Prompt Temperature Simulator

Temperature is a parameter that controls how random (or deterministic) an AI model's output is. At temperature 0, the model always selects the most probable next token — output is deterministic and reproducible. As temperature increases, the model considers less probable tokens, leading to more varied, creative, and sometimes unpredictable output.

Most APIs (OpenAI, Anthropic, etc.) expose a temperature parameter, typically from 0 to 2. Choosing the right value depends on the task: factual or code generation often benefits from low temperature; creative writing or brainstorming from higher values.

At low temperature (0–0.3): Output tends to be focused, consistent, and predictable. Good for summaries, translations, structured data extraction. At medium temperature (0.5–0.9): Some variation, useful for general-purpose chat and balanced creativity. At high temperature (1.0–2.0): Output becomes more creative, diverse, and sometimes tangential or less coherent. Useful for brainstorming, creative writing, or when you want surprising ideas.

This simulator demonstrates these effects with a simplified model: synonym swaps, sentence reordering, and optional tangential phrases. It is not real LLM output but illustrates the concept.

For tasks requiring accuracy (code, facts, structured output), use 0 or very low temperature. For balanced conversation or writing, 0.7–0.9 is common. For maximum creativity or brainstorming, 1.0–1.5 can work, but expect more variation and occasional off-topic content. Very high values (1.5–2.0) often produce incoherent or repetitive output in real models. Experiment with your specific use case and model.