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LLM Temperature Simulator

Move the sliders to see how temperature and top_p affect model outputs. Sample responses update instantly so you can build intuition for these critical parameters before touching your API calls.

✓ Temperature 0.0–2.0 demo ✓ Top-P nucleus sampling explained ✓ Creativity vs. consistency meter ✓ Runs 100% in your browser

Frequently Asked Questions

What does the temperature parameter actually do?▼

Temperature controls how the model samples from the probability distribution over tokens. At temperature 0, the model always picks the single highest-probability token, producing deterministic output. As temperature increases, lower-probability tokens become more likely to be chosen, increasing diversity and creativity. Mathematically, each token probability is divided by the temperature value before applying softmax — so a higher temperature flattens the distribution, and a lower temperature sharpens it.

When should I use a high temperature vs. a low temperature?▼

Use low temperatures (0.0–0.3) for tasks requiring accuracy and consistency: classification, structured data extraction, code generation with a single correct answer, and factual Q&A. Use medium temperatures (0.7–1.0) for conversational responses and balanced creative writing. Use high temperatures (1.1–1.5) for brainstorming, generating diverse options, poetry, or marketing copy where novelty is valued. Temperatures above 1.5 are rarely useful in production — output quality degrades significantly.

What is Top-P (nucleus sampling) and how does it differ from temperature?▼

Top-P (also called nucleus sampling) limits the pool of tokens the model can sample from. Instead of sampling from all possible tokens, the model only considers tokens whose cumulative probability adds up to the top-p value. For example, top-p=0.9 means only the tokens that together account for 90% of the probability mass are eligible. Temperature then controls the randomness within that pool. Top-P is effective at preventing very low-probability 'tail' tokens from being selected, which often cause incoherent output.

Should I tune both temperature and top-p together?▼

Most model providers (OpenAI, Anthropic) recommend tuning one or the other, not both simultaneously. Both parameters reduce the token pool in different ways, and combining them can produce unintuitive results. For most use cases: set top-p to 1.0 and adjust temperature, or set temperature to 1.0 and adjust top-p. Anthropic's Claude documentation suggests keeping the non-tuned parameter at its default. The exception is when you specifically want to limit tail tokens (low top-p) while also controlling creativity (temperature).

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