To compare AI models fairly, give them equivalent tasks, define the scoring criteria before testing, document the full setup, and examine results by task—not just as one overall score. The same prompt is a useful starting point, but differences in model versions, tools, message formats, safeguards, and evaluation conditions can still make a comparison misleading.
What a fair model comparison can—and cannot—show
Start by stating the decision you want the comparison to support. Are you testing which model follows a writing style, answers questions from a particular document set, or resists a defined attack? Each question calls for different test cases and scoring. OpenAI’s evaluation best practices and its May 29, 2026 playbook for trustworthy third-party evaluations distinguish among claims such as capability elicitation, safeguard performance, and model comparison.
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A result supports a conclusion about the models and conditions actually tested. It does not automatically establish which model is best for every task or how it will behave in ordinary use. Identical prompts alone do not control the rest of the measurement setup.
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- Define the claim. Write down the decision the test is meant to inform and the task the models must perform. Keep a capability test separate from a safety or refusal test.
- Build a representative prompt set. Include realistic examples and, where relevant, expert-written edge cases and adversarial cases. If you refine prompts or settings during development, hold back some examples so you can check the final setup on cases that were not used to tune it. OpenAI recommends combining production and domain-expert examples and including typical, edge, and adversarial cases when appropriate.
- Make the instructions equivalent. Preserve the exact task text and the order and content of system, developer, and user instructions. “Same prompt” should mean equivalent instruction context, not merely the same user message pasted into unlike setups. If a provider’s interface or API requires a different message structure, record the difference and narrow the claim.
- Record the tested configuration. Save model name and version, test date, system instructions, reasoning settings, tools and browsing access, exposed sampling settings, retries, token or time budgets, context limits, safety settings, and the surrounding harness. The harness may include the interface, control logic, memory, tools, retries, and validators—not just the prompt.
- Set the scoring rules in advance. Choose measures that match the task, such as correctness, completeness, instruction adherence, factual support, style, refusal behavior, latency, or cost. Specify how partial credit and ties work, and do not change the rubric after seeing which model performs better.
- Run and preserve the test. Keep the prompts, configurations, outputs, scoring rules, and run counts together. If answers can vary across runs, say how many times each prompt was run and how that variability was handled.
- Review the results and validity risks. Compare task-level outcomes, inspect examples, and check for broken questions, incorrect reference answers, unreliable tools, shortcuts, refusals, or other factors that could distort the score.
How to score answers consistently
Use criteria readers can see
Translate the decision into observable criteria before running the test. For a document question-answering task, for example, a rubric might separately score whether the response is correct, supported by the supplied documents, complete enough to answer the question, and faithful to the required format. The criteria should reflect the task; there is no single scoring scheme that fits every comparison.
Compare subjective outputs side by side
For open-ended answers, pairwise comparison or scoring against specific criteria can be more consistent than asking a judge for a free-form overall impression. OpenAI’s evaluation guide describes pairwise comparisons, classification, and criterion-based scoring as useful formats. If people judge the outputs, explain the rubric, how evaluators were prepared, whether they knew which model wrote each answer, and how disagreements were handled. If an automated judge is used, check a sample of its judgments against human ratings and report uncertainty rather than treating the judge as infallible.
Report the slices, not only the average
Show the overall result alongside meaningful task categories, such as routine requests, edge cases, or a particular failure type. Include representative wins, ties, and failures so readers can understand what a score means in practice. Google’s LLM Comparator is a web app with a companion Python library for exploring side-by-side results, slicing outcomes, and inspecting individual outputs.
What to record about the setup
A provider’s public model name is not a complete description of a test. Record the conditions that could affect the output, and clearly identify any you could not equalize.
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| What to document | Why it matters |
|---|---|
| Model and version, plus test date | Identifies which system was actually evaluated rather than relying on a changing product label. |
| System, developer, and user instructions | Shows whether models received equivalent instruction context. |
| Reasoning settings and exposed sampling or decoding options | Documents configuration differences that can affect responses. |
| Tools, browsing, and other access | Clarifies whether a model could retrieve information or take actions unavailable to another. |
| Safety settings and refusal handling | Helps distinguish a capability failure from a refusal or safeguard response. |
| Harness, interface, memory, control logic, retries, and validators | Describes the system around the model that shapes how prompts are processed and outputs are returned. |
| Token or time budget, context limit, and run count | Shows the resources available and whether repeated runs were used to account for variation. |
| Scoring rubric, evaluator type, and disagreement process | Explains how outputs became reported results and where judgment entered the process. |
When settings differ across providers, state exactly what differs and which conclusion the test can still support. OpenAI’s 2026 evaluation playbook explains why a standardized harness helps readers attribute score differences to systems rather than changes in measurement conditions; standardization is useful when the report explains why those fixed conditions fit its claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validity problems that can distort a comparison
- Unrepresentative prompts: A small or narrow set may not reflect the work the result is supposed to describe. Public benchmark items may also be familiar to a model, so apparent success might not reflect performance on unfamiliar tasks.
- Broken or ambiguous tasks: Questions without a sound answer, incorrect reference answers, or unclear instructions can measure the flaws in the test rather than model ability.
- Shortcuts and scoring artifacts: A model may exploit a pattern in the evaluation or earn credit for a superficial feature that is not the capability the test intends to measure.
- Refusals: If the goal is to measure capability, a refusal may prevent that measurement. If the goal is to test safeguards, the refusal may be part of the behavior being evaluated; define which interpretation applies.
- Unequal access or familiarity: Different tools, interfaces, or message formats can affect results. In a pilot with Anthropic, OpenAI noted that differences in access and familiarity made exact apples-to-apples comparisons difficult, and it excluded developer-message tests where message structures differed.
- Overgeneralizing from adversarial tests: A deliberately difficult attack set is not necessarily representative of real-world behavior. OpenAI’s pilot report also cautions that methodological inconsistencies can make sweeping conclusions inappropriate.
- Run-to-run variation: If a prompt produces different answers on repeated runs, a single output may give a misleading impression. Report the repeat policy and monitor variability as the evaluation set grows; OpenAI recommends continuous evaluation.
Make these limitations visible alongside the results. A benchmark score is evidence about the tested tasks, configurations, and scoring method—not a universal ranking of AI models.
Quick Recap
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What to include in a useful report
- The decision or claim the comparison addresses.
- The prompt set’s scope and how its examples were selected.
- Model versions, test date, configuration, tools, and any setup differences.
- The scoring criteria, evaluator method, and treatment of ties or partial credit.
- Overall results plus task-level breakdowns and illustrative outputs.
- Run counts and how variability was handled.
- Validity risks that limit the conclusion, including refusals, possible contamination, or unequal access.
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