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

- HackerByte page: <https://hackerbyte.net/en/stories/a-system-named-jev-integrates-with-opus-to-achieve-similar-accuracy-at-a-signifi-50397429>
- Original source: <https://hightouch.com/blog/llm-judges-identity-resolution>
- Hacker News discussion: <https://news.ycombinator.com/item?id=50397429>
- Submitted to Hacker News: 2026-11-06T19:48:51.000Z

A system named Jev integrates with Opus to achieve similar accuracy at a significantly 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.

## Key points

- A hybrid Jev \+ Opus model achieved near\-Opus accuracy for identity resolution.
- This hybrid approach resulted in costs over 4x lower than using Opus alone.
- Jev's confidence scores are better calibrated than Luna's, making it more suitable for hybrid models.
- Decision models \(like Luna\) are much cheaper and faster but less accurate than frontier LLMs.
- Identity resolution is crucial for personalization and analytics but is challenging due to data inconsistencies and over\-merging.