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Temperature vs Top-P Sampling

Temperature reshapes how sharply a model favors its top-probability tokens; top-p trims which tokens are even eligible to be sampled. They tune together, not against each other.

DimensionTemperatureTop-P (Nucleus) Sampling
What it actually controls

Rescales the shape of the probability distribution — sharper toward the top choice, or flatter toward the whole vocabulary.

Restricts which tokens are eligible to be sampled at all, regardless of how the distribution is shaped.

Behavior at extreme settings

At 0, sampling becomes fully deterministic — the model always takes its single highest-scoring token.

At a very low value, the nucleus can shrink to just one or two tokens; near 1, it barely restricts anything.

Adapting to model confidence

Applies the same rescaling regardless of how confident the model is at that step.

Wins here

The eligible set shrinks automatically when the model is confident and grows when it is genuinely uncertain.

Guarding against near-nonsense tail tokens

Even lowered, some probability mass still touches every token in the vocabulary.

Wins here

Can zero out the long tail entirely, regardless of how temperature shaped the rest of the distribution.

Interpretability of the setting itself Wins here

A single scaling factor that is straightforward to reason about directly.

Its practical effect depends on the shape of the distribution at each step, harder to predict in the abstract.

Common production default

Rarely set alone in serious systems.

Rarely set alone either — most APIs ship sensible defaults for both together (e.g., temperature around 0.7-1.0 with top-p around 0.9-0.95).

Task fit

Low values suit tasks with one right answer: code, extraction, math.

A moderate value paired with a moderate temperature suits creative or varied-phrasing tasks without inviting nonsense.

When to choose Temperature

Reach for temperature as the single dial to move between deterministic and exploratory output when rare tail tokens aren't a specific concern.

When to choose Top-P (Nucleus) Sampling

Reach for top-p when you want variety without occasionally sampling near-nonsense from the tail, especially once temperature is raised.

The verdict

This isn't really an either/or choice — they are independent dials solving different problems, and nearly every production system sets both together rather than treating them as alternatives.

Reading the trade-offs is step one.

Miatz turns comparisons like this into judgment — the free founding cohort trains you to make this call on real systems, not just recite it.