Temperature Simulator

Temperature scales all token probabilities uniformly, affecting the entire probability distribution. Top-p (nucleus sampling) instead selects from the smallest set of tokens whose cumulative probability exceeds the threshold p. Temperature affects how "peaked" the distribution is, while top-p cuts off the tail of low-probability tokens. Most experts recommend adjusting one at a time, not both simultaneously, to understand their effects better.

Yes, in most APIs (especially OpenAI), temperature can go up to 2.0. Values above 1.0 flatten the probability distribution, making unlikely tokens more likely to be selected. This can produce more surprising and varied outputs but often leads to incoherent, nonsensical, or grammatically incorrect text. Values above 1.5 are rarely useful for practical applications.

Some APIs don't implement true temperature 0 — they use a very small value like 0.0001 instead. Additionally, if the prompt or system message includes randomness, or if the API uses parallel processing with non-deterministic execution, outputs can vary. For truly reproducible results, look for a "seed" parameter if available, and ensure all other settings are identical.

For code generation, use temperature 0 or 0.1. Code has strict syntax requirements, and even small deviations can cause errors. Low temperature ensures the model picks the most likely (and usually correct) tokens. Some developers use slightly higher values (0.2-0.3) when they want the model to consider alternative implementations, but this increases the risk of bugs.

Temperature doesn't directly control response length — that's managed by max_tokens. However, very high temperatures can cause the model to pick unusual tokens like early stop sequences, potentially shortening responses. For quality, there's a sweet spot: too low can be repetitive and boring; too high can be incoherent. Temperature 0.7 is often optimal for quality in most applications.

Yes, for purely factual questions, temperature 0 is generally best. It ensures the model gives its most confident answer. However, note that low temperature doesn't prevent hallucinations — the model may still confidently output incorrect information. For critical factual tasks, always verify the output against authoritative sources.

Temperature affects token selection regardless of the system prompt. A system prompt can ask the model to "be creative," but low temperature will still constrain output variety. For best results, align your temperature setting with your system prompt's intent: creative system prompts work better with higher temperature, while precise instructions work better with lower temperature.

ChatGPT's exact temperature setting isn't public, but based on its behavior, it likely uses something around 0.7-0.9 for general conversations. The API default is 1.0, but the ChatGPT interface may use a lower value for more consistent UX. When building your own chatbot, 0.7 is a good starting point to balance personality with reliability.

Yes, most APIs allow you to set temperature on each API call. You could use low temperature (0.3) for fact-finding parts of a conversation and higher temperature (0.9) for creative brainstorming, all within the same session. The model doesn't "remember" previous temperature settings — it only affects the current response generation.

The name comes from statistical mechanics and the Boltzmann distribution in physics. In thermodynamics, temperature controls the randomness of molecular motion — higher temperature means more random molecular behavior. The softmax function with temperature is mathematically equivalent to the Boltzmann distribution, so the analogy is direct: higher temperature = more randomness in choosing tokens, just as higher physical temperature = more random molecular motion.