Compare AI models on supported answers, critical errors, review effort, cost and latency. Keep a repeatable test set before committing or switching.
There is no single best model for every business task. Model choice still matters: a candidate may fail on your document types, misunderstand a tool, take too long or lack an acceptable deployment option. The practical question is which complete configuration meets your requirements with a cost and review burden you can support.
Define failure before comparing scores
Consider an illustrative assistant that drafts answers from approved supplier terms. A useful response must use the applicable source, preserve dates and quantities, and leave unanswered questions unresolved. It must not invent a delivery promise or disclose material the requesting user cannot access.
Separate required controls from qualities you can trade off. A prohibited disclosure cannot be balanced away by a nicer writing style. Decide which failures prevent release before looking at an average score.
Construct examples from the actual work, with permission to use their data. Include an absent answer, conflicting versions, unusual units and a question that requires more than one source. Record the expected answer or acceptable range and the evidence that supports it.
Compare configurations under known conditions
Use the same task set and document snapshot for each candidate. Record the model version, prompt, retrieval setup, available tools and relevant settings. If you tune each candidate separately, document that tuning effort and then assess both on examples that were not used for tuning.
The following figures are fictional and illustrate how a decision can differ from a headline score:
| Result on 100 test cases | Candidate A | Candidate B |
|---|---|---|
| Answers meeting all task criteria | 92 | 89 |
| Prohibited-disclosure cases | 1 | 0 |
| Illustrative model charges | $8 | $12 |
| Recorded checking and correction time | 180 minutes | 100 minutes |
Under a rule that any observed prohibited disclosure blocks release, A cannot be selected as-is despite its higher overall acceptance count. B’s zero observed disclosures do not prove that it will never disclose information; more access tests and appropriate controls are still needed.
Dividing model charges by accepted answers gives about $0.087 for A and $0.135 for B, before staff time and other operating costs. This calculation explains one component of cost. It does not make A acceptable or establish that either candidate is ready for production.
Check the outcome and the variability
Anthropic’s guide to evaluating AI agents distinguishes test tasks, repeated attempts, grading and the resulting system state. It also describes combining automated and human assessment. That distinction is useful when a workflow can write data: a convincing final message does not prove that the intended record was updated correctly.
Repeat important cases where outputs vary. Inspect the actual saved record or action result as well as the response. Calibrate any model-based scoring against human judgements on representative examples; a second model’s approval is not independent proof of correctness.
Measure waiting time and retries under realistic load. Record unsupported answers and human corrections separately, so a fast but frequently rejected draft does not look like completed work.
Treat a model change as a release
Keeping business rules, source data and tool interfaces separate from a model can reduce migration work. It does not remove it. Prompts, tool arguments, supported formats, context limits and contractual data handling can differ.
Before a switch, rerun the acceptance set and relevant access checks. Compare changed failure cases, confirm the new operating terms and define how to return to the earlier configuration. If the system writes to another application, include those writes in the test rather than evaluating text alone.
The business owner should approve the acceptable result, data boundaries and spending limit; a technical provider can run the comparison. An AI strategy review should produce that evidence, while an AI workflow assessment can establish which parts of the surrounding system also need work.
