Does Temperature 0 Make LLM Output Deterministic?

The short answer

Setting temperature to 0 is the first thing most teams try when answers differ between runs. It helps, but it is not a guarantee of identical output.

Why differences remain

Temperature only changes how the next token is sampled. Other sources of variation sit outside that setting:

• Floating-point arithmetic. Parallel computation on GPUs can add numbers in a different order, so near-tied token probabilities can flip.
• Batching. The same request can be processed alongside different other requests, which can change low-level numerics on some serving systems.
• Model and system updates. A provider can change a model version or serving stack without your code changing.
• Everything around the model. Retrieved documents, tool results, timestamps and conversation history can all differ between runs, even when the prompt template does not.

For an agent, small early differences compound. A different wording in step two can lead to a different tool choice in step three. This is the pattern behind output inconsistency and inconsistent answers.

What to do instead

1. Pin the model version where the provider allows it, and record it with each run.
2. Constrain the output: structured formats and fixed enumerations shrink the space of possible differences.
3. Make the surrounding inputs stable: log and reuse retrieved context when you need to replay a run.
4. Test for consistency, not equality: run the same query several times and check that the decision agrees, rather than requiring identical text. This is a consistency check.
5. Cache the answer if you need exactly the same response for the same input.

When temperature 0 is still the right choice

For classification, extraction, and tool-argument generation, low temperature is sensible because you want the most likely answer. Just do not promise auditors exact reproducibility on that basis alone. The consistency failure pattern describes what happens when teams assume otherwise.