Benchmark Jev on the decision, not the demo.
TypeSafe latency and cost claims are useful starting points. Your input distribution, question design, and fallback policy decide whether Jev is a good production fit.
Jev decision accuracy
Compare Jev against the rule, model, or human baseline already in your workflow. A valid label is not automatically the right label.
Calibration
Group Jev outputs by confidence and check whether an 0.8 decision is correct roughly 80% of the time on your traffic.
Latency
Measure p50 and p95 from your region, including network, gateway, and retries. Vendor 70–500ms figures are a starting point.
Fallback rate
Track how often low-confidence Jev outputs still need GPT, Claude, or a human. A cheap model that always escalates is not cheap.
Downstream outcome
Measure resolved tickets, accepted routes, prevented risky calls, or another business result. That is the Jev benchmark that matters.