How to Use the LLM Temperature & Top-P Sampling Simulator
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1
Adjust Temperature (0.05 to 2.00) to see how Softmax scaling flattens or sharpens token probabilities.
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2
Slide Top-P (0.10 to 1.00) to observe cumulative probability mass pruning in real time.
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3
Tune Top-K (1 to 50) to enforce hard candidate caps.
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4
Test scenario presets (Deterministic Code, Factual Q&A, Creative Storytelling, Wild Brainstorming).
Practical Example & Use Case
Understanding why setting temperature to 0.1 eliminates bugs in automated code refactoring, while temperature 0.8 is needed for marketing tagline ideation.
Technical Notes & Limitations
Calculates real-time Softmax and nucleus sampling algorithms on representative candidate token logit distributions in-browser.
Frequently Asked Questions
What is the mathematical difference between Temperature and Top-P?
Temperature divides raw logits before the Softmax function, smoothing or sharpening the curve. Top-P sorts tokens by descending probability and truncates all tokens beyond the cumulative threshold P.
Should I adjust both Temperature and Top-P simultaneously?
OpenAI and Anthropic recommend adjusting either Temperature OR Top-P, rather than altering both at once, to ensure predictable generation behavior.
What temperature should I use for coding and math?
Use Temperature 0.0 to 0.2 with Top-P 0.1 to 0.3. This forces the model to select the statistically optimal syntax without introducing random hallucinations.