Our summary
A model that selects the right answer from a fixed list has not necessarily completed an agent's whole task. Ofox explains how to read Jev benchmark results with that distinction in mind. Its article brings together published research rather than reporting a new test of the model.
The proposed plan defines acceptable results before testing. Keep related documents and near-duplicate examples together when separating tuning data from unseen test data. Compare alternatives on the same task, including retries, fallback calls and the final result. A cheaper individual decision does not necessarily make the complete workflow cheaper.
Different tasks use different measures, so one score cannot establish a universal winner. The probability that a statement is true differs from certainty about choosing one option from a list. Ofox's downloadable kit uses hand-written sample data, not Jev results; neither the kit nor the cited studies were run here.
Key takeaways
- Use published results to choose a relevant experiment, not to approve unrelated languages, tools or workflows.
- Set thresholds with tuning examples, then test on unseen examples grouped by conversation or document family.
- Count failed attempts, fallbacks and the quality of the completed task when comparing cost and speed.