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LLM benchmark settings are the choices that shape a model’s input, generation, and scoring conditions. For a fair comparison, keep the exact model version, prompts, decoding controls, output limits, evaluation code, and execution environment constant; change only the setting you are studying. Use a seed and temperature of zero when they suit the task and are available, but do not treat them as guarantees of identical results.
Which LLM settings should I fix during benchmarking?
Fix every setting that could affect the input, output, or score, not just temperature. A controlled comparison needs the same model identifier and version, prompt content and formatting, decoding configuration, task context, output limits, dataset and evaluation procedure, and relevant runtime details for each model or condition being compared.
That does not mean every benchmark should use one universal configuration. A benchmark of default user-facing behavior may call for different generation settings from a classification-style evaluation, while a creativity study may intentionally allow more variation. The key is to define the configuration from the research question, apply it consistently, and disclose it so readers can interpret the result.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Model and serving: record the model name or identifier, version or snapshot, provider or hosting environment, serving route if known, and experiment date.
- Input: preserve the prompt text, system and developer instructions, chat template, formatting, few-shot examples, their order, conversation history, and preprocessing.
- Generation: record temperature, seed where supported, top-p, top-k or other exposed sampling controls, maximum output tokens, stop tokens, and any other relevant generation options.
- Evaluation: preserve the dataset version and split, item order or randomization procedure, postprocessing, output normalization, metric definitions, and evaluator implementation.
- Execution: record the API or hosting details, inference library and version, batch size, hardware, and weight precision where applicable.
- Uncertainty: report independent repetitions and score variability, and distinguish variation across repeated calls from uncertainty caused by the benchmark sample.
The 2025 evaluation guidelines for empirical LLM studies and the Hugging Face reproducibility guide both emphasize that a result depends on a broader setup than a model name and one decoding value. The exact list can vary with the evaluation, but recording the relevant pieces makes comparisons easier to audit and repeat.
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Does temperature 0 make an LLM deterministic?
No. Temperature zero can reduce variation for many models, but it does not guarantee the same answer on every call. Robert E. Blackwell, Jon Barry, and Anthony G. Cohn state that “not all models give deterministic answers” even with temperature zero and a fixed seed in their work on reproducible LLM evaluation.
Temperature is a decoding control: it affects how the model selects among possible next tokens. Lowering it can make generation less variable, which is useful when a benchmark is intended to assess a consistent answer rather than a range of creative outputs. But an identical setting does not necessarily freeze the entire serving process. Numerical rounding, routing, serving infrastructure, or a silent model update can still affect results, as the Alan Turing Institute study on uncertainty in benchmark scores explains.
Use temperature zero as a control when it matches the task and the interface exposes it. Do not assume it is inherently the “correct” benchmark value. For tasks intended to reflect ordinary user behavior, use the relevant documented configuration; for open-ended generation, a nonzero value may be part of what the benchmark is measuring. State why the chosen value fits the question, and keep it fixed across conditions unless temperature itself is under study.
If repeated calls at temperature zero differ, retain and report that variation rather than selecting a preferred output. If they do not differ in a small sample, that still does not prove the system is deterministic under every condition. The Alan Turing Institute study on uncertainty in benchmark scores treats repeated outputs and benchmark scores as uncertain observations, not guaranteed fixed measurements.
Should I use the same seed for every run?
Use a consistent seed policy wherever the model or provider supports seeds, and report the values used. A seed can improve reproducibility, but it cannot completely remove nondeterminism; the 2025 empirical guidelines explicitly caution that seeds do not fully mitigate it.
For a controlled comparison, decide in advance whether each condition will use one fixed seed or a planned set of seeds, then apply that policy consistently. Record whether the seed was accepted by the interface and any other generation controls paired with it. Some services do not expose a seed, and seed behavior can differ by system, so “same seed” should be reported as a configuration detail rather than treated as proof that two runs share an identical random process.
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When the goal is to estimate typical performance rather than reproduce one particular generation, do not rely on one seed and one observed score as the whole result. Use repeated calls or a set of planned runs and report the spread. There is no universally best seed or universally required number of repetitions; choose based on the benchmark purpose, cost, and precision needed, and make the choice visible.
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For a controlled comparison, yes: use the same prompt content and format for each model, including instructions, examples, and example order. If prompt wording or formatting differs, the result may reflect those changes as well as the model differences.
“Same prompt” means more than reusing the user’s final question. Preserve the system and developer instructions, any chat template or role markers, formatting conventions, few-shot demonstrations, their order, and the surrounding task context. Save the actual prompt artifact or a versioned representation of it so another researcher can see what the model received. If an API applies a provider-specific template that cannot be inspected, document what is known and disclose the limitation rather than implying the inputs were identical at every layer.
Prompt formatting can have a large effect in particular tested settings. In their 2023 study, Sclar, Choi, Tsvetkov, and Suhr reported up to 76 accuracy points of prompt-format sensitivity for LLaMA-2-13B. Across more than 50 tasks and several models, they reported about 10 accuracy points on average in formatting sensitivity. These are findings from those experiments, not estimates of what every benchmark or model should expect.
The same paper’s GPT-3.5 FormatSpread analysis covered 320 formats and 53 tasks, with a reported spread as high as 56 accuracy points and a median of 6.4 points. The authors reported an average cost below US$10 per task for that analysis; this is a study-specific figure, not current API pricing or a general cost estimate. The paper on prompt-format sensitivity recommends considering a range of plausible formats because a prompt that works especially well for one model may not transfer to another.
There are two legitimate but different prompt policies:
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- Canonical-prompt comparison: choose one prompt policy before evaluation and keep it fixed across models. This asks how the models perform under that specific input.
- Prompt-sensitivity study: deliberately vary plausible prompt formats while holding other factors constant, then report the range or distribution. This asks how much performance depends on format.
Do not quietly switch between the two. In particular, repeatedly tuning prompts against the test set until one model looks better makes it difficult to tell whether the score reflects model capability or prompt selection. Set the prompt policy in advance, preserve the variants, and apply the same rule to every model.
What else can make an LLM benchmark comparison unfair?
A comparison can be confounded by any difference that changes the model’s input, the way it generates, or the way the evaluator scores it. Freezing temperature alone is not enough if the two conditions use different model snapshots, output limits, few-shot order, metric code, or runtime setup.
| Comparison axis | What to hold constant or disclose | Why it matters |
|---|---|---|
| Model and serving | Exact identifier, version or snapshot, provider or host, and date | A changing model or route can change output even when the prompt and settings are unchanged. |
| Prompt and context | Instructions, chat template, format, examples and order, history, preprocessing | Different input construction can change the task the model receives. |
| Decoding and limits | Temperature, seed, other sampling options, max output tokens, stop tokens | Generation choices and truncation can affect both response content and completion. |
| Data and scoring | Dataset version and split, order or randomization, normalization, metric and evaluator code | Different items or scoring rules can produce different reported scores. |
| Runtime | API or hosting details, inference library, batch size, hardware and precision where applicable | Serving and numerical implementation can contribute to differences or variability. |
| Repeated-run policy | Number of calls, seeds or repetition plan, and how uncertainty is summarized | A single run can conceal variation in outputs or scores. |
The Hugging Face guide to reproducibility troubleshooting specifically points to issues such as few-shot order, metrics, batch size, hardware, libraries, and precision. These details are especially relevant for locally hosted or open-weight evaluations, where the evaluator may control more of the runtime stack. For a hosted API, record the provider and the model identifier or snapshot available to you; do not claim control over infrastructure details that the service does not expose.
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When a model comparison necessarily changes several axes—for example, when each provider offers a distinct serving environment—say so plainly. The result then compares the complete model-and-service configurations, not model weights in isolation. Avoid attributing the entire score difference to the model if the benchmark setup cannot separate the effects.
How should I separate a controlled comparison from sensitivity analysis?
A controlled comparison changes the model or condition of interest while holding the rest of the evaluation setup fixed. Sensitivity analysis deliberately changes a setting, such as prompt format or temperature, and measures how much the result shifts.
For a model comparison, select the prompt, decoding settings, dataset, output limits, and evaluator before running the comparison. Apply them to every model and report any unavoidable differences. If instead the question is whether temperature changes a model’s score, run planned temperature conditions while keeping model version, prompt, data, and evaluation code constant. If the question is whether prompt formatting changes relative rankings, select plausible formats and apply the same format set across the models.
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Report the spread across planned conditions, not just the best result. For prompt sensitivity, explain how the formats were chosen and whether they were determined before looking at test outcomes. For generation variability, explain the repetition policy and whether repeats were separate calls, different seeds, or another defined condition. A clear separation prevents a sensitivity study from being mistaken for a single-score model ranking.
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There is no universal number of repetitions that fits every benchmark. The appropriate number depends on the purpose, budget, and precision needed, so state the repetition plan and show the observed variability instead of presenting one score as certain.
It helps to distinguish two kinds of uncertainty. Repeating calls on the same benchmark items helps reveal output or serving variation for those items. Resampling or changing the evaluated benchmark items addresses uncertainty about how well the result generalizes beyond that particular sample. These are different questions: repeated calls on a fixed set do not by themselves show how the score would vary on a different set of examples.
Where possible, report a mean or other chosen summary alongside an interval or distribution that makes the run-to-run spread visible, and explain what the interval represents. Do not imply that one uncertainty estimate covers every source of variation. The 2026 paper “Randomness in large language models: What researchers need to know (and report)” summarizes the principle this way: LLM outputs should be treated as draws from a distribution rather than as fixed measurements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should a reproducible benchmark report include?
A useful report lets readers reconstruct the evaluation conditions and see which sources of variation remain. The following checklist can be adapted to a paper, internal benchmark, or model card.
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- Prompt artifact: verbatim prompt set or versioned accessible artifact, including system and developer instructions, chat template, formatting, demonstrations, and order.
- Generation configuration: temperature, seed and support status, top-p/top-k or other exposed options, output-token maximum, stop tokens, and remaining relevant controls.
- Task construction: context and conversation history, input preprocessing, output postprocessing, and normalization.
- Data: dataset version, split, item ordering or randomization approach, and any exclusions.
- Evaluation: metrics, their definitions, evaluator implementation and version, and any scoring or normalization choices.
- Execution: API or host details, software and inference-library versions, batch size, hardware, and weight precision where relevant.
- Uncertainty: number and type of repetitions, score variability or interval, and a clear distinction between repeated-call variation and benchmark-item uncertainty.
If a provider does not expose a setting or version, mark it as unavailable or unknown instead of filling in an assumption. If a model identifier points to a mutable alias rather than a fixed snapshot, recording the date and provider information is especially useful, but it cannot prove the underlying model remained unchanged. These qualifications help readers judge how closely a later replication can match the original conditions.
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How do I choose settings for creative or default-behavior benchmarks?
Choose settings that match the behavior the benchmark is meant to represent, rather than automatically setting temperature to zero. A deterministic-style evaluation and a study of creative generation answer different questions.
For a consistency-focused task with a supported interface, a low-variation configuration may make comparisons easier to interpret. For a benchmark intended to represent a normal user’s creative interaction, a more permissive generation setting may be part of the behavior being measured. In either case, declare the rationale and configuration before scoring, then apply the same policy across compared conditions.
There is also a trade-off between a single canonical condition and measuring a distribution. A fixed condition gives a direct answer about performance under that setup, while multiple planned runs or formats can show how much the answer depends on sampling or prompt choices. Neither is universally superior; the benchmark question determines which is informative. The Alan Turing Institute’s study notes that determinism is not always desirable, particularly for novel or artistic outputs.
Common benchmarking mistakes and how to correct them
- Assuming temperature zero means reproducible: treat it as a variability-reducing control, not a guarantee. Repeat runs when score stability matters and report the observed spread.
- Changing prompts between models: preserve identical prompt artifacts for a controlled comparison. If prompt variation is the subject, vary it systematically and consistently across models.
- Reporting a seed as a complete reproducibility method: report the seed policy and other settings, while disclosing that the seed does not eliminate nondeterminism.
- Comparing mutable model names without a date: record the identifier, version or snapshot if available, provider, and execution date; qualify what could not be pinned down.
- Ignoring output truncation: record maximum output tokens and stop conditions so readers can tell whether a response could have been cut short.
- Changing evaluator details mid-comparison: version the metric and evaluator implementation, keep normalization fixed, and rerun all conditions if the scoring method changes.
- Calling one observed score “the model’s performance”: explain the repetition policy and uncertainty. A single score is evidence from that run and setup, not a universal fixed value.
- Selecting the best prompt after inspecting test outcomes: decide the prompt policy in advance or report the sensitivity analysis across the full planned set.
A compact protocol for a fair LLM benchmark
- Write down the question. State whether the benchmark compares models under one fixed condition, evaluates default behavior, or studies sensitivity to a setting.
- Freeze the protocol. Select the model/version policy, prompts, data split, decoding controls, output limits, evaluator, and runtime details that can be controlled.
- Version the artifacts. Save the prompt set, dataset references, evaluation code, and configuration in an identifiable version or record.
- Plan variation intentionally. Decide which factors are fixed and which are varied, plus the number and type of repetitions. Do not choose conditions based on which outcome looks most favorable.
- Run consistently. Apply the same policy to all compared models or conditions, noting unavailable controls and any service changes or execution differences.
- Summarize both result and uncertainty. Report the score, run-to-run spread or suitable interval, number of runs, and what uncertainty the summary does—and does not—represent.
- Publish the setup. Include the checklist details so readers can assess comparability and attempt a replication without guessing at hidden choices.
The practical rule is simple: fix the settings that are not the subject of the experiment, vary only the factor you intend to study, and report the remaining uncertainty. A benchmark becomes more credible not because it claims perfect determinism, but because it makes its conditions and limitations visible.
Frequently Asked Questions
Can I compare two models if one API does not support seeds?
Yes, but disclose that the seed control was unavailable and do not imply that the runs had matched random states. Use the same repeat policy you can apply consistently, and report the observed variability.
Should I use a model alias or a versioned snapshot in a benchmark?
Use a fixed snapshot or version identifier when the service provides one. If only a mutable alias is available, record the alias, provider, and run date and qualify that a later call may not access an identical model.
What if a benchmark’s prompt has to differ by model?
Then the result is not a clean comparison of models under identical inputs. Explain why prompts differ, preserve each version, and frame the conclusion as a comparison of the complete model-and-prompt configurations.
Does publishing a prompt guarantee that someone can reproduce the score?
No. Reproduction can also depend on model version, provider-side serving behavior, evaluator code, data, and runtime details. Publishing the prompt removes one important ambiguity but does not eliminate other sources of variation.
