Top Reranker Models for RAG in 2025: A Comparative Review

Why initial retrieval is insufficient

RAG first retrieves documents with keyword search or vector similarity. These methods can return many partially relevant or noisy results because embedding models may miss fine‑grained details, especially for short queries or specialized terminology. Excessive or irrelevant context confuses the LLM and degrades answer quality, so a refinement step is required.

The diagram shows the RAG workflow: a user query is used to search a vector store, the retrieved passages are combined with the query, and the LLM generates a structured answer.

Reranker: Optimizing search

A reranker (cross‑encoder) re‑orders the initial set of documents by evaluating how well each passage matches the user’s intent. It acts as a quality filter that promotes the most relevant chunks to the top.

This two‑stage process first retrieves a broad candidate set and then refines it, dramatically improving relevance.

How reranking improves RAG

Rerankers increase the accuracy of the context fed to the LLM by evaluating semantic similarity rather than simple keyword overlap. By focusing the LLM on a smaller, higher‑quality document set, the model can produce more precise and trustworthy answers and reduce hallucinations.

Retrieval: Get an initial candidate set.

Rerank: Re‑order candidates based on relevance scores.

Generation: Pass only the top‑ranked documents to the LLM.

In practice a typical pipeline retrieves the top 25 documents, passes them to a reranker, and then selects the top 3 for final generation.

2025 leading reranker models

Cohere (API) – Cross‑encoder, closed‑source. Pros: high accuracy, multilingual, easy API integration, fast “Nimble” variant. Cons: pay‑per‑use API, cannot modify the model. Suitable for general RAG, enterprise search, multilingual chatbots.

bge‑reranker (open‑source) – Cross‑encoder, Apache 2.0. Pros: high accuracy, runs on modest hardware, no license fees. Cons: requires self‑hosting and infrastructure management. Suitable for general RAG, budget‑conscious projects, open‑source‑first teams.

Voyage (API) – Cross‑encoder, closed‑source. Pros: state‑of‑the‑art relevance scores, simple Python client. Cons: API cost, slightly higher latency for the top model. Suitable for finance, legal document review, any scenario where accuracy outweighs latency.

Jina (mixed) – Cross‑encoder / ColBERT variant, mixed source. Pros: balanced performance, handles long documents (up to 8 k tokens). Cons: may not reach the absolute peak accuracy of Voyage. Suitable for general RAG, long‑form documents, cost‑performance balance.

FlashRank (open‑source) – Lightweight cross‑encoder. Pros: extremely fast inference, low resource usage, easy integration. Cons: lower accuracy than larger models. Suitable for real‑time or high‑throughput scenarios, edge devices.

MixedBread (mxbai‑rerank‑v2, open‑source) – Cross‑encoder (Qwen‑2.5 backbone), Apache 2.0. Pros: SOTA performance on BEIR benchmarks, multilingual, supports up to 8 k tokens, fast inference. Cons: requires self‑hosting, relatively new model. Suitable for high‑performance multilingual RAG, code/JSON handling, LLM tool selection.

Cohere Rerank example

%pip install --upgrade --quiet cohere

from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_cohere import CohereRerank
from langchain_community.llms import Cohere
from langchain.chains import RetrievalQA

llm = Cohere(temperature=0)
compressor = CohereRerank(model="rerank-english-v3.0")
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever)
chain = RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever)

bge‑reranker example

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CrossEncoderReranker
from langchain_community.cross_encoders import HuggingFaceCrossEncoder

model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base")
compressor = CrossEncoderReranker(model=model, top_n=3)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.invoke("What is the plan for the economy?")
print(compressed_docs)

Voyage Rerank example

%pip install --upgrade --quiet voyageai
%pip install --upgrade --quiet langchain-voyageai

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain.retrievers import ContextualCompressionRetriever
from langchain_openai import OpenAI
from langchain_voyageai import VoyageAIRerank, VoyageAIEmbeddings

documents = TextLoader("../../how_to/state_of_the_union.txt").load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, VoyageAIEmbeddings(model="voyage-law-2")).as_retriever(search_kwargs={"k": 20})
llm = OpenAI(temperature=0)
compressor = VoyageAIRerank(model="rerank-lite-1", voyageai_api_key=os.environ["VOYAGE_API_KEY"], top_k=3)
compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.invoke("What did the president say about Ketanji Jackson Brown?")
print(compressed_docs)

Jina Rerank example

from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.document_compressors import JinaRerank

compressor = JinaRerank()
compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.get_relevant_documents("What did the president say about Ketanji Jackson Brown?")
print(compressed_docs)

FlashRank example

from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import FlashrankRerank
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(temperature=0)
compressor = FlashrankRerank()
compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
compressed_docs = compression_retriever.invoke("What did the president say about Ketanji Jackson Brown?")
print([doc.metadata["id"] for doc in compressed_docs])

MixedBread (mxbai‑rerank‑v2) example

!pip install mxbai_rerank
from mxbai_rerank import MxbaiRerankV2
model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2")
query = "Who wrote To Kill a Mockingbird?"
documents = [
    "To Kill a Mockingbird is a novel by Harper Lee published in 1960...",
    "The novel Moby-Dick was written by Herman Melville...",
    "Harper Lee, an American novelist...",
    "Jane Austen was an English novelist...",
    "The Harry Potter series...",
    "The Great Gatsby, a novel written by F. Scott Fitzgerald..."
]
results = model.rank(query, documents)
print(results)

How to evaluate a reranker

Accuracy@k: Frequency of relevant documents in the top k.

Precision@k: Proportion of relevant documents among the top k.

Recall@k: Fraction of all relevant documents retrieved in the top k.

NDCG: Normalized Discounted Cumulative Gain, accounts for relevance and position.

MRR: Mean Reciprocal Rank, focuses on the rank of the first relevant result.

F1‑score: Harmonic mean of precision and recall.

Choosing the right reranker

Relevance requirements: How accurate must the results be?

Latency: Does the application need real‑time responses?

Scalability: Can the model handle current and future data volumes?

Integration effort: How easily does the reranker fit into the existing RAG stack?

Domain specificity: Is a model fine‑tuned on domain data needed?

Cost: API fees versus self‑hosting compute costs.

Cross‑encoders offer the highest precision but are slower; bi‑encoders scale better but may sacrifice some accuracy. LLM‑based rerankers can be extremely accurate but are costly and slower. Multi‑vector models aim for a balance, while lightweight scoring methods are fastest but less semantically deep.

Conclusion

Rerankers are essential for extracting the most relevant context for LLMs in RAG pipelines. The market offers a spectrum from high‑accuracy closed‑source APIs (Cohere, Voyage) to flexible open‑source solutions (bge‑reranker, Jina, FlashRank, MixedBread). Selecting an appropriate model requires weighing accuracy, latency, scalability, integration effort, and cost.

References

Cohere – https://cohere.com/rerank

bge‑reranker – https://huggingface.co/BAAI/bge-reranker-large

Voyage – https://docs.voyageai.com/docs/reranker

Jina – https://huggingface.co/jinaai/jina-embeddings-v2-base-en

FlashRank – https://github.com/PrithivirajDamodaran/FlashRank

ColBERT – https://huggingface.co/colbert-ir/colbertv2.0

MixedBread (mxbai‑rerank‑v2) – https://www.mixedbread.com/blog/mxbai-rerank-v2