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The most useful way to compare frontier AI models is to test them on representative examples of your own work, under matched conditions, and score repeated results against a clear rubric. Compare the complete setup—including tools and orchestration for multi-step tasks—not just a model’s best-looking answer. Then weigh success and error severity against latency, resource use, and cost.
What are you deciding?
Start by naming the real choice: for example, which candidate should draft recurring support replies, make a code change, extract fields from documents, or complete a research workflow. Define what a successful result looks like and which mistakes are unacceptable. Include operational requirements such as response time, data handling, and budget.
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There is no universal scoring formula or set of metric weights that suits every workflow. A minor quality improvement may not justify extra delay or expense for a low-risk task; in a high-impact workflow, a severe error may matter more than speed or price.
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How do you build a useful task set?
Use representative work, not only public benchmarks
Choose prompts and tasks that resemble the work the model will actually do. Public benchmarks can provide context, but they do not establish which model will perform best in your specific workflow. Include ordinary cases as well as uncommon cases whose failure would be costly. Ask domain and technical stakeholders to agree on intended outcomes and likely failure modes.
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Test important steps as well as the final result
For a multi-step workflow, include checks at important decision points. A task can go wrong during routing, extraction, tool use, intermediate state management, or final response generation. Looking only at the final answer can hide where a failure began. Early reviews of model outputs can also reveal missing cases or recurring errors; use those findings to improve the task set and rubric.
How do you make the comparison fair?
Run candidates on equivalent tasks with equivalent instructions, context, tools, scoring rules, safeguards, and resource budgets. Record the configuration so you can interpret the result and repeat the test:
- Model name and version.
- System prompt and task instructions.
- Context and reference materials provided.
- Relevant reasoning or sampling settings.
- Tools, safeguards, and harness or orchestration.
- Retry policy and resource budget.
Keep the test close to the system you intend to deploy. For an agent that uses tools over multiple turns, the model is only part of what is being evaluated: the environment, context management, orchestration, and recovery behavior can affect the outcome. A bare chat test may not predict how the complete agent setup performs. OpenAI’s guidance on trustworthy third-party evaluations discusses the importance of describing the system and conditions behind an evaluation claim.
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Translate “good” into observable criteria. Use reference answers, required fields, functional tests, end-to-end completion checks, or pass/fail safety constraints where possible. For qualitative qualities, define scoring levels with examples and set a pass threshold before comparing results.
Pairwise comparisons can help when outputs are open-ended, but a grader can be swayed by answer position or verbosity. Model-based graders can make review more scalable; check their agreement with human judgments and audit them regularly. OpenAI’s evaluation best practices covers rubric design, pairwise comparison, grader bias, and validating graders.
What belongs on the scorecard?
| Dimension | What to record | Question to ask |
|---|---|---|
| Task success | Pass rate across repeated trials; completion of the end-to-end objective | Did it meet the agreed standard? |
| Correctness | Reference match, factual accuracy, or functional test results | Is the answer or artifact correct? |
| Instruction following | Required constraints met and prohibited actions avoided | Did it stay within the requested format and boundaries? |
| Failure severity | Error type and impact, not just the number of errors | Which failures would matter in deployment? |
| Tool and workflow behavior | Tool selection, state handling, retries, and recovery | Did the complete setup behave reliably? |
| Latency | Time to complete the task | Is it fast enough for this workflow? |
| Cost and resource use | Tokens, inference cost, and cost per task or successful completion | Is the result worth the resources consumed? |
| Robustness | Performance on routine cases, edge cases, and repeated trials | Does it hold up beyond the easiest examples? |
These are comparison dimensions, not a ranking formula. Decide which are hard requirements and which can trade off against one another in your use case.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why repeat trials and inspect failures?
Generative model outputs vary, so a single run—especially the best run—can give a misleading picture. Repeat important tasks and report how often each candidate meets the success threshold. Anthropic’s guide to evaluating AI agents describes each attempt as a trial and explains why multiple trials help account for variability.
Inspect transcripts or traces where available. Check whether the task is solvable and the grader is correct; if every candidate fails, the test itself may be broken. Also test both when a behavior should occur and when it should not, so a system is not rewarded for using a tool or taking an action unnecessarily. Look for shortcuts in the prompt, task, scorer, or harness that could distort the result.
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How should you interpret the result?
An evaluation supports a conclusion about the tested system under the tested conditions. It does not prove broad superiority across other prompts, product contexts, settings, or tool environments. OpenAI’s discussion of contextual business evaluations makes the case for realistic examples and expert review because broad evaluations cannot capture every workflow-specific nuance.
Use your task set to choose the model and setup that meet your requirements—not to declare a universal winner. Revisit the comparison when the workflow, model version, tools, or operating constraints change.
Can you use OpenAI’s Evals platform to compare external models?
OpenAI’s documentation describes evaluation of selected third-party models and custom endpoints through its Evals platform. The documented route has eligibility and administrative setup requirements; requests send data to third parties under different terms and weaker safety guarantees, and tool calls are not supported in that external-model evaluation flow. The documentation states that Evals will become read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. These availability details are time-sensitive; check the current external-model evaluation documentation before relying on the platform.
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