Choose an AI model by task, not by name

Make requirements testable

“The strongest model” is not an acceptance criterion. Define observable outcomes: support answers cite a knowledge base, table extraction preserves row counts, code suggestions pass existing tests. Define failure before choosing a score.

1. Input: text, images, audio or documents? Does it include sensitive information?
2. Output: prose, schema-valid JSON, tool calls or media files?
3. Operating limits: context length, response time and a human fallback after failures.

Build a shortlist

Filter the catalog by capability and shortlist a few families, then confirm specific versions in vendor documentation. Vision understanding is different from image generation. A family label is not necessarily a valid model parameter.

On api-turbo, a candidate also needs an enabled channel and permission for your key. Catalog inclusion, gateway support and account access are separate conditions.

Create a repeatable evaluation sheet

Prepare normal, difficult, underspecified and malformed examples with expected outcomes. Keep inputs and output requirements consistent across candidates. For stochastic generation, record parameters and repeat important examples.

Record correctness, format compliance, manual corrections, time to first useful output and total duration. Different output lengths affect total duration; retain that context.

Roll out gradually and recheck

Start with a small set of non-critical tasks and track failure categories, not only averages. Rerun evaluations after changes to the ID, version or channel. Replacement models can also behave differently for tools and JSON.

This is an evaluation method, not an api-turbo benchmark. Numbers without a published dataset, date and method should not be treated as performance promises.