Reranking Models
Cross-encoder models that rerank initial retrieval results for improved relevance. More accurate than bi-encoders but slower, typically applied to top-k candidates.
About this tool
Overview
Reranking models refine initial retrieval results by computing precise relevance scores between queries and retrieved documents.
Architecture
Cross-Encoders
- Process query and document together
- More accurate than bi-encoders
- Expensive: Can't pre-compute
- Applied to top-k results only
Popular Models
- Cohere Rerank: Commercial API
- bge-reranker-v2-m3: BAAI multilingual reranker
- Cross-Encoder Models: HuggingFace Sentence Transformers
- Jina Reranker: Various sizes available
Typical Pipeline
- Retrieval: Get top-100 candidates with vector search
- Rerank: Apply reranker to refine order
- Select: Choose top-10 for LLM context
Benefits
- Improved Precision: Better relevance ranking
- Better RAG: More relevant context for LLMs
- Hybrid Approach: Combine speed (retrieval) + accuracy (reranking)
When to Use
- High-precision requirements
- When latency allows (~100ms)
- Production RAG systems
- Search quality matters
Pricing
- Open Source: Free (bge-reranker, others)
- Cohere Rerank: Usage-based API pricing
- Self-hosted: Compute costs
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Information
Websitewww.pinecone.io
PublishedMar 11, 2026
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