We tested whether Jev could replace some of the LLM calls inside three agentic search workloads. 1...

Milvus on X: "We tested whether Jev could replace some of the LLM calls inside three agentic search workloads. 1️⃣ In 𝗗𝗲𝗲𝗽𝗦𝗲𝗮𝗿𝗰𝗵𝗲𝗿, Jev matched DeepSeek Flash at 93.25% Recall@5. Median latency for the stopping decision fell from 2.23 seconds to 0.55, while estimated cost was about 7.5… / X

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We tested whether Jev could replace some of the LLM calls inside three agentic search workloads. 1️⃣ In 𝗗𝗲𝗲𝗽𝗦𝗲𝗮𝗿𝗰𝗵𝗲𝗿, Jev matched DeepSeek Flash at 93.25% Recall@5. Median latency for the stopping decision fell from 2.23 seconds to 0.55, while estimated cost was about 7.5x lower. For this kind of narrowly defined stopping decision, Jev can replace the general-purpose LLM without sacrificing aggregate retrieval quality. 2️⃣ In 𝗠𝗲𝗺𝗦𝗲𝗮𝗿𝗰𝗵, Jev raised Recall@5 from 74.71% to 79.41%. Voyage rerank-3 still performed better, reaching 81.87% at a lower cost. For this workload, the dedicated reranker remains the better choice: higher retrieval quality at a lower cost. 3️⃣ In 𝗩𝗲𝗰𝘁𝗼𝗿 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚, Jev beat GPT-4o-mini on HotpotQA and MuSiQue. It came within one point of GPT-5-mini on HotpotQA, but trailed it by 4.13 points on MuSiQue. Jev is a viable option when latency matters and some loss in recall is acceptable, but a stronger generative model remains better for difficult multi-hop retrieval. Overall, Jev worked best for frequent, bounded semantic decisions. Its advantage narrowed when a specialized model already fit the task or when the decision required reasoning across multiple pieces of evidence. See the full results:

milvus.io/blog/jev-vs-ll…

3:50 PM · Oct 13, 2026

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