A benchmark is a question, not an answer
A leaderboard can narrow a search. It cannot decide whether its tasks, language, tools, grading, repetitions, and version match the work you need done.
No models were called, and no benchmark harness, grader, dataset evaluation, or application was run. The example has no scores or rankings. It shows how to inspect applicability before treating a published result as decision evidence.
Start with your decision, not the table
Suppose a fictional team needs help turning six English-language incident notes into a short internal timeline. The output must quote the notes, preserve uncertainty, and run inside an approved text-only environment. “Choose the model with the highest overall score” skips the decision's important nouns: six source files, English text, citation fidelity, uncertainty, a short deliverable, and a constrained runtime.
Write that workload first. Then ask which published evaluation is close enough to inform the choice—and which differences still require a small local confirmation.
An applicability staircase
- DecisionName the choice the evidence is meant to support and the consequence of choosing badly.
- WorkloadRecord task shape, input modality, language, tools, output form, constraints, and failure costs.
- BenchmarkMatch those facts to the benchmark's task set instead of borrowing its broad label.
- MethodRecord harness, prompts, repeats, grader, scoring rule, model/provider configuration, and version.
- MismatchList every material difference between the published setup and your workload.
- ConfirmationDesign the smallest authorized local check for the unresolved differences. Until it runs, label it proposed.
One fictional reading record
| Question | Fictional record | Conclusion allowed |
|---|---|---|
| Relevant work? | A published suite includes English, text-based, file-producing agent tasks. | Potentially relevant; inspect the exact tasks and tools. |
| Same work? | The local decision needs source quotations and calibrated uncertainty from incident notes. | No. Similar output shape does not establish citation fidelity. |
| Same conditions? | Published harness, tool access, prompting, repeat count, and grader differ from the intended environment. | Treat the result as external evidence under named conditions, not a local prediction. |
| What next? | A proposed, separately approved check would freeze six harmless notes, an answer key, exact runtime settings, and a human review rubric. | No result yet. The proposal identifies the remaining evidence gap. |
What the methodology page establishes
Artificial Analysis documents Intelligence Index version 4.3.2 as a weighted combination of ten evaluations across agent tasks, coding, scientific reasoning, and general capability. Its page identifies a primarily text-based, English-language suite; separates multilingual, image, and speech evaluation; and discloses per-evaluation task counts, repeat counts, response forms, scoring approaches, and tool use. The same page describes general testing parameters, including different temperature treatment for reasoning and non-reasoning models, retry handling, and generally pass@1 scoring. Some components use executable checks, while others use rule-based, rubric, equality-checker, or pairwise judging. Those details make the results more inspectable. They do not make one combined score applicable to every workload.
The page is one publisher's methodology for its evaluations. This note does not assert that Artificial Analysis is neutral, reproduce its results, endorse its rankings, or evaluate its implementation.
Five questions before carrying a result forward
- Does the task resemble the decision workload at the level of inputs, tools, and deliverables?
- Are modality and language aligned, or reported in a separate evaluation?
- Could the harness, prompt, retry, context, or tool policy change the behavior you care about?
- What does the grader actually observe, and what important quality remains outside its score?
- Which mismatch needs a small local confirmation before the decision is defensible?
Download the blank benchmark-applicability worksheet →
Primary methodology checked: Artificial Analysis Intelligence Benchmarking Methodology, version 4.3.2. The applicability staircase, fictional record, questions, and worksheet are original synthesis.