Best Rerankers for RAG
Find the best reranker for RAG. We benchmark Cohere Rerank, Voyage, BGE reranker, Jina, and Zerank on ranking quality, latency, and cost—so you can pick the right one. If you want to compare the best rerankers for your data, try Agentset.
Last updated: February 15, 2026
| Compare | ||||||
|---|---|---|---|---|---|---|
1638 | 0.079 | 265 | $0.025 | cc-by-nc-4.0 | ||
1629 | 0.095 | 614 | $0.050 | Proprietary | ||
1573 | 0.082 | 266 | $0.025 | cc-by-nc-4.0 | ||
1544 | 0.110 | 613 | $0.050 | Proprietary | ||
1539 | 0.083 | 248 | $0.025 | Apache 2.0 | ||
1520 | 0.103 | 616 | $0.020 | Proprietary | ||
1510 | 0.094 | 447 | $0.050 | Proprietary | ||
1473 | 0.106 | 4687 | $0.050 | Apache 2.0 | ||
1469 | 0.114 | 3333 | $0.050 | cc-by-nc-4.0 | ||
1451 | 0.080 | 392 | $0.050 | Proprietary |
Rerankers Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no reranking to configure.
Trusted by teams building production RAG applications
Overview
Our Recommendation
We recommend Zerank 2 as the best overall reranker for production use. See our full reranker benchmark for methodology and detailed results.
Highest Win Rate
Zerank 2 leads with 1638 ELO, winning more head-to-head matchups than any other reranker.
Close Competition
Cohere Rerank v4.0 Pro follows at 1629 ELO, making it a strong alternative for production use.
Strong Accuracy
Top rerankers deliver 15-40% higher precision than embeddings alone, surfacing better context.
Understanding Rerankers
What are rerankers?
The Two-Stage Retrieval Process
Rerankers are specialized models that enhance search quality in Retrieval-Augmented Generation (RAG) systems. They follow a two-stage retrieval process: first, an embedding model retrieves a set of potentially relevant documents; then, the reranker re-evaluates those results and reorders them so the most relevant documents appear at the top. This second step turns broad retrieval into precise context selection.
Why Rerankers Matter
Embedding models are fast but often miss subtle relevance. Rerankers apply cross-attention mechanisms to better understand the relationship between a query and a document. In practice, this delivers 15–40 % higher retrieval accuracy and more relevant results compared to embeddings alone.
When to Use a Reranker
Rerankers matter when accuracy and context quality are critical. They're especially useful if your knowledge base contains similar or overlapping documents or if queries require nuanced reasoning. Most add only 100–600 ms of latency while sharply improving the documents passed to your LLM.
Selection Guide
Choosing the right reranker
For Maximum Accuracy
Best for:
- • Customer-facing chatbots
- • High-stakes decision support
- • Complex technical documentation
For Self-Hosting
Best for:
- • Data privacy requirements
- • High-volume applications
- • Custom fine-tuning needs
For Low Latency
Best for:
- • Real-time chat applications
- • Mobile applications
- • High-concurrency scenarios
For Multilingual Support
Best for:
- • International applications
- • Multilingual documentation
- • Cross-language search
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked rerankers and embedding models built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Methodology
How We Evaluate Rerankers
The Reranker Leaderboard tests models on three datasets — financial queries, scientific claims, and essay-style content — to see how well they adapt to different retrieval patterns in RAG pipelines.
Testing Process
Each reranker is tested on the same FAISS-retrieved documents (top-50). We measure both ranking quality and latency, capturing the real-world balance between accuracy and speed.
ELO Score
For each query, GPT-5 compares two ranked lists and picks the more relevant one. Wins and losses feed into an ELO rating — higher scores mean more consistent wins.
Evaluation Metrics
We measure nDCG@5/10 for ranking precision and Recall@5/10 for coverage. Together, they show how well a reranker surfaces relevant results at the top.
Common questions
Reranker FAQ
- What is a reranker?
- A reranker refines an initial list of retrieved results by reordering them so the most relevant documents appear first. Unlike basic retrieval models, rerankers use deeper scoring methods to improve search precision and ranking quality.
- Why do I need a reranker for RAG?
- Rerankers make Retrieval-Augmented Generation (RAG) systems more accurate. They ensure your LLM receives the most relevant context, leading to better-grounded answers — especially when your knowledge base is large or overlapping.
- How much do rerankers improve results?
- In our benchmarks, rerankers improved retrieval accuracy by 15–40 % compared to semantic search alone. That means cleaner context, fewer hallucinations, and more reliable RAG performance.
- Why use ELO scoring for ranking?
- ELO scoring measures how often one model outperforms another in direct comparisons. It reflects real-world consistency better than isolated metrics — a higher ELO means the model wins more head-to-head matchups across diverse queries.
- Which datasets are used for evaluation?
- We benchmark rerankers on three datasets — FiQA (finance), SciFact (science), and PG (long-form content). PG doesn't include labeled relevance data, so it's evaluated only with ELO-based LLM judgments, not traditional metrics like nDCG or Recall.
- 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.