Reranker
A second-stage retrieval model that re-scores a small set of candidate results from initial retrieval — using a cross-encoder or LLM to produce more accurate final rankings.
Rerankers solve the speed-vs-accuracy trade-off in retrieval. Stage one (vector or keyword search) returns 50–100 candidates fast but imprecisely. Stage two (the reranker) carefully scores each candidate against the query, returning the top 5–10 in correct order. The combined system beats either alone.
The 2026 production rerankers: Cohere Rerank 3 (managed, fast, strong baseline), Voyage Rerank (RAG-tuned), BAAI BGE rerankers (open-source), and increasingly LLM-as-reranker for highest-quality production use cases. Each adds 50–500ms of latency per query but typically lifts retrieval recall@5 by 20–40%.
For agent stacks, the question is whether to rerank at all. Skip it for low-stakes retrieval where p95 latency matters more than precision. Add it for high-stakes retrieval (customer-facing answers, agentic decisions) where the precision lift justifies the latency cost.
Frequently asked
Do I need a reranker in my RAG stack?+
For high-stakes retrieval where wrong answers are costly: yes, the precision lift is worth the latency. For low-stakes retrieval (chat with the user iterating): often no, vanilla vector search is sufficient.
Cohere Rerank vs Voyage Rerank vs open-source — what should I pick?+
Cohere Rerank for managed simplicity with strong English quality. Voyage Rerank for RAG-specialized tuning. Open-source BGE rerankers for self-hosted control. All three are within 5–10% of each other on most benchmarks.