| Rank | Model Name | Score | Provider | License | Link |
|---|---|---|---|---|---|
🥇1 | Cohere Rerank 3 | 94.2 | Cohere | Proprietary | — |
🥈2 | Jina Reranker v2 | 91.8 | Jina AI | Open Source | — |
🥉3 | Voyage Rerank | 89.5 | Voyage AI | Proprietary | — |
4 | bge-reranker-v2-m3 | 87.3 | BAAI | Open Source | — |
5 | Mixedbread Rerank | 85.1 | Mixedbread AI | Proprietary | — |
6 | ms-marco-MiniLM | 82.7 | Microsoft | Open Source | — |
7 | Cohere Rerank 2 | 80.4 | Cohere | Proprietary | — |
Key Insights
What the data tells us
Cohere leads the pack
Cohere Rerank 3 achieves the highest accuracy score (94.2), setting the standard for production reranking. The model excels across diverse domains and languages.
Open source options shine
Jina Reranker v2 (91.8) and bge-reranker-v2-m3 (87.3) prove that open-source rerankers can compete with proprietary solutions while offering complete control.
Consistent improvements
All tested rerankers show 15-40% improvement in retrieval accuracy over baseline semantic search alone, making reranking essential for production RAG.
Methodology
How we rank rerankers
Quality scores are based on retrieval accuracy improvement measured by nDCG@10 (Normalized Discounted Cumulative Gain) across diverse benchmark datasets. Scores are normalized to a 0-100 scale where 100 represents the best performance.
Testing Process
Each reranker is tested on multiple benchmark datasets including MS MARCO, BEIR, and domain-specific corpora. We measure the improvement in ranking quality when reranking the top 100 results from a semantic search system.
Evaluation Metrics
nDCG@10 measures how well the reranker places relevant documents in the top 10 positions. Higher scores indicate better ranking quality, which directly translates to better RAG answer quality.
Score Calculation
Scores are normalized relative to the best-performing model in our tests. A score of 100 represents peak performance, while lower scores indicate proportional decreases in ranking accuracy.
FAQ
Common questions
- What is a reranker?
- A reranker is a model that takes an initial set of search results and reorders them to place the most relevant documents at the top. Unlike retrieval models that return initial results, rerankers use more sophisticated scoring to improve ranking quality.
- Why do I need a reranker for RAG?
- Rerankers significantly improve the quality of documents passed to your LLM, leading to more accurate and relevant answers. They're especially important when your knowledge base is large or contains similar documents.
- How much do rerankers improve results?
- In our testing, rerankers improve retrieval accuracy by 15-40% compared to semantic search alone. This translates to noticeably better RAG answers with fewer hallucinations.
- Should I use an open-source or proprietary reranker?
- Open-source rerankers like Jina v2 offer great performance and full control for self-hosting. Proprietary options like Cohere provide slightly better accuracy and managed infrastructure. Choose based on your accuracy requirements and deployment preferences.