Model Temperature Effects

Extended definition

Model Temperature Effects describe how the temperature parameter (controlling output randomness) influences which brands get mentioned and cited. Low temperature (0-0.3) makes models deterministic, consistently citing the same high-authority sources. High temperature (0.7-1.0) introduces variety, occasionally citing less prominent sources. Commercial AI search engines typically use moderate temperature (0.4-0.6) balancing consistency and diversity. Temperature affects whether you need to be the #1 authority (low temp) or top-5 (higher temp allows rotation). Understanding temperature helps calibrate authority targets and explains citation variance.

Why this matters for AI search visibility

Temperature determines how dominant your authority needs to be for consistent citations. If an engine uses low temperature, second-place authority means few citations because the model always picks first place. Higher temperature means #2-5 ranked sources still get meaningful citation share. Temperature also explains why citation rates vary between identical queries: the randomness isn't arbitrary, it's temperature-driven sampling. For visibility strategy, understanding likely temperature settings helps prioritize whether to aim for absolute dominance or competitive parity.

Practical examples

• Engine running at 0.2 temperature cites market leader 94% of time; competitor at 0.7 temperature distributes citations more evenly (leader 61%, others 39%)
• A/B testing reveals brand captures 8% citations at low temperature but 31% at higher temperature, indicating #3-4 authority positioning
• Temperature increase from 0.3 to 0.5 expands total brands cited per answer from 1.2 to 2.8 average