A Model Comparison Is Not Evidence Until It Survives Five Fresh Runs

A single good answer is not evidence that a model path is ready for a real workflow.

It might be a lucky run. It might benefit from a warm cache, a different hidden prompt, a temporary rate condition, or a provider-side behavior that changes on the next request.

For agent and coding workflows, I would compare provider/model paths with the same ingredients:

• the same repository or task state
• the same prompt and tool permissions
• the same context policy
• the same success criteria
• several fresh runs, not one conversation continued indefinitely

Then record more than the final text:

• resolved provider and model
• endpoint or protocol path
• time to first output and total completion time
• retries and failure class
• whether tool calls completed correctly
• provider-reported final usage
• cost per successful workflow

That last line matters. Token price is an input. A completed task is the outcome.

A route that is cheap on one request can become expensive once it needs retries, loses tool state, produces an incomplete result, or fails during a longer agent session.

For a useful comparison, preserve the raw attempt trace. If someone later asks why one path cost more or failed more often, “the model looked good once” is not an answer.

The practical question is:

Can this exact provider/model path complete this exact workflow repeatedly, with behavior you can explain?

Full disclosure: I’m building Your Model around this workflow-first way of comparing provider and model paths through one OpenAI-compatible endpoint.

What is the smallest real task you use to reject a model or provider path?