Five fresh runs are a useful first check, not a proven universal threshold for comparing language models. Repeating the same evaluation can show whether a result changes from run to run, but five repetitions cannot make a weak benchmark representative, account for dependent questions, or test whether different prompt wording changes the outcome. Treat five as a screening step; gather more evidence when the difference is uncertain or the decision matters.
What a “fresh run” can tell you
Language-model outputs can vary across repeated calls, including in settings intended to be deterministic. The Alan Turing Institute summarizes research noting that some models may not return the same answer even at temperature zero with a fixed random seed: its uncertainty research summary. A fresh run should therefore mean a new, independent call under the same declared conditions, with no earlier output fed into the next call.
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Repeating an evaluation helps reveal run-to-run variation. It does not answer every question about whether one model is better. Anthropic frames a central benchmark concern this way: “Is the difference in capabilities real, or could one model simply have gotten lucky in the choice of questions on the benchmark?” Its guidance on statistical approaches to model evaluations recommends reporting standard errors, accounting for dependent questions, and using power analysis to plan evaluation size.
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Why five is a screen, not a guarantee
Five repetitions may expose obvious instability, but the sources do not establish five as a validated cutoff that makes comparisons reliable in general. A Turing-linked 2025 paper examines repeat counts from one to 30 to study benchmark means and prediction intervals; that range supports examining repetition, not treating five as a universal answer: the uncertainty-quantification paper.
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Whether five is enough depends on the metric, the amount of run-to-run variation, the size of the apparent difference, the test design, and the consequences of choosing incorrectly. Anthropic advises choosing question and repeat counts in relation to the desired comparison and statistical power. Without a target effect size, variance estimate, and evaluation design, there is no defensible universal count.
How to run a fair comparison
- Choose the decision and metric first. Define the intended task, what counts as success, and how results will be scored before inspecting outputs. If practical fit matters, state whether quality, cost, latency, or a combination determines the choice.
- Keep conditions aligned. Give both models the same item set, task instructions, scoring method, and declared generation parameters. Record the provider, model identifier, date, and relevant settings, and retain raw outputs.
- Match items across models. Score both models on the same questions so differences in item difficulty do not masquerade as differences between models. If several questions come from one passage, customer, or scenario, account for that clustering instead of assuming every answer is independent; Anthropic discusses this issue in its evaluation guidance.
- Repeat independently and report the spread. Use the same prompt and settings for each fresh call. Show per-run results, a central estimate, and an uncertainty interval or another justified uncertainty measure. A single best run is not a model’s typical performance.
- Test prompt wording as a separate factor. Repeated calls with a fixed prompt estimate run variation; they do not show whether a meaning-preserving rewording changes the score. ReliableEval treats prompt resampling as a distinct source of evaluation variation and reports substantial prompt sensitivity among tested frontier models: the ACL Anthology record. If prompt robustness matters, vary equivalent wording deliberately and report those results separately.
- Increase sampling when uncertainty could change the decision. If the estimated gap is small, uncertainty intervals overlap substantially, or the choice carries significant cost, collect more runs, more representative items, or both. Power analysis can help plan the effort for a defined target comparison.
Keep benchmark accuracy separate from generalization
A model’s score on a fixed benchmark describes its performance on those particular items. It is a different claim from predicting how it will perform on similar, unseen tasks. The National Institute of Standards and Technology’s 2026 report distinguishes benchmark accuracy from generalized accuracy and discusses statistical modeling of uncertainty and item difficulty. It evaluated 22 API-access frontier models on three popular benchmarks; those figures describe that report’s scope, not the whole current model market.
For a fixed-set comparison, explain that the conclusion applies to the tested items. To make a claim about future similar tasks, use an inference method that accounts for how items were sampled and state its assumptions. Repeating calls on the same benchmark does not substitute for testing a broader, representative set of tasks.
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What to include in the result
- The model identifiers, provider, evaluation date, prompts, generation settings, item set, metric, and scoring method.
- Per-run scores alongside the central estimate and uncertainty measure.
- Whether uncertainty refers to repeated calls on the fixed items, variation across items, or both.
- Any grouping or dependence among questions, and how it was handled.
- Whether prompt wording was varied; if so, report that experiment separately from fixed-prompt repeats.
- Observed cost and latency, when operational fit matters, with the measurement conditions stated.
- The scope of the conclusion: results on this benchmark, or an estimate intended to generalize to similar tasks.
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