Temperature: The Softmax Knob Between Determinism and Chaos
Temperature is one dial placed between the model's scores and its word choice. Low temperature squeezes the outcome lottery toward the top pick — great for facts, code, and JSON. High temperature flattens the odds — creative, but error-prone. Here is what the knob actually touches, where it misleads, and the 2024–2026 findings on when temperature matters (and when it silently hurts reasoning models).
01.The Problem: Same Prompt, Different Answers
Ask a chatbot the same question twice.
You often get two different answers.
Now ask an AI to pull an invoice total into JSON. Twice.
You want the exact same bytes every time.
Both jobs run on the same machine. At every step, the model gives a raw score — a logit — for every candidate next word in its vocabulary. Something has to turn those scores into a pick.
Who decides which word gets picked? And can we dial that decision-maker up or down?
Yes, and the dial has a name: sampling temperature.
You set it as a number in an API call. That one number decides whether your model behaves like a careful accountant or like a jazz improviser.
So the question becomes
What does this knob physically change inside the math — and what does it NOT change?
The second half is where most people get burned.
One Dial, Three Regimes 🌡️
One Dial, Three Regimes 🌡️
Temperature rescales logits before softmax: p_i ∝ exp(z_i / T). Small T sharpens toward the argmax; large T flattens toward uniform.
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