TEST-DRIVING JEV ON A SECURITY TASK: IDENTITY RESOLUTION

Summary from the TLDR IT newsletter

Vincenzo Iozzo tested Jev, a specialized classification model, on matching accounts across systems to the same person in five real organizations. Jev scored 0.948 F1 on large orgs at $0.62 per 1,000 accounts, beating Claude Haiku 4.5 (0.894 F1, 19x the cost) and Sonnet 5 (0.911 F1, 47x the cost) at roughly a tenth of their latency. His conclusion is that small calibrated models beat prompted frontier LLMs on bounded, high-volume security classification.