Evaluate AI models against the work you need done—not a generic ranking. Set a measurable acceptance bar, run candidates on the same representative cases, calculate cost per usable result, inspect the exact data-handling route, and test repeatability and failure recovery. The result should be a documented choice for your workload, plus explicit risks and conditions that would trigger another evaluation.
How do I compare AI models for my use case?
Start by defining the job, then compare each candidate under controlled conditions. A model that performs well on a public benchmark or in a polished demo may still fail on your inputs, output format, users, or operating constraints. NIST’s AITE program describes testing on blind, sequestered data as one way to reduce contamination and support objective assessment; its program page says the effort is in an initial phase, so availability and scope may change. Read NIST’s AITE overview.
1. Define the job and the acceptance bar
Write down what the system must do before choosing models. Include the intended users, stakes, operating context, required output, and what counts as an unacceptable failure. For example, a support-ticket classifier might need to assign one approved category, return valid structured output, and escalate uncertain cases rather than invent a category.
- Task: Describe the input and the action or output expected.
- Success criteria: Specify what must be correct, complete, grounded in source material, or formatted properly.
- Failure criteria: Identify errors that matter operationally, such as a false approval, missed escalation, unsupported claim, or invalid response format.
- Thresholds: Set minimum acceptable results for critical criteria and any required human review before seeing the comparison results.
Use examples that reflect real traffic, including common cases, uncommon but important cases, and inputs likely to expose a failure. Record where each example came from and whether it contains personal, confidential, or otherwise restricted data.
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2. Make the comparison reproducible
Give each candidate the same test inputs, expected outcomes, prompt context, available tools, and relevant configuration. Record the model and endpoint version, test date, settings, and test-data provenance. If a candidate uses a different deployment pattern—such as a self-hosted model rather than an externally hosted endpoint—record that as an operating choice, not as a difference explained by model quality alone.
Generative outputs can vary between runs. Repeat representative cases when that variability could change the decision, and preserve the outputs and run logs. OpenAI’s evaluation guide describes structured evaluations as a way to assess accuracy, performance, and reliability despite nondeterministic behavior. See OpenAI’s evaluation best practices.
3. Score outcomes, not impressions
Use a rubric tied to the job rather than asking reviewers which answer “feels best.” Depending on the task, score correctness, completeness, source-groundedness, appropriate refusal, format compliance, or successful escalation. Use known answers where they exist; use human review when judging the result requires context. Keep separate failure categories so that a high overall score cannot conceal a consequential weakness.
A labeled dataset is useful when the task has answerable ground truth. For example, Google Cloud’s documented Vertex AI evaluation workflow uses a dataset containing ground truth and batch inference output; that is a platform-specific workflow, not a requirement for every evaluation method. See Google Cloud’s Vertex AI instructions.
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What should I measure for cost, quality, privacy, and reliability?
Use the same decision record for each candidate. The measures below make tradeoffs visible without implying that every deployment has the same constraints.
| Evaluation axis | Practical measure | Evidence to record |
|---|---|---|
| Quality | Task success and categorized failures on representative examples; human review where needed | Test set, scoring rubric, run count, configuration, model or endpoint version, and date |
| Cost | Cost per accepted result for the real workload | Dated prices for the exact service configuration, input and output volume, retries, tool calls, and review effort |
| Privacy | Data use, retention, application state, deletion, region, processors, and contractual controls | Exact endpoint and service terms, organization settings, contract, and data-flow map |
| Reliability | Repeatability, latency, timeouts, rate limits, recovery, and robustness to adversarial or malformed inputs | Repeated-run logs, operating conditions, incident records, and error logs |
| Deployment fit | Integration, access, monitoring, support, and operational controls | Architecture and service documentation, ownership, and fallback plan |
OECD guidance emphasizes reviewing evaluation evidence, the test design, risks, and whether data is suitable and representative. See the OECD Due Diligence Guidance for Responsible AI.
How do I calculate the cost of an AI model for my workload?
Compare cost per accepted, usable result—not token price alone. A low-priced call can become expensive if it needs multiple retries, tool calls, or substantial human correction. Conversely, a higher-priced call may reduce review work or failure costs. No fair, current cross-provider price or performance comparison is established here; obtain dated official prices for the model and service configuration you are actually evaluating.
Build a workload-based estimate
For the same measurement period and workload, estimate:
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Cost per accepted result = (model and service charges + tool charges + retry charges + human review or correction cost) ÷ number of accepted results.
Use actual or realistic request volumes and include input and output usage, unsuccessful attempts, retries, tools, and any review required to meet your acceptance bar. Apply your own labor rates and workload assumptions; record them so another reviewer can reproduce the estimate. If your system has a latency or throughput requirement, track whether the candidate meets it rather than treating a cheap but unusably slow response as equivalent.
Price is only comparable when the dates, units, and service configuration match. Record whether a price is recurring or promotional and the exact model or endpoint it applies to. Do not substitute a general provider price for the route and settings you intend to deploy.
What should I check in an AI provider’s data-retention policy?
Follow the data through the exact route your application will use. Record whether prompts, outputs, or derived metadata are used for training; how abuse monitoring works; whether application state is stored; retention and deletion controls; access; processing region; subprocessors; and contract terms. A hosted model reached through a cloud partner may have a different processor and arrangement from the provider’s direct API. Avoid describing a setup as private or compliant based only on a model name.
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Check the actual endpoint and contract
OpenAI’s live platform documentation says API data is not used to train or improve models unless a customer explicitly opts in. It also says abuse-monitoring logs may include prompts, responses, and derived metadata, retained by default for up to 30 days, subject to exceptions and endpoint-specific application-state rules. These are provider documentation statements, not a guarantee for every customer or configuration; verify the current terms, eligibility, and settings for your account and endpoint. Check OpenAI’s platform data controls.
Anthropic documents distinct API retention arrangements, including zero data retention and HIPAA readiness, and states that when its services are used through Amazon Bedrock or Google Cloud’s Agent Platform, the cloud provider is the data processor. Confirm the precise service and contract rather than assuming that direct API terms apply to a partner-cloud route. Check Anthropic’s API and data-retention documentation.
Include the evaluation process in the data-flow review
Your test harness can create another data-sharing path. OpenAI’s external-model evaluation documentation warns that evaluation calls to third-party models pass data to third parties under different terms and weaker safety guarantees than calls to OpenAI models; it lists Google, Anthropic hosted on AWS Bedrock, Together, and Fireworks among available providers. Check which evaluation service receives test data before using real or sensitive examples. See OpenAI’s external-model evaluation guidance.
Do not confuse retention controls with differential privacy
Retention and deletion rules govern what a service stores and for how long; they are not, by themselves, a mathematical privacy guarantee. NIST describes differential privacy as a framework for quantifying privacy loss when an individual’s data appears in a dataset and discusses factors and hazards to consider when assessing such claims. See NIST SP 800-226, published March 6, 2025.
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How can I measure reliability and test failure modes?
Reliability is not just a strong score on ordinary examples. NIST’s AI Risk Management Framework page quotes ISO/IEC TS 5723:2022’s definition as the “ability of an item to perform as required, without failure, for a given time interval, under given conditions.” The conditions and time interval matter: specify the workload, service environment, and operating period relevant to your decision. See NIST’s AI Risks and Trustworthiness page.
Run normal, repeated, and adversarial tests
Test typical requests as well as edge cases, malformed inputs, adversarial prompts, and service errors. Repeat cases where response variability is relevant. Track task success, variance, latency, timeouts, rate limits, and how the system recovers from errors. For a higher-stakes deployment, add red teaming and user testing rather than relying on model scoring alone.
NIST’s ARIA evaluation-planning manual describes an approach combining model testing, red teaming, and user testing to assess an AI system’s trustworthiness. Use it to shape a holistic plan for your application, not as a universal pass/fail ranking of models. Read the NIST ARIA Evaluation Planning Manual, published September 18, 2026.
Document recovery and operational limits
Record how the application behaves when a call fails, times out, hits a rate limit, or returns an unusable result. Decide whether it retries, routes to a fallback, asks a person to intervene, or stops safely. Include these outcomes in the evaluation because operational recovery affects whether a model is dependable for the intended task.
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Choose against the requirements and tradeoffs you set—not a single aggregate score. Record why the selected option meets the acceptance bar, which risks remain, who owns them, and what changes should trigger reevaluation. Re-run the relevant suite when the model, endpoint, prompt, data, service terms, or deployment route changes.
- Confirm that minimum quality and safety requirements are met for the intended task.
- Compare cost per accepted result under the actual workload and review process.
- Confirm that the exact data route and contract meet organizational requirements.
- Check reliability under expected operating conditions and document fallback behavior.
- Keep test data, rubric, configuration, dated results, and unresolved limitations with the decision record.
Tooling and provider documentation can change. OpenAI’s evaluation best-practices page states that existing Evals evaluations were scheduled to become read-only on October 31, 2026, with the platform scheduled to shut down on November 30, 2026. Those dates are approaching; verify the live notice before depending on that platform or recommending a migration path. Check the current evaluation best-practices page.
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