JevBench by Benchmark Heaven · Decision model evaluation
How to evaluate Jev-compatible alternatives
Evaluate Jev-compatible alternatives on the same typed decisions and failure rules, with pinned model versions and disclosed runtime settings. Use JevBench to shortlist candidates by Capability and serving budget, then test probability calibration, abstention and error costs on your own held-out workload.
Reviewed · Maintained by Benchmark Heaven, independently of TypeSafe AI.
Which Jev-compatible models perform best?
The answer depends on your workload and budget. The live open-weights leaderboard identifies the current Capability leaders; the hosted API leaderboard compares endpoints. Read eligibility, measurement dates, confidence intervals and ties. This guide avoids copying a ranking that would become stale.
A repeatable evaluation
- Define state, answer options and the probabilities your application needs. Verify native typed-output support; a prose answer parsed into a label is a different adapter.
- Freeze a representative held-out set, labels, scoring rules and cost assumptions. Keep training, calibration and test splits separate.
- Pin weights or the hosted model version where possible. Record endpoint, quantization, hardware, prompt/adapter and measurement dates.
- Score correctness, probability quality, coverage and failures with identical rules. Compare measured latency and USD per 1,000 decisions under the same serving conditions.
- Validate acceptance thresholds and escalation policies against real error costs. Run a separate test for unseen domains and operational failures.
Review the alternatives inventory and selection guide for licenses and deployment constraints.