Combining the decision model Jev with Opus allows organizations to maintain frontier-level identity resolution precision while reducing expenses fourfold. Decision models provide baseline speeds over 10 times faster and up to 90 times cheaper, but standard frontier models traditionally yield the highest standalone accuracy. Jev excels in this hybrid setup because its low confidence scores correlate with its errors, allowing the system to selectively escalate uncertain records. This automation efficiently replaces manual human review, which struggles to evaluate the thousands of samples necessary to catch rare over-merging errors.
Key points
Pairing well-calibrated decision models with frontier systems delivers top-tier accuracy…: Pairing well-calibrated decision models with frontier systems delivers top-tier accuracy at one-fourth of the expense.
Jev functions effectively in hybrid architectures because its low confidence scores accur…: Jev functions effectively in hybrid architectures because its low confidence scores accurately signal when it needs to route difficult cases.
Automated model-based verification enables organizations to process tens of thousands of…: Automated model-based verification enables organizations to process tens of thousands of data records simultaneously.
Sources checked by our analysis pipeline: 1
We wanted to test whether LLMs and decision models, specifically Jev and Luna, could solve a hard problem that we encounter regularly (grading identity resolution results). We found that while both models performed okay out of the box (88.8–97.3% accuracy), implementing a hybrid model that combines Jev + Opus achieves frontier accuracy at > 4x lower costs.
Our other findings:
- Luna performs better than Jev out of the box, but the gap disappears on harder cases.
- Jev's confidence is better calibrated. Most of its mistakes come with low confidence its right. Most of Luna's mistakes come with high confidence. This enables Jev to perform better than Luna in a hybrid model harness.
- Frontier models are still more accurate than decision models
- Decision models are up to 90× cheaper and more than 10× faster out of the box. They cost $0.5 - $0.10 per 1,000 identities and answer in about 0.15 seconds, but trail Opus accuracy by about 6 percentage points on harder benchmarks.
What is identity resolution?
Identity resolution is the process of mapping data to…
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