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

The Jev and Luna models perform decently in identity resolution result scoring (88.8–97.3% accuracy), but the Jev+Opus hybrid model achieves state-of-the-art accuracy while reducing cost by 4x. Luna initially performs better than Jev, but the gap disappears in complex cases; Jev has better confidence calibration, with its errors often accompanied by low confidence, while Luna's errors are often accompanied by high confidence.