LLM Temperature Explained: Why the Same Prompt Gives Different Answers
Temperature controls how random an AI model's word choices are. Low temperature gives consistent, predictable answers; high temperature gives varied, creative ones. How it works, what values to use for which tasks, top-p, and why temperature 0 still isn't fully deterministic.
Ask an AI model the same question twice and you'll often get two different answers. That's not a bug — it's sampling, and the main dial that controls it is called temperature.
How a model picks words
A language model writes one token (roughly a word piece) at a time. (What are tokens?) At each step, it doesn't produce one answer; it produces a probability for every possible next token:
"The weather today is ___" sunny 40% · cloudy 25% · warm 15% · lovely 8% · … · purple 0.001%
Then it picks one. Temperature decides how it picks.
What temperature does
- Low temperature (near 0): sharpen the odds. The most likely token almost always wins. Output is focused, consistent and predictable.
- Temperature 1: use the model's probabilities as they are. Natural variety.
- High temperature (above 1, where allowed): flatten the odds. Unlikely tokens get picked more often. Output is more varied, surprising — and eventually incoherent.
Think of it as a creativity dial with "reliable" at one end and "chaotic" at the other.
What to use for what
| Task | Temperature | Why |
|---|---|---|
| Extracting data, classification, JSON | Low (0–0.3) | You want the same right answer every time |
| Code | Low to moderate | Correctness matters; some flexibility helps |
| Q&A, support bots, summaries | Low to moderate | Accurate but natural |
| Brainstorming, marketing copy, fiction | Moderate to high (0.7–1) | You want variety |
Ranges differ by provider — where the Claude API accepts temperature, it's 0 to 1; OpenAI's accepts 0 to 2. And many newer models don't let you set it at all. On the Claude API, the current Opus, Sonnet and Fable models (Claude Opus 4.7 and later, Sonnet 5 and later) reject temperature, top_p and top_k with an error; you steer them with instructions, the effort setting and structured output instead. Haiku 4.5 and older models still accept sampling settings. OpenAI's reasoning models restrict them too. Check the docs for the model you use; the default is usually a reasonable starting point.
await client.messages.create({
model: 'claude-haiku-4-5',
max_tokens: 300,
temperature: 0.2,
messages: [{ role: 'user', content: 'Classify this ticket: ...' }],
})
Top-p (and why you usually shouldn't touch both)
Top-p ("nucleus sampling") is another dial. Instead of reshaping the odds, it cuts off the long tail: top-p 0.9 means "only consider the most likely tokens that together add up to 90% probability; ignore the rest."
Both control randomness. The general advice from providers is to adjust one or the other, not both. Temperature is the more intuitive one.
Temperature 0 isn't a guarantee
Even at temperature 0, you can still get slightly different outputs between runs, because of how calculations are batched on the provider's hardware and other implementation details. Close, but not identical.
So if your app needs consistent output — a specific JSON shape, a fixed label set — don't rely on temperature alone. Use:
- Structured output with a schema, so the format is enforced. (Structured output from LLMs)
- Validation in your code, with a retry when it fails. (Validate input with Zod)
- Caching the answer if the same input should always produce the same result.
Temperature doesn't fix accuracy
A common misunderstanding: lowering temperature doesn't make the model know more. If it doesn't have the facts, it'll produce the same wrong answer more consistently. For accuracy, give it the information it needs. (Why AI hallucinates, what is RAG?)
The summary
- Models choose each token from a probability list; temperature controls how adventurously.
- Low = consistent and focused; high = varied and creative.
- Use low for extraction, code and facts; higher for brainstorming and writing.
- Adjust temperature or top-p, not both.
- For reliable formats, use structured output and validation — not just temperature 0.
EasySpawn runs your AI-powered backend on its own server, where you can log prompts, settings and outputs side by side and tune them with Claude Code. See how it works or join the waitlist.
Related: What Is an LLM? · What Is a System Prompt? · Structured Output From LLMs · Why AI Hallucinates
Keep reading
Why Does AI Hallucinate? And How to Reduce It in Your App
AI models sometimes state false things with complete confidence. Why it happens — they predict plausible text rather than look up facts — the kinds of hallucination you'll meet in coding and apps, and practical ways to reduce it: grounding, tools, structure, checks and room to say 'I don't know'.
What Is a System Prompt? How to Give an AI Its Instructions
The system prompt is the standing instruction that shapes every answer an AI model gives in your app — its role, rules, tone and format. How it differs from user messages, what to put in one, a template you can adapt, and why it's not a security boundary.