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To find the model that works best for your needs, test candidates on representative prompts and workflows, under matched conditions, against a clear rubric. Repeat important tasks, inspect failures, and compare quality with latency and cost. For tool-using or multi-step work, evaluate the complete model-plus-harness setup—not just a single chat response.
Start with the decision you need to make
Define the specific job before choosing models to test. Examples include drafting a recurring support reply, changing code, extracting fields from documents, conducting research, or running an internal automation. State what a successful result must do and which mistakes are unacceptable.
Include practical constraints alongside output quality. A workflow may have requirements for response time, data handling, or budget. There is no universal weighting for these factors: what matters depends on the task and the consequences of failure.
Build a test set from your actual work
Use realistic examples from the workflow wherever possible. A useful set includes routine tasks as well as uncommon cases that would be costly to mishandle. Ask the people who understand the domain and the technical setup to agree on intended outcomes and likely failure modes.
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For a multi-step workflow, include important decision points as well as the end-to-end task. Routing, extraction, tool use, state management, and final response generation can each fail. Looking only at the final result may conceal where a breakdown occurred.
Review early outputs to find missing test cases and recurring errors, then refine the task set and rubric. Public benchmarks and broad capability tests can provide context, but they do not establish how a model will perform on every workflow-specific need.
Keep test conditions comparable
Give each candidate equivalent tasks, instructions, context, tools, and scoring rules. Record enough detail to explain what the result represents:
- Model name and version.
- System prompt and other task instructions.
- Context and reference material provided.
- Sampling or reasoning settings, where relevant.
- Available tools, safeguards, and harness or orchestration.
- Retry policy and resource budget.
Keep the tested setup close to the one you intend to use. For agentic work, the harness is part of the system: the environment and orchestration can affect tool selection, task-state management, and recovery after errors. A bare model prompt may not predict how the deployed workflow behaves. OpenAI’s May 29, 2026 playbook on third-party evaluations discusses why evaluation claims should describe the tested conditions and validity checks.
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Make success observable wherever possible. Check required fields against a reference, run functional tests on generated code, or mark whether the task was completed. For qualitative qualities, define what each score means with examples and set a pass threshold; “good” alone is too vague to produce consistent comparisons.
For open-ended responses, pairwise comparisons can help reviewers judge which output better meets the task. However, graders may be influenced by output position or verbosity. Model-based graders can make evaluation more scalable, but compare their judgments with human labels and audit them regularly. OpenAI’s evaluation best practices covers grader design and validation.
Use a scorecard that covers outcomes and operating cost
Record results by dimension rather than collapsing them immediately into a single ranking. Decide which dimensions are minimum requirements and which can trade off against one another.
| Dimension | What to record | Question to ask |
|---|---|---|
| Task success | Pass rate across repeated trials; completion of the end-to-end objective | Did it complete the task to 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 follow the requested format and boundaries? |
| Failure severity | Error type and impact, not just the number of errors | Which failures would matter most in deployment? |
| Tool and workflow behavior | Tool selection, state handling, retries, and recovery | Did the complete agent 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 quality worth the resources consumed? |
| Robustness | Results across routine cases, edge cases, and repeated trials | Does it remain reliable beyond the easiest examples? |
Repeat important tasks and inspect failures
Generative AI outputs can vary between attempts, especially for open-ended or agentic tasks. OpenAI’s evaluation best practices states, “Generative AI is variable.” Anthropic’s guide to evaluating AI agents puts it simply: “Each attempt at a task is a trial.”
For tasks that matter, run multiple trials and record how often each candidate meets the threshold—not only its best result. Inspect transcripts or traces when available to see where failures occur. Test both when a behavior should happen and when it should not; otherwise, a system may appear successful by overusing a tool or action.
If every run fails, first check that the task is solvable under the supplied conditions and that the grader is correct. A broken test is not evidence that a model cannot perform the task. Also look for shortcuts in the prompt, task, scorer, or harness that might distort the comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret the result as evidence about the tested setup
A score applies to the models, settings, tasks, and workflow conditions you tested. It does not prove general superiority across other prompts, products, settings, or tool environments. OpenAI’s discussion of evaluations for business workflows likewise emphasizes contextual evaluation: broad evaluations do not capture every nuance of a specific organization’s work.
Use the results to make the decision you defined at the start. A modest quality improvement may not justify added cost or delay in a low-risk workflow. In a high-impact workflow, a severe failure mode may outweigh speed or price. Set the trade-offs to fit your use case rather than treating any one scorecard dimension as universally decisive.
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When to use an evaluation platform
OpenAI documentation describes evaluating selected third-party models and custom endpoints through its Evals platform, subject to eligibility and administrative configuration requirements. The documented external-model flow says calls pass data to third parties under different terms and weaker safety guarantees, and that tool calls are not currently supported.
The documentation states that the Evals platform will become read-only for existing users on October 31, 2026, and is scheduled to shut down on November 30, 2026. These dates and platform details may change; check the current documentation for evaluating external models before relying on that route.
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