A Jev that can ask Opus for help gives Opus accuracy at 4x lower cost

AI-generated summary

This post details an experiment testing LLMs like Jev and Luna for identity resolution, a complex data mapping task. A hybrid model combining Jev with Opus significantly improved accuracy to near-Opus levels while reducing costs by over four times compared to using Opus alone. The key finding is that Jev's better-calibrated confidence scores make it a more effective component in such hybrid systems than Luna, which suffers from overconfidence.