Temperature controls how the model samples from its probability distribution over possible next tokens. Low temperature makes the model pick high-probability tokens consistently. High temperature flattens the distribution and introduces more randomness. For tasks requiring accuracy and reasoning, randomness is not creativity. It is noise.
Analysis Briefing
- Topic: Temperature, sampling, and its effect on reasoning quality
- Analyst: Mike D (@MrComputerScience)
- Context: A technical briefing developed with Claude Sonnet 4.6
- Source: Pithy Cyborg | AI News Made Simple
- Key Question: Why does making an AI more “creative” often make it less accurate at the same time?
What Temperature Actually Does to the Output Distribution
After the model computes a probability for every token in its vocabulary, temperature scales those probabilities before sampling. At temperature 1.0, probabilities are used as-is. At temperature 0.2, the distribution is sharpened: already-likely tokens become much more likely, and low-probability tokens become negligibly rare. At temperature 0.8, the distribution is slightly flattened, giving lower-probability tokens a better chance of being selected.
For a factual question, the correct answer usually has a high probability in the model’s distribution. At temperature 0.2, the model reliably picks that high-probability correct token. At temperature 0.8, it occasionally picks a lower-probability token that happens to be plausible-sounding but wrong. Over a multi-step reasoning chain, small deviations at each token compound. A chain of twenty tokens at high temperature produces meaningfully more variance than the same chain at low temperature.
This is not about intelligence. It is about reliability of execution. The model’s knowledge does not change with temperature. The probability of executing that knowledge correctly does.
When High Temperature Is the Right Choice
High temperature is appropriate when variety, creativity, or exploration of multiple possibilities is the goal. Brainstorming article angles, generating names for a product, writing poetry, or producing diverse example sentences all benefit from a flatter distribution. You want the model to explore lower-probability continuations that are still coherent but unexpected.
Low temperature is appropriate when there is a right answer. Coding, mathematical reasoning, factual retrieval, structured data extraction, classification, and any task with a defined correct output should use low temperature. The model already has the highest probability on the correct answer. Temperature 0.2 ensures it executes on that consistently.
Temperature 0 (or close to it) produces near-deterministic outputs. The same prompt will return nearly identical responses. This is valuable for testing and for production systems where reproducibility matters.
The Interaction With Model Size
Few-shot examples making AI worse is a related phenomenon: the same parameter that helps large models can hurt small ones. Temperature behaves similarly. Smaller models have less sharply peaked probability distributions for correct answers. At low temperature, a small model reliably produces its best guess, which may still be wrong. At high temperature, a small model wanders into incoherence faster than a large model because it has less probability mass concentrated on correct continuations.
For small models on reasoning tasks, the right response is usually to lower temperature and add chain-of-thought structure, not to raise temperature hoping for a lucky correct answer.
What This Means For You
- Default to temperature 0.2 or lower for any task with a correct answer. Reasoning, coding, extraction, and classification all perform better with lower temperature.
- Use temperature 0.7 to 0.9 for generative tasks where you want variety. Brainstorming, creative writing, and exploration benefit from the increased sampling randomness.
- Set temperature to 0 in production pipelines that require consistency, because reproducible outputs are easier to test, debug, and monitor than stochastic ones.
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