Table of Contents
Temperature in AI: Clear Definition + Examples (2026)
Quick Answer
Temperature is a number (usually 0 to 2) that controls how random a large language model's next-word choice is. Lower = safe and repetitive; higher = creative and unpredictable.
- Range: typically 0.0 to 2.0 (default often 0.7 or 1.0)
- 0.0 = always picks the highest-probability word
- 2.0 = nearly random word choice, often gibberish
What Does Temperature Mean?
When an LLM generates text, it computes a probability for every possible next token. Temperature rescales those probabilities before sampling. At temperature 0, the model always picks the single most likely token — fully deterministic. As temperature rises, lower-probability tokens become more competitive, so the model is willing to pick less obvious words (OpenAI API docs, 2024).
Think of it as a "creativity dial." Zero = a strict grammar teacher. thoughtful writer. Two = a caffeinated poet.
How It Works
Internally, the model outputs logits (raw scores) for each token. Temperature divides each logit before the softmax step:
adjusted_logit = logit / temperature
Dividing by a small number (0.2) makes large logits even larger relative to small ones, concentrating probability on the top candidate. Dividing by a large number (1.5) flattens the distribution, giving rare tokens a real chance.
Examples
| Temperature | Output Example |
|---|---|
| 0.0 | "The capital of France is Paris." (Always same answer.) |
| 0.3 | Customer support reply that stays on-brand and factual. |
| 0.7 | Balanced blog draft with some variation between runs. |
| 1.2 | Poetry or brainstorming with surprising word choices. |
| 1.8 | Experimental fiction or intentional chaos. |
Temperature vs Top-p
Top-p (nucleus sampling) limits the model to the smallest set of tokens whose combined probability exceeds p. Temperature rescales; top-p truncates. Most teams tune one or the other — not both aggressively. Anthropic's Claude API docs recommend adjusting only one at a time.
When to Use
| Temperature Range | Use Case |
|---|---|
| 0.0 to 0.3 | Factual Q&A, code generation, structured data extraction |
| 0.5 to 0.8 | General chat, blog drafts, summaries |
| 1.0 to 1.5 | Creative writing, brainstorming, marketing taglines |
Conclusion
Temperature is the simplest lever for shaping AI output. Start with 0.7, drop to 0 for facts, raise past 1 for creativity. Learn more AI concepts on Misar Blog.