducibility is where the determinism caveat becomes a live issue, because temperature zero alone will not deliver it.
Common misuse
- Treating it as a creativity or accuracy control. It controls randomness of selection.
- Adjusting several sampling parameters simultaneously, which makes the effect uninterpretable.
- Assuming temperature zero guarantees identical output across runs.
Questions people ask
Does lower temperature make the model more accurate?
No. It makes the model more consistent, not more correct. Temperature controls the randomness of which token is selected, not whether the selected answer is true. A low temperature will faithfully and repeatedly return the same wrong answer if the underlying retrieval or source is wrong. Accuracy comes from grounding, not from the sampling dial.
What temperature should a business voice agent use?
Low, generally. An agent answering about your policies and prices should give the same answer to the same question, and high temperature varies not only phrasing but which facts get included, which undermines grounding. If you want variety somewhere, such as a greeting, write a few designed alternatives rather than turning up the randomness.
Is temperature zero fully deterministic?
It is deterministic sampling, not fully deterministic output. At zero the model always selects the most probable token given identical input, but provider-side batching, hardware non-determinism and model version changes can still produce variation. A system that needs exact reproducibility for compliance cannot rely on temperature alone.
What are top-p and top-k?
They are related sampling controls often adjusted alongside temperature. Top-p, or nucleus sampling, restricts selection to the smallest set of tokens whose cumulative probability passes a threshold. Top-k restricts to a fixed number of the most likely candidates. Adjusting several of these at once makes the combined effect hard to reason about.
Last reviewed 3 August 2026. Written and reviewed by Dhruv Dholakia, founder of AsqVox.