Choose models by workload, not reputation
A compact evaluation method for quality, latency, features, and cost.
The most famous model is not automatically the best model for every request. A reliable choice starts with the job your application must complete.
Define the acceptance set
Collect a small group of representative inputs, expected traits, and failure cases. Include messy real examples—not only polished demos. Score whether each output completes the task, follows the required format, and avoids unacceptable errors.
Measure the whole request
Track output quality together with time to first token, total latency, input tokens, output tokens, and failed requests. A cheaper model that needs three retries may not be cheaper in production.
Keep a small portfolio
Many applications need only two or three model classes: a fast default, a stronger model for difficult requests, and possibly a specialized coding or vision model. Start with that small portfolio and expand only when real measurements justify it.