Define your evaluation set
Collect prompts that represent the actual application: simple questions, long questions, ambiguous requests, retrieval-grounded answers, multilingual tasks and cases requiring abstention. Include typical and difficult examples.
Define success before comparing providers
For each prompt, record expected behavior. The output may be a factual answer, a correctly formatted object, a tool action proposal or a clear refusal. Review results consistently and keep a human evaluation component for subjective tasks.
Measure operational fit
| Measure | Why it matters |
|---|---|
| Task success rate | Users care about correct outcomes, not token counts |
| First-token latency | Affects perceived responsiveness in chat |
| P95 completion time | Exposes slow edge cases |
| Cost per successful request | Includes errors, retries and prompt context |
| Tool argument validity | Critical for agents that invoke application functions |
| Citation accuracy | Important when answering from documents |
Compare at least two configurations
Try a lower-cost baseline and a higher-capability option. If the cheaper model clears your acceptance threshold, it may be the better production choice. Keep a fallback strategy for provider outages if service continuity is important.
Re-test after upgrades
Model behavior and provider pricing can change. Pin versions where possible, maintain a regression suite, and review release notes before switching your default.
Also read: Best LLM APIs for chat and chat API cost controls.
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