jev| Comparison detail | Keep fixed or record |
|---|---|
| Input | Same ticket and category definitions |
| Configuration | Exact model version and output settings |
| Quality | Wrong labels, review outcomes and failed requests |
| Time and cost | Measure each complete request in your environment |
The official demo answers a narrow question
TypeSafe’s launch article shows Jev alongside GPT-5.6 Terra with default reasoning. It uses short, information-dense input and structured decisions. The publisher says that this input favors Jev. Watch it to understand output behavior, not to estimate the speed of your application.
Give both systems the same classification task
Choose the exact GPT model you use today. Give both systems the same ticket text, label definitions and other/review policy. Compare their selected labels. Do not include a long written explanation on only one side and treat the resulting time difference as a fair classification comparison.
Look at expensive mistakes first
For a support inbox, confusing a refund request with a tracking question may matter more than missing a rare catch-all category. Read disagreements and count errors by category. Keep transport failures separate from wrong labels so a failed request cannot disappear from the report.
Bring paired results into the bench
Export one row per ticket with id, expected, predicted and predicted_b. Put one system in each prediction column, using the same IDs. Evaluation Bench shows exact-label agreement and the rows where the systems differ. It does not call either model or measure cost and latency; record those during your runs.
Example result file
id,expected,predicted,predicted_b ticket-001,shipping,shipping,shipping ticket-002,billing,returns,billing