Knowledge graph extraction using an LLM is known to be costly, mainly due to the infinite number of entity extractions and relationship inferences. With Jev, I want to see if it can reduce cost and latency while maintaining accuracy. We froze 160 positive cases from FewRel 1.0 train_wiki: 16 relations × 10 cases, seed 17. Jev, GPT-5.6 Luna, and DeepSeek V4.1 Flash saw the same cases, choices, order, and scoring. latency (p95): Jev — 431 ms DeepSeek — 1,242 ms Luna — 1,699 ms Jev was 2.9× faster than DeepSeek and 3.9× faster than Luna with sequential, one-case requests. cost per 100 planned decisions: Jev = 0.410¢ Luna = 1.225¢ DeepSeek = 1.462¢ accuracy, counting uncovered cases as incorrect: Jev — 87.5% Luna — 89.375% DeepSeek — 93.125% so the result isn't “Jev wins everything.” it is much faster and cheaper on this task, with a real accuracy tradeoff. credits to Llamaindex's DocJev work that complement our upstream pipeline!   submitted by   /u/richie9830 [link]   [comments]