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A language model can give different answers to the same prompt, even when you set temperature to zero. But whether that counts as “different” depends on what you measure: the exact wording, the parsed answer, or whether the answer is correct. The available evidence explains how to test repeatability, but does not establish the prompt set, models, measurements, or hypothesis behind the first-person experiment implied by this topic. The published results below are therefore attributed to their studies, not presented as that experiment’s findings.
What does it mean for an LLM to be deterministic?
In ordinary use, people often call a model deterministic if repeating a prompt produces the same answer. That shorthand hides several distinct tests:
- Exact-string repeatability: Are the returned characters identical?
- Answer repeatability: After parsing or normalizing the response, does it convey the same answer?
- Outcome repeatability: Does the answer produce the same result on the task, such as passing the same code tests?
- Correctness: Is the answer right? A model can repeat the same wrong answer, or vary while remaining correct.
A report that says only “the model was consistent” is hard to interpret unless it states which of these it measured. Different wording can fail an exact-string comparison while preserving the same parsed answer. Conversely, repeated answers that look similar may differ in a consequential detail.
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Text generation involves sampling choices, and a hosted model’s behavior can also depend on the service configuration handling a request. OpenAI says Chat Completions are non-deterministic by default, meaning outputs may differ between requests. Its API documentation describes a seed as a way to make results mostly consistent, not as a guarantee: the prompt and other request parameters must match, and a small chance of variation remains even when the seed and backend fingerprint match. OpenAI’s advanced-usage documentation and its seed cookbook explain these controls.
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The system_fingerprint is a response value that can help identify backend configuration changes that may affect output. It is useful context for interpreting a repeatability test; it does not make outputs identical. If the fingerprint changes between runs, that is a possible explanation to investigate, not proof that it caused a particular difference.
Does temperature 0 make an LLM deterministic?
No—not for hosted APIs in general. Temperature zero can reduce variation, but it does not establish that every repeated request will return identical output. A 2023 code-generation study found that temperature zero did not guarantee repeatability. A 2025 study of hosted models also reported variable output at temperature zero through APIs. These findings apply to the studies’ tested tasks, models, and conditions, not every provider or deployment. The 2025 Eval4NLP paper and the 2023 study by Liu and colleagues examine different settings and should not be treated as universal estimates.
For scale, Liu et al. reported that zero equal test output across different requests occurred for 75.76% of CodeContests tasks, 51.00% of APPS tasks, and 47.56% of HumanEval tasks in their comparison. Those figures describe 829 coding problems under that paper’s conditions; they are not the share of all prompts or all LLMs that are non-deterministic.
What have studies measured about repeatability?
The 2025 Eval4NLP study selected four subtasks each from BBH and MMLU and tested five models. It separated agreement in raw responses from agreement in parsed answers, because surface wording can differ even when the parsed answer matches. For two unstable conditions, the researchers ran 20 few-shot trials. That design illustrates why a repeatability claim needs its task, comparison method, and number of runs—not just a model name.
Repeatability is not a substitute for accuracy. A model may agree with itself across runs yet be wrong, while a model with lower exact-string agreement may still produce the correct parsed answer repeatedly. The 2025 paper reports agreement metrics separately from accuracy measures, reinforcing that the two questions should be evaluated independently.
How to measure prompt consistency
A useful evaluation starts by defining what counts as “the same” and keeping request conditions visible. OpenAI’s model optimization guide recommends establishing a baseline with evaluations on data representative of real inputs, then measuring and iterating.
- Choose representative prompts. Include the kinds of inputs the system will actually receive, not only easy or hand-picked examples.
- Freeze the request. Record the exact prompt and context, model identifier or snapshot where available, provider and API version, sampling settings, output limit, and seed if supported. Keep these parameters the same across repeats.
- Record backend information. Save the system fingerprint when the provider returns one, so configuration changes can be distinguished from repeated requests under an apparently matching setup.
- Repeat each request. State how many runs were made and under what conditions. One repeat is not enough to characterize variation; choose a run count appropriate to the reliability question and report it rather than implying a universal threshold.
- Score at multiple levels. Compare exact strings, normalized or parsed answers, and task correctness separately. For code, for example, test execution can answer a different question from text equality.
- Report the distribution. Show agreement or outcome rates across runs, along with the prompt set and conditions. Do not select one preferred response and treat it as representative.
- Change one factor at a time when diagnosing variation. Compare settings such as temperature or seed while holding other request details constant; note model or backend changes that make runs incomparable.
If comparing prompt versions or models, apply the same prompts, repetitions, parsing rules, and task scoring to each. Useful results include exact-text agreement, answer-level agreement, accuracy across runs, and sensitivity to changes in settings. Keep conclusions limited to the evaluated prompts and environment: the cited studies do not establish a single industry-wide percentage for deterministic LLM output.
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For OpenAI API requests that support the seed parameter, using the same seed and keeping the prompt and other request parameters unchanged can make outputs mostly consistent. OpenAI explicitly cautions that determinism is not guaranteed, even when the system fingerprint matches. A seed is therefore a reproducibility aid, not a promise of byte-for-byte identity. This API guidance should not be generalized to every ChatGPT interface, model provider, or local inference setup.
What the available evidence cannot establish
The studies and provider documentation establish that repeated outputs can vary and offer ways to measure or reduce that variation. They do not supply the underlying data for a particular author’s first-person experiment: its prompts, model versions, run counts, settings, scoring rules, measured values, or original hypothesis. Without those details, it is not possible to say what that experiment found or what hypothesis it overturned. The defensible conclusion is narrower: determinism depends on the comparison being made, and even settings intended to reduce randomness do not guarantee identical hosted-model outputs.
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