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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteLLM outputs are not reliably identical from run to run, and setting temperature to zero does not guarantee otherwise. But the answer depends on what you call “the same”: the exact text, the parsed answer, or whether the model gets the task right. The available evidence supports those distinctions; it does not establish the measurements or hypothesis implied by a first-person experiment headline.
Why can the same LLM prompt produce different answers?
A prompt is only one part of an API request. Sampling settings, model and provider versions, and changes to the provider’s serving infrastructure can all affect the response. OpenAI says Chat Completions are “non-deterministic by default,” meaning outputs may differ between requests. Its documentation also identifies a system_fingerprint that can help track backend configuration changes that may affect results. OpenAI’s advanced-usage documentation and seed-parameter cookbook describe these controls for OpenAI’s API; they are not universal guarantees about every provider or model.
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Even when two responses differ, the practical significance depends on the task. “The capital is Paris” and “Paris is the capital” are different strings but the same answer. For code generation, two different programs might both pass the same tests—or one might fail. A useful repeatability claim therefore needs to say what was compared.
Does temperature 0 make an LLM deterministic?
No. Temperature zero may reduce variation in some settings, but published studies show it is not proof of identical outputs from hosted models.
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- A 2025 Eval4NLP study reported variable output at temperature zero through APIs. It examined four subtasks each from BBH and MMLU across five models. The authors distinguished agreement on raw responses from agreement on parsed answers, and ran 20 few-shot trials for two unstable conditions. Read the Eval4NLP paper.
- A 2023 code-generation study found that temperature zero reduced non-determinism compared with temperature one but did not guarantee repeatability. In its comparison across 829 coding problems, 75.76% of CodeContests tasks, 51.00% of APPS tasks, and 47.56% of HumanEval tasks had zero equal test output across different requests. Those figures describe that paper’s benchmarks and conditions, not the expected rate for all prompts or models. Read Liu et al.’s study.
These findings do not establish a universal percentage of prompts that are deterministic. The results are bounded by the models, tasks, settings, and environments the researchers tested.
Will a fixed seed make ChatGPT return the same answer?
A seed can make outputs more consistent in supported API workflows, but it is a best-effort control, not a promise. OpenAI recommends reusing the same seed while keeping the other request parameters unchanged, including the prompt and temperature. It cautions that outputs can still differ even when the seed, parameters, and system fingerprint match. The fingerprint is useful as a signal of backend changes, not as a guarantee that identical fingerprints force identical text. See OpenAI’s seed guidance.
This guidance concerns API requests and should not be assumed to describe every consumer chat interface. Other providers may expose different controls or none at all. A report should name the provider and interface rather than treating “ChatGPT” or “LLM” as interchangeable with a particular API configuration.
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State the comparison level before reporting a result. A model can score poorly on exact-string agreement while producing the same parsed answer every time; it can also repeat an answer consistently and still be wrong.
| Measure | What counts as a match | What it tells you |
|---|---|---|
| Exact raw-string agreement | The response text is identical, character for character. | Strict textual repeatability; even harmless wording or formatting changes count as differences. |
| Normalized-text agreement | Responses match after specified normalization, such as trimming whitespace or standardizing formatting. | Whether superficial formatting changes explain some string differences. The normalization rules must be stated. |
| Parsed-answer agreement | The answer extracted according to a stated parsing rule is the same. | Whether the substantive answer is stable despite differences in phrasing. |
| Task outcome or correctness | Each response satisfies a task-specific scoring rule, such as passing a test or matching an accepted answer. | Whether outputs perform reliably—not whether the text repeats. |
The 2025 Eval4NLP study explicitly treats raw-response agreement as stricter than parsed-answer agreement. Its distinction matters whenever alternate wording can preserve the same answer.
How to measure prompt consistency
Use a repeatable procedure and publish the conditions alongside the result. OpenAI’s optimization guidance recommends establishing a baseline with evaluations using data representative of real inputs, measuring performance, then tuning and repeating the cycle. See the model optimization guide.
- Define the question. Decide whether you care about identical wording, stable answers, or task success. Choose the comparison and scoring rules before looking at the results.
- Choose representative prompts. Use inputs that reflect the tasks you expect the system to handle, not only examples that are easy to score or likely to produce stable answers.
- Record the complete request context. Save the exact prompt and context, model identifier or snapshot where available, provider and API version, sampling parameters, output-length limit, seed if supported, and system fingerprint if returned.
- Repeat identical requests. Keep the recorded settings fixed for each comparison and report the number of runs. The number needed depends on the question; do not imply that a handful of trials establishes a universal rate.
- Score each level separately. Report exact-text agreement, parsed-answer agreement, and task correctness as distinct measures where relevant. Show the distribution across runs rather than selecting one response as representative.
- Compare changes one at a time. If testing a different prompt, model, temperature, seed, or backend, label the comparison clearly. Otherwise, a changed output cannot be attributed to a specific change.
- Repeat after tuning. Measure the revised setup against the same baseline and representative evaluation data so gains and regressions are visible.
For comparisons among models or prompt variants, report the tested tasks and input distribution alongside agreement, accuracy across runs, and variability. Benchmark-specific percentages should not be turned into rankings that claim to cover all LLMs.
What can be concluded about an individual experiment?
Published studies and provider documentation establish that repeated outputs can vary, that temperature zero and fixed seeds do not guarantee identical results, and that the chosen agreement metric changes what “consistent” means. They do not establish the results of an unspecified first-person experiment: without its prompts, models, run counts, settings, scoring rules, and measurements, no particular hypothesis or outcome can be verified. Treat any claimed experiment result as unsubstantiated unless those details and data are available.
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