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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To choose an AI model for a real application, test candidates on the same representative examples, grade them against criteria set in advance, and compare results by task, risk, and operating conditions—not by a general leaderboard score alone. A useful benchmark is a repeatable loop: define the behavior you need, run test inputs, inspect failures, improve the system, and run the tests again.
Start by defining what the model must do
Describe the application before selecting metrics or collecting examples. Record who will use it, the inputs it will receive, the required output format, and what counts as a useful response. For instance, a support assistant might need to answer from approved documentation, return a valid escalation label when it cannot answer, and avoid inventing policy details.
Turn that description into acceptance criteria. Separate requirements the system must meet from preferences that can be traded against one another. A valid JSON response might be a must-pass condition; a warmer tone might be a preference. Define unacceptable failures as well as successful behavior. OpenAI’s evaluation guide describes evaluations as a process of specifying desired behavior, testing it, examining results, and iterating.
Set safety thresholds before testing
For applications where harmful, biased, or policy-violating output could cause meaningful harm, derive test cases from the product’s actual risks. Decide minimum acceptable safety levels before seeing candidate results, then include cases that test those levels. Google’s Gemini safety guidance recommends setting expectations before testing so the evaluation set can target the safety metrics that matter.
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Choose in advance how safety results affect the decision. A candidate that performs well on average but fails badly on a critical category may be unacceptable; Google’s guidance notes that worst-case performance can matter more than average performance for some safety tasks.
Build a representative evaluation dataset
Use real application examples where you have permission, carefully authored scenarios, or a mixture. For tasks with verifiable answers, include expected outcomes or labels. The goal is not to collect the largest possible set; it is to cover the situations the application is expected to encounter and the failures that matter.
Include ordinary use and difficult cases
- Common inputs and normal traffic patterns.
- Relevant user groups or content categories, so results can be inspected by slice rather than only as one overall score.
- Different phrasings, input lengths, and levels of detail.
- Ambiguous, incomplete, or difficult examples that expose likely failure modes.
- Relevant adversarial or safety-sensitive cases.
Keep a final comparison set separate from examples used to tune prompts or models where feasible. This held-out set makes it harder to mistake improvement on familiar examples for better performance on new ones. Google’s evaluation guidance recommends diverse, use-case-relevant evaluation data and held-out data for assurance where training overlap is a concern.
Public benchmarks can add context, but they cannot replace tests of your application. Google’s guidance warns that benchmark implementations can differ and public sets can saturate. It lists BOLD at 23,679 prompts, CrowS-Pairs at 1,508 examples, and TruthfulQA at 817 questions across 38 categories; these are counts shown on Google’s guidance page, not claims about the datasets’ original publication years.
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A grader should measure the behavior you actually care about. A convenient metric is not useful if it rewards the wrong thing.
| Output or judgment | Suitable grading approach | Watch out for |
|---|---|---|
| Exact label, required field, or schema constraint | Deterministic checks, such as string or structure validation. | A technically valid output may still be wrong or unhelpful; keep separate checks for content quality. |
| Text where closeness to a reference reflects quality | A text-similarity metric. | Different wording can be correct, while similar wording can still be inaccurate. |
| Open-ended answer quality | A written rubric; automated or model-based scoring can help if checked against human judgments. | Vague criteria and unvalidated automated judging can produce misleading scores. |
| Ambiguous or high-impact judgment | Human review, with a clear rubric where possible. | Do not treat an automated score as authoritative when the judgment is difficult to automate reliably. |
OpenAI’s grader reference documents string-check, text-similarity, score-model, label-model, and multi-graders. Google’s responsible AI toolkit includes LLM Comparator for qualitative side-by-side assessment across models, prompts, or tunings. These are examples of available approaches, not a requirement to use a particular vendor’s tool.
Run a fair, reproducible comparison
Give each candidate the same test items, task instructions, output requirements, and application-relevant settings. If one model receives a different prompt or generation configuration, the comparison no longer isolates the model choice. Repeat runs when outputs may vary for the same prompt; Google’s safety guidance specifically notes the value of repeated trials.
Record enough detail to understand and reproduce each result. A practical run record includes:
- Model identifier or version and test date.
- Prompt and task instructions.
- Generation settings and output requirements.
- Dataset version, grader version, and run identifier.
- Results by metric and relevant data slice, not only an aggregate.
For cost, latency, context capacity, and deployment requirements, measure candidates under the workload you intend to run and document those conditions. There is no provider-neutral cost or latency protocol established by the sources cited here, so define the local method rather than presenting a result as universally comparable.
Compare candidates across the dimensions that matter
Use a small set of decision-relevant axes rather than collapsing every result into a single score too early:
- Task success and output validity: Does the model complete the requested task and meet required formats?
- Factuality or groundedness: Where relevant, are claims supported by the supplied sources or expected facts?
- Safety and policy compliance: Does it meet preset thresholds, including in high-risk categories and worst-case cases?
- Fairness across relevant groups: Do results differ materially across user or content slices that matter to the application?
- Consistency: How stable are results across repeated runs?
- Operational fit: What are cost, latency, context capacity, and deployment requirements under the intended workload?
Write down the tradeoff rule before choosing. For example, a candidate might need to pass every required format and safety check, after which task quality and operational fit can be compared. If one candidate improves one metric but worsens another, show that tradeoff explicitly instead of hiding it in an unexplained weighted average.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use failures to improve the benchmark and system
Review incorrect answers, grader disagreements, and weak slices. Determine whether the cause is a model limitation, unclear instructions, an unrepresentative test item, or a grading rule that does not reflect the intended behavior. Update the system or dataset as appropriate, retain a held-out comparison set where feasible, and rerun the same benchmark so changes remain comparable.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minutePublic scores are reference points, not a decision rule: benchmark saturation and differences in implementation can obscure how candidates will behave in your product. Your application-specific results—especially on high-consequence failure cases—should drive the choice.
OpenAI evals platform dates are changing
OpenAI’s current Working with evals guide says the Evals platform is being deprecated: existing evals are scheduled to become read-only on October 31, 2026, with platform shutdown scheduled for November 30, 2026. The guide points new users or those seeking an iterative environment toward Datasets. Platform plans can change, so verify the live documentation before building a workflow around a particular product.
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