What Temperature Actually Controls
When a language model generates text, it produces a probability distribution over all possible next tokens — some words are far more likely than others given what came before. Temperature modifies that distribution. At temperature 0, the model always picks the single most probable token — completely deterministic and repeatable. At temperature 1 (or higher), the probability distribution flattens: less likely tokens become more possible, introducing variability. At very high temperatures (above 1.5), the distribution becomes so flat that the output can become incoherent. Temperature is, in essence, a dial that controls how much the model follows the statistical equivalent of 'what word usually comes next here.'
Low Temperature: When to Use It
Low temperature (0 to 0.3) is appropriate for tasks where consistency and accuracy matter more than variety: code generation, data extraction, factual summarization, classification, structured data formatting, and any task where you need the same input to produce the same output reliably. If you're processing thousands of customer emails into categories, you want every run of the same email to produce the same category. If you're generating SQL queries from natural language, you want the most likely correct syntax, not creative alternatives. For any task where 'wrong' is clearly defined and 'consistent' is more valuable than 'varied,' use low temperature.
High Temperature: When to Use It
Higher temperature (0.7 to 1.0+) is valuable for brainstorming, creative writing, generating diverse options, and any task where you want the model to surprise you with something less expected. When you're stuck for ideas and want ten genuinely different directions, higher temperature generates options that wouldn't have appeared at lower settings. When you're writing fiction and want unexpected word choices and fresh metaphors, higher temperature produces more distinctive prose. The risk is increased incoherence — higher temperature output needs more human review and editing, but it also produces more raw material worth editing.
Temperature vs. Prompt Wording: Which Matters More?
Temperature controls how much randomness exists in word selection; your prompt determines what direction that randomness explores. A high-temperature prompt can still produce focused, relevant output if the instructions are tight — the randomness just makes the word choices more varied within the constrained space. A low-temperature prompt with vague instructions will consistently produce the same mediocre output. Prompt quality matters more than temperature for most tasks. Temperature tuning is a fine adjustment to make after you've already built a good prompt — not a substitute for clear, specific instructions.
Practical Temperature Settings to Know
Most AI APIs default to temperature values around 0.7-1.0, which is a reasonable middle ground for general tasks. For code generation and data extraction, 0 to 0.2 produces the most reliable results. For general writing and Q&A, 0.5 to 0.8 is a good range. For brainstorming and creative tasks, 0.8 to 1.2 generates more diverse output. Some models cap temperature at 2 — values above 1 on most models produce noticeably more erratic output that requires careful screening. When in doubt, run the same task at two or three temperature settings and compare the results rather than assuming a specific value is correct.