Temperature is a sampling setting that can affect variation in model outputs. Its support, allowed values, and exact behavior depend on the model and API. Lower values generally favor more likely token choices; they do not guarantee a deterministic or correct answer.
Evaluate the supported setting
Check the documentation for the exact model version. Some models restrict sampling controls or use other settings. Keep other parameters fixed while comparing repeated trials on representative tasks.
Example: a label classification task
Run the same reviewed requests several times and check whether labels change. Then measure correctness against the task rubric. Consistently returning the same wrong label is not a quality improvement.
Variation and correctness are separate
A higher-variation setting can be useful when exploring candidate wording, while a constrained application may value consistency. Choose based on measured task outcomes. Temperature does not supply missing evidence, enforce permissions, or prevent unsupported claims.
Practical example
A deployment changes a sampling setting while testing consistency on classification tasks.
What to check
Confirm support and meaning for the exact model and measure repeated trials with otherwise identical inputs.
Limits and tradeoffs
A lower temperature does not guarantee deterministic output or eliminate factual errors.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.