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Yes, you can average LLM benchmark scores—but the result may not be a fair or useful leaderboard. Benchmarks can measure different abilities on different scales and under different evaluation rules. Rescaling scores helps with some differences, but choices about tests, weights, missing results, and uncertainty still shape the ranking. Treat a composite as a summary for a defined purpose, then inspect the underlying results before choosing a model.
Why a raw average can mislead
A score of 80 on one benchmark does not necessarily mean the same thing as 80 on another. Tests can differ in difficulty, scoring metric, output format, and performance expected by random guessing. Averaging their raw scores treats the numbers as if they were on a shared scale—even when they are not.
Consider a hypothetical comparison between a multiple-choice test with a substantial chance-level score and a free-response test whose chance-level score is near zero. The same reported accuracy on both tests would represent different gains over chance. A raw mean would conceal that distinction.
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There are also two separate acts of averaging. Within a benchmark, item scores may be averaged to estimate performance across a broader population of questions. Across benchmarks, those benchmark-level scores may then be combined into an overall number. The second step adds decisions about which capabilities count, how to put scores on a common scale, and how much each test should influence the result.
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Normalization helps with scale, but does not settle the ranking
The Open LLM Leaderboard’s normalization documentation describes scaling scores to 0–100 relative to random baselines, including examples for tasks with and without subtasks. That kind of adjustment can make scores easier to compare by accounting for differences such as chance performance. It is one leaderboard’s method, not a universal standard.
Even after scores are rescaled, the composite reflects choices about which tests to include, how to set baselines, how to weight results, and what to do when a model lacks a result for a test. Equal weighting after normalization gives each included benchmark equal influence by construction; it does not show that the benchmarks represent general capability equally or match a particular reader’s needs.
Historical results illustrate how much the aggregation rule can matter. In a 2026 chapter, Moritz Hardt recounts that when the Open LLM Leaderboard moved from summed accuracy to normalized scores, Qwen1.5-32B-Chat moved from rank 57 into the top 10. The same account says Qwen2-72B-Instruct ranked first under both schemes. These are historical examples from that chapter, not current model rankings.
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Before scores are aggregated, the evaluation protocol can already affect the outcome. A 2024 ACL paper by Nora Al-Twairesh, Areeb Alowisheq, M. Saiful Bari, and Haidar Khan reports that minor multiple-choice evaluation changes—including answer-choice order or selection method—shifted MMLU leaderboard rankings by as many as eight positions in their experiments. That result is specific to the paper’s experiments; it does not establish that every benchmark has the same sensitivity.
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For a meaningful comparison, check how models were prompted, how answers were selected, whether tools or an agent setup were used, and how responses were scored. Benchmark names alone do not establish that two published scores came from comparable versions, windows, or procedures.
A score is an estimate, not a certainty claim
Benchmark results come from a finite set of questions. If the goal is to estimate performance across a broader universe of possible questions, the observed average is only an estimate of that wider average. Anthropic’s November 19, 2024 statistical explainer recommends reporting standard errors and accounting for clustered questions: related questions may supply less independent evidence than the same number of unrelated ones.
Under the statistical setup described in that explainer, a 95% confidence interval can be calculated as the mean plus or minus 1.96 times the standard error. That interval concerns uncertainty under the stated setup; it does not, by itself, resolve run-to-run variation or establish that a small gap matters for a real task. The LLM Benchmarks project’s methodology guide, revised September 7, 2026, makes the practical caution that small score gaps are not established performance advantages without appropriate uncertainty analysis. That is the project’s guidance, not a formal statistical standard.
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Uncertainty estimates for individual benchmarks do not automatically justify a single cross-benchmark measure of general intelligence. A composite still depends on which benchmarks were chosen and how their scores were combined.
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Different aggregation methods answer different questions
| Method | What it does | What to watch |
|---|---|---|
| Raw mean or sum | Combines reported benchmark scores in their original units. | Unlike scales and baselines can make the arithmetic hard to interpret. |
| Normalized mean | Combines scores after rescaling them, such as relative to random baselines. | Baselines, included tasks, and weights still shape the result. |
| Mean win rate | Summarizes how often a model beats other models in the comparison set rather than directly averaging unlike score units. | Its result depends on which models are in that set; adding a weaker model can change relative averages among stronger models. |
Hardt’s 2026 chapter discusses these trade-offs, including the Open LLM Leaderboard history above. Avoid treating any one aggregation rule as assumption-free: each makes a different comparison, and none chooses the right capabilities or weights for every use.
How to judge a leaderboard for your decision
Use an overall score to shortlist models only when the leaderboard’s workload and weighting make sense for the job you need done. Before treating a small lead as decisive, inspect the model-level evidence that sits behind the composite:
- Task and target: Identify the capability or workload being measured. An expert-knowledge test, coding task, broad index, and tool-using agent task are not interchangeable targets.
- Version and date: Check the dataset or benchmark version, task window, and evaluation date. A shared benchmark name alone is not enough to establish comparability.
- Metric and baseline: Find out whether the value is accuracy, exact match, a rate, a normalized score, or a rank, and what chance or baseline performance means.
- Protocol: Compare prompting, answer selection, tools, agent setup, and scoring procedure.
- Coverage: Check which tests each model actually completed and the leaderboard’s stated rule for missing results. A composite drawn from only part of a planned suite can describe a different comparison.
- Uncertainty: Look for confidence intervals or another appropriate uncertainty analysis. Where available, distinguish uncertainty from question sampling from variation across runs.
- Aggregation and weights: Determine whether the score is a raw mean, normalized mean, weighted score, mean rank, or win-rate summary, then ask whether its weighting fits your priorities.
Match the composite to the workload
A leaderboard can be useful when its scope is explicit. Algolia’s LLM Leaderboard methodology, for example, defines a product-search workload using an agent and describes confidence intervals and statistical tiers. Its results are evidence about that workload, not a general ranking of models for every task.
For a deployment decision, test representative prompts and workflows from the application itself, alongside the leaderboard evidence. That makes the evaluation relevant to the decision without assuming that any benchmark—or a one-off application test—perfectly predicts production performance.
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