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A benchmark produces a comparable number only when every model is run through the same scenarios, under the same prompt and scoring procedure, and the result is reported with enough detail that someone else could repeat it. A leaderboard score is therefore a statement about one protocol applied to selected tasks. It is not a general measure of how capable a model is.
How a benchmark turns responses into a score
Every benchmark run follows roughly the same pipeline. Each stage is a place where the final number can change, which is why two scores with the same benchmark name can disagree.
- Instances. The benchmark supplies a set of test items. Each item usually has a textual input and either a reference answer or a set of scoring criteria. Stanford CRFM’s HELM Lite describes its scenarios this way.
- Adaptation. A runner wraps each item in a prompt or task template. That template can include instructions, a system message, and in-context examples.
- Inference. The wrapped prompt is sent to a specific model under stated settings, such as the exact model identifier, the provider or access route, and the decoding configuration.
- Extraction. The raw text is parsed into the thing being scored. This may be a letter choice, a number, or a final answer pulled out with a regular expression or with the benchmark’s official evaluation logic.
- Scoring. A metric is applied to each extracted answer. The metric may be exact match, a similarity measure such as F1, or a judgment from another model.
- Aggregation. Per-item results are combined across samples and tasks into the figures a leaderboard displays. The aggregation formula is a separate design decision from the metric.
What has to stay constant
For two scores to mean something side by side, the evaluated conditions must match, or the difference must be disclosed. Stanford’s HELM framework states the principle directly: models should be evaluated on the same scenarios as far as possible, and the adaptation strategy should be controlled. A scenario is defined by its task, domain, and language, so “same benchmark” should mean the same relevant test conditions, not just the same name on a chart.
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- The benchmark and dataset release, the split used, the sampled instances, and any exclusions.
- The exact model identifier or dated snapshot, the provider or access route, and the inference settings.
- The prompt template, any few-shot examples, and any system instructions.
- Output limits, answer parsing, normalization, and postprocessing.
- The metric definition, the reference data, and, if a judge is used, the judge model and its prompt.
- The number of trials, any measured variation or uncertainty, and the aggregation method.
- The evaluation date and known limits, including possible training-data contamination and capabilities the benchmark does not cover.
Worked example: the choices in HELM Lite (2023)
Stanford CRFM’s HELM Lite, published December 19, 2023, shows how these choices look in practice for that specific release. It capped each scenario at 1,000 instances. It included five in-context examples where they fit inside the model’s context window. Multiple-choice tasks were scored directly. For short free-form answers, it used measures such as F1, which the authors describe as imperfect but meaningful for that kind of task. These were design decisions of that release, not requirements that every benchmark must follow.
Worked example: the protocol in NIST AI 800-3 (February 2026)
NIST’s AI 800-3, “Expanding the AI Evaluation Toolbox with Statistical Models,” gives a more recent and more operational example. The evaluation used Inspect AI’s choice scorer and multiple-choice solver. It accessed the test sets where they were available and randomized the order of answer choices. It ran five independent trials for BIG-Bench Hard and Global-MMLU Lite, and eight independent trials for GPQA-Diamond.
The report also included a canary string, a marker placed in the evaluation material so that later readers can check whether it appeared in training corpora. The canary helps identify and reduce contamination risk. It does not prove that contamination has been ruled out.
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Measuring several properties at once
The original HELM release, published by Stanford CRFM on November 17, 2022, measured seven metrics across its 16 core scenarios where possible: accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency. It also added targeted scenarios for specific skills and risks. The point of that design is that accuracy alone does not describe a model. The same report says its coverage was still incomplete, and it explicitly acknowledged what was missing.
The 2022 paper reported scale figures for its own run: 30 models from 12 providers, more than 4,900 evaluations, and coverage of the 16 core scenarios rising from 17.9% in previous work to 96.0% in HELM. These describe that paper’s work at the time, not the current state of model evaluation.
Mean win rate: avoiding mixed scales
HELM Lite considered averaging its metrics directly, but the authors noted that metrics can use different scales or units, which makes a plain average hard to interpret. They instead reported mean win rate: for each model, the fraction of pairwise comparisons it won, averaged across scenarios. This avoids mixing metric scales. However, a mean win rate cannot be read in isolation, and it changes when the set of compared models changes. The authors also warn against overinterpreting rankings, because the suite does not test every capability.
Mean scenario score: a different formula
HELM Capabilities, published March 20, 2025, uses a different aggregate: the mean scenario score. Its WildBench score is rescaled from a 1–10 range to 0–1 so it can sit alongside the other scenarios. The report notes that this differs from HELM Classic and HELM Lite because mean win rate depends on the comparison set and can react sharply to small score changes that flip ranks. A reader comparing aggregates across reports therefore needs to check which formula each one uses.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstall| Report | Aggregate reported | How different scales are handled | Depends on the set of compared models? |
|---|---|---|---|
| HELM Lite (December 19, 2023) | Mean win rate across scenarios | Uses pairwise wins, so raw metric scales are not averaged | Yes |
| HELM Capabilities (March 20, 2025) | Mean scenario score | WildBench score rescaled from 1–10 to 0–1 | Not stated in the report summary for this formula |
| NIST AI 800-3 (February 2026) | Not stated in the cited report summary | Not stated in the cited report summary | Not stated in the cited report summary |
When a judge model scores open-ended answers
Some tasks have no single exact-match answer, so the benchmark needs a rule for deciding whether a response is correct. HELM Capabilities used a mix of methods. It applied regular-expression extraction for MMLU-Pro and GPQA, official evaluation logic for IFEval, and multiple judge models with averaged scores for WildBench. For Omni-MATH, three LLM judges voted on answer equivalence.
The authors changed the Omni-MATH judging prompt after human evaluation of canary results suggested the original prompt could encourage hallucination when judging long incorrect outputs. This is a good illustration of why the judge prompt is part of the measurement, not a detail to skip.
The report names the practical risks. Judge outputs can contain formatting errors, which produce missing annotations or false negatives. Judges can also favor responses that resemble their own style or model family. Using several judges and averaging their results reduces some of this bias and provides fallbacks when one judge fails. It does not make the judgment infallible. A careful report names the judge models, the prompt or rubric, the aggregation rule, and any validation, rather than saying only that answers were “LLM-judged.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare two benchmark results
When two results are presented side by side, check six things before reading the gap between them as a difference in model quality:
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- Task, dataset release, and sample coverage.
- Model version and access route.
- Prompt, adaptation, and inference settings.
- Metric, answer extraction, or judge procedure.
- Number of trials and how variation was treated.
- The aggregate formula and the set of models included.
If any of these differ, the results should be labeled as not directly comparable, or the effect of the difference should be explained. This is practical guidance drawn from the HELM and NIST methodologies, not a formal universal standard.
Current status of the HELM project
The stanford-crfm/helm repository states that HELM entered maintenance mode on June 1, 2026. Its README still describes the project as an open-source framework with documentation and leaderboards. Maintenance mode is a fact about the project’s development status. It does not mean the HELM methods are invalid or that all of its resources have been withdrawn, but readers should expect less active development from here on.
The lesson for any leaderboard is the same. A rank belongs to a particular run, a particular model snapshot, and a particular date. Treat it as a dated measurement under stated conditions, not as a timeless or exhaustive verdict.
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