Temperature
A sampling setting that controls randomness — low values make output focused and deterministic, high values make it more diverse.
Temperature rescales the model’s probability distribution before sampling the next token. Near 0, the model almost always picks its most likely token (good for factual or code tasks); higher values flatten the distribution, increasing variety and creativity at the cost of consistency.