Choose for the task. Evaluate for your users.
A practical framework for building a model shortlist without chasing a single benchmark.
PrismHub editorial·5 min readStart with a representative workload
Collect examples that reflect what your application actually sees: short and long inputs, ambiguous questions, common languages, and failure cases. Remove sensitive data before using the examples in evaluation.
Define what a good answer means
Use task-specific acceptance criteria. For extraction, measure schema validity and field accuracy. For code, run tests. For document questions, check that the answer is supported by the supplied sources.
Compare the complete experience
Measure end-to-end latency, answer quality, and cost per successfully completed task. A low token price is not automatically a lower workload cost if the application needs more retries or longer prompts.
Make selection a repeatable process
Keep prompts, model versions, input sets, and scoring criteria together. Re-run the evaluation when any of them changes, and keep an existing version available while you test a replacement.