---
language: en
title: "Non-determinism in LLMs"
description: "Non-determinism in LLMs is the variation in outputs under seemingly identical inputs. Learn how to compare responses and distinguish variation from factual errors."
canonical: "https://tropk.ai/en/resources/glossary/non-determinism-in-llms"
last_modified: "2026-10-12T16:13:30.800Z"
---

# Non-determinism in LLMs

Reviewed on 2026-10-06.

## What it means

Non-determinism in LLMs is the possibility of getting different outputs when repeating a seemingly identical request to the same model. Variation can arise from probabilistic token selection and execution conditions that the user does not control. Therefore, identical text and visible parameters are not enough to ensure identical responses.

## Why it matters for marketing

For marketing, this property limits conclusions drawn from a single query about a brand. A recommendation may appear in one run and disappear in another, even if the website has not changed. When evaluating content or vendors, the team needs to distinguish a trend observed across repeated runs from a one-off fluctuation, while keeping a record of the conditions compared.

## Example in practice

An agency tests a classifier for shipping questions using the same model, instruction, and input message. In ten hypothetical runs, the message receives two different labels. Before automatically routing customers to different queues, the agency decides to measure how often this disagreement occurs and route unstable cases for review, since repeating the request did not ensure consistency.

## How to apply or verify

To verify, keep the model version, full [prompt](/en/resources/glossary/prompt), tools, and available parameters fixed. Save all outputs, including those that disagree, and compare both the text and the final decision. If external search is involved, also record the retrieved documents: changes to these inputs mean that all divergence cannot be attributed to execution non-determinism.

## Important distinctions

Zero temperature can reduce randomness in token selection, but it does not guarantee identical reproduction on every infrastructure. Thinking Machines demonstrated effects related to batch size in a studied implementation, not a proven cause for every service. Non-determinism also does not mean factual error: different responses may be correct, while identical responses may repeat an error.

## Sources and references

- [Defeating Nondeterminism in LLM Inference](<https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/>) — Thinking Machines Lab

## Continue a leitura

- [Inference](</en/resources/glossary/inference>)
- [LLM](</en/resources/glossary/llm>)
- [Temperature in LLMs](</en/resources/glossary/temperature-in-llms>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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