Temperature
Temperature is a setting that controls how random a language model is when it picks the next token. A low value makes it choose the most likely continuation almost every time, while a high value gives less likely words a real chance.
Last updated November 8, 2026
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How it works
At each step a model scores every token in its vocabulary and turns the scores into probabilities. Temperature rescales those scores before the choice is made. Close to zero, the most probable token dominates and the output becomes nearly deterministic: asking the same question twice gives almost the same answer. Around one, the model samples in line with its raw probabilities. Higher still, the distribution flattens, surprising words appear more often, and the text can become inventive, then incoherent. Picture a lottery drum: a low temperature leaves only the heaviest balls inside, a high one lets the lightest ones tumble out.
In practice the right value depends on the job. A clause extractor, a unit-test generator or a SQL helper wants low values, where consistency matters and a creative detour is a bug. A slogan brainstorm, a bedtime story or a villain’s monologue wants higher ones, where variety is the point. Low temperature does not make a model more truthful, though: a model can repeat the same wrong answer with perfect consistency.
Two related controls are often set alongside it. Top_p, or nucleus sampling, keeps only the smallest group of tokens whose probabilities add up to a chosen share and samples from that group. A stop sequence tells the model where to end its reply. A seed, where a provider supports it, makes a random choice repeatable. The usual advice is to adjust temperature or top_p, not both at once. Mathematically, the logits are divided by the temperature before the softmax function normalizes them; dividing by a number below one sharpens the peaks, while dividing by a number above one flattens the landscape and spreads probability toward the tail.
In NeverNo
On NeverNo, the API accepts temperature, top_p, stop and seed in a chat request. They are passed to the models that take them and left out for the others, and the models list says which parameters each model supports. Which parameters apply depends on the model, so check the list before relying on one. Qwen 3.5 35B, the free private model from Alibaba that reads images and lets you choose whether it reasons, is one of the open models in the catalog.
The page about uncensored AI chat shows how NeverNo answers frankly and keeps your history in your browser. Whatever you choose for temperature, the hard limits stay the same: nothing involving minors, nothing about real people without their consent, and no real help to seriously harm others.
Worth remembering
Temperature changes how random the next-token choice is, not how much the model knows.
Low values suit tasks that need consistency; higher values suit creative work.
A low temperature does not guarantee a correct answer, only a more repeatable one.
Top_p, stop sequences and seed are close relatives; change temperature or top_p, not both.
Some reasoning models fix or ignore the setting, so read a model’s supported parameters first.
Frequently asked questions
What temperature should I use?
For factual questions, code and structured output, start low, between 0 and about 0.3. For writing and idea generation, try a value near 0.7 to 1. These are starting points, since each model reacts a little differently.
Does temperature 0 give the same answer every time?
It makes the answer very consistent, but not always strictly identical, because floating-point rounding, GPU batching and server-side scheduling can nudge the arithmetic. A seed, when the model supports one, helps reproduce a result.
Does a high temperature cause hallucinations?
It makes unlikely wording more probable, which can lead a model further from reliable ground. A model can state false things at any temperature, so check important facts whatever the setting. Logging the exact value alongside each output makes experiments reproducible and comparable across runs.
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