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Top 7 Rerankers for RAG are Cohere Rerank 4.0 Pro, Jina Reranker v3, Voyage rerank-2.5, NVIDIA Llama Nemotron Rerank VL 1B v2, Mixedbread mxbai-rerank-large-v2, BAAI bge-reranker-v2-m3, and Alibaba-NLP gte-multilingual-reranker-base. The best choice depends on managed quality, context, modality, language coverage, licensing, and local-inference requirements.
A reranker does not replace the first-stage retriever or the language model. A reranker improves the order of retrieved evidence, which can let a RAG system pass fewer and more relevant passages to the generator. The shortlist below separates hosted APIs, long-context and listwise models, multimodal infrastructure, and open-weight local deployments instead of pretending that different vendor benchmarks form one universal leaderboard.
Key takeaways
- Cohere Rerank 4.0 Pro is the strongest default managed choice when multilingual and structured-data relevance matter more than local deployment control.
- Jina Reranker v3 stands out for listwise ranking and a 131,072-token combined context, but its CC-BY-NC 4.0 model license requires commercial-use review.
- Voyage rerank-2.5 offers a hosted quality model and a lower-latency Lite variant, with 32,000-token context and up to 1,000 documents per request.
- NVIDIA Llama Nemotron Rerank VL 1B v2 is the specialist choice for ranking image-only and text-and-image evidence in multimodal RAG.
- Mixedbread mxbai-rerank-large-v2, BAAI bge-reranker-v2-m3, and Alibaba-NLP gte-multilingual-reranker-base are the main local, open-weight alternatives, with different trade-offs in model size, context, and multilingual coverage.
What is a reranker in RAG, and where does it fit?
A reranker is a second-stage retrieval component that scores or reorders passages after an initial lexical or embedding retriever has produced a candidate set. A typical retrieval-augmented generation pipeline retrieves possible evidence, reranks that evidence against the query, sends only the best passages to the language model, and generates an answer from the reduced context.
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- Retrieve: Use keyword search, dense embeddings, or a hybrid retriever to produce candidate passages.
- Rerank: Give the query and candidates to a cross-encoder, listwise model, or multimodal reranker.
- Trim: Keep the highest-ranked evidence that fits the language model’s context and the application’s quality budget.
- Generate: Ask the language model to answer using the selected context and, where applicable, provide citations.
| Component | Main job | What it can and cannot do |
|---|---|---|
| First-stage retriever | Find a broad candidate pool quickly | Controls recall; can return lexical or semantic matches, but may include irrelevant passages |
| Reranker | Order the candidate pool by query relevance | Improves precision within the pool; cannot find a document that retrieval omitted |
| Generator | Compose the final answer from selected context | Benefits from better evidence, but can still misunderstand, omit, or hallucinate information |
Which reranker is best for your RAG system?
The best reranker depends on the system constraint rather than on a universal leaderboard. Choose the managed model that matches your quality and operational needs, or choose a local model when data locality, predictable infrastructure, licensing, or offline inference is more important.
| Primary requirement | Recommended model | Why it fits | Main caution |
|---|---|---|---|
| Managed multilingual and structured-data quality | Cohere Rerank 4.0 Pro | Hosted API, multilingual and semi-structured JSON support, 32K context | Usage cost and hosted-service dependence |
| Long-context listwise comparison | Jina Reranker v3 | 131,072-token combined context and cross-document listwise ranking | CC-BY-NC 4.0 model license and potentially different throughput characteristics |
| Hosted quality-versus-latency choice | Voyage rerank-2.5 | Standard and Lite models, 32K context, multilingual and instruction-following support | Token-based API costs can grow with large candidate sets |
| Images, scanned pages, or screenshots in evidence | NVIDIA NeMo Retriever Reranking NIM | Can rank text queries against text-only, image-only, or text-and-image passages | Requires NVIDIA GPU and NIM-oriented infrastructure |
| Commercially flexible open-weight deployment | Mixedbread mxbai-rerank-large-v2 | Apache-2.0 license, multilingual and code-search positioning, long-context support | 2B-parameter local serving is more demanding |
| Local multilingual baseline | BAAI bge-reranker-v2-m3 | Apache-2.0 license, established Hugging Face ecosystem, practical local inference | Pairwise scoring and a less distinctive context window can limit large-document workloads |
| Efficient multilingual local inference | Alibaba-NLP gte-multilingual-reranker-base | 306M parameters, more than 70 languages, and up to 8,192 input tokens | Less context and fewer managed-service conveniences than the hosted options |
1. Cohere Rerank 4.0 Pro: best managed-quality default
Cohere Rerank 4.0 Pro is the best starting point for teams that want a managed, general-purpose reranking API with multilingual and structured-data support. Cohere’s current rerank documentation lists rerank-v4.0-pro as a multilingual model for English and non-English documents, including semi-structured JSON, with a 32K context length. Cohere positions Pro for its highest-quality and more complex use cases, while the Fast variant is aimed at lower latency and higher throughput. The Cohere Rerank model documentation contains the current model distinctions.
Cohere’s best-practices documentation describes up to 10,000 documents for the v4 models, subject to the chunk constraint, and up to 16,384 query tokens. Those limits are ceilings rather than recommendations: sending thousands of candidates through a hosted reranker can increase latency and cost, while a smaller, high-recall candidate pool is usually easier to operate.
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Use the current v4 documentation when implementing a new system. Cohere’s older v2 family was deprecated in favor of v3.5, and Cohere’s current documentation now lists v4.0 models; model names should be checked again immediately before production deployment in Cohere’s deprecation guidance.
2. Jina Reranker v3: best for long-context listwise ranking
Jina Reranker v3 is the most technically distinctive option here because it compares a list of candidate documents in one context instead of behaving like only a series of independent query-document scores. Jina describes jina-reranker-v3 as a 0.6B-parameter multilingual reranker with a listwise, “last but not late interaction” architecture. Jina’s documentation states that the combined query-and-candidate context can reach 131,072 tokens and that the API supports more than 100 languages. These details appear in Jina’s Reranker API documentation.
Listwise ranking matters when candidates overlap, repeat evidence, or need to be compared with one another. A model that sees the candidate list together may be able to distinguish which passage is the most useful among several similar passages, although the result can have different latency and throughput behavior from a pairwise cross-encoder. Candidate-set size should therefore be part of the evaluation, not an afterthought.
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Jina’s release article reports a vendor-reported 61.94 nDCG@10 BEIR result. That figure belongs to Jina’s announcement dated October 3, 2025, and it is not a cross-vendor leaderboard because vendors may use different datasets, model versions, and evaluation procedures. The result is useful context, not proof that Jina v3 will rank every domain better than Cohere or Voyage. See Jina’s October 3, 2025 Reranker v3 announcement for the vendor’s stated result and methodology.
License review is essential. Jina states that its rerankers are released under CC-BY-NC 4.0, so a commercial deployment may require contacting Jina or arranging commercial terms. Jina v3 is attractive for long documents, multilingual retrieval, and code-search scenarios, but the model license should be resolved before the engineering team commits to local production use.
3. Voyage rerank-2.5: best hosted quality-versus-latency choice
Voyage rerank-2.5 is a practical hosted alternative to Cohere when a team wants a quality-oriented standard model and a lower-latency option from the same API family. Voyage’s reranker documentation lists rerank-2.5 and rerank-2.5-lite as its recommended models. Both support 32,000-token context, instruction following, and multilingual use; Voyage describes the standard model as quality optimized and Lite as a quality-and-latency compromise. The Voyage reranker documentation also states that the API accepts up to 1,000 documents per request and returns relevance scores and indexes.
The standard-versus-Lite split gives teams a clear experiment: keep the retriever and candidate pool fixed, then compare answer quality and latency with each Voyage model. Lite may be the better fit when the reranker is on a high-volume path, while the standard model is the more natural first test when difficult relevance judgments matter more than throughput.
Voyage’s pricing documentation states that the first 200 million reranker tokens are free per account, followed by documented pricing of $0.05 per million tokens for rerank-2.5 and $0.02 per million tokens for rerank-2.5-lite. Pricing and model availability are volatile, so confirm the Voyage AI pricing documentation before budgeting. Large candidate sets can increase processed-token usage quickly, making candidate-pool control important even when the API limit is higher.
4. NVIDIA Llama Nemotron Rerank VL 1B v2: best for multimodal RAG
NVIDIA Llama Nemotron Rerank VL 1B v2 is the specialist choice when retrieved evidence includes visual information that a text-only reranker cannot inspect directly. NVIDIA describes nvidia/llama-nemotron-rerank-vl-1b-v2 as a vision-language reranking model that can score a text query against text-only passages, image-only passages, or passages containing both text and images.
This capability is useful for page images from scanned PDFs, slide decks, screenshots, charts, diagrams, and document layouts where the answer may depend on visual evidence. NVIDIA’s NeMo Retriever Reranking NIM reorders citations by query relevance in retrieval and RAG pipelines. The NVIDIA NeMo Retriever Reranking NIM overview explains the model’s multimodal role.
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The deployment trade-off is substantial for ordinary text-only RAG. NVIDIA’s documentation describes up to 512 passages per call, sorted raw-logit output, and token limits applied at the query-passage-pair level. The service is delivered through containerized NIM infrastructure, so teams must account for NVIDIA GPU compatibility, runtime support, batching, and capacity. NVIDIA provides additional request and usage details in its NeMo Retriever reranking usage documentation.
Do not choose the vision-language reranker merely because a RAG system uses PDFs. Choose it when the retrieved page image or other visual representation contains evidence that text extraction, OCR, or a conventional text reranker would miss.
5. Mixedbread mxbai-rerank-large-v2: best larger open-weight option
Mixedbread mxbai-rerank-large-v2 is a strong open-weight candidate for teams that want multilingual and code-search positioning with Apache-2.0 licensing and can support a larger local model. Mixedbread’s model card describes the model as supporting more than 100 languages, code, and long-context use. The model repository identifies the v2 large model as a 2B-parameter text-ranking model and shows it alongside a smaller base model in the family.
The Apache-2.0 license provides broader commercial deployment flexibility than a noncommercial model license, but Apache-2.0 does not eliminate operational responsibilities. The operator still owns serving, batching, memory planning, quantization decisions, monitoring, security updates, and the evaluation of model behavior on domain-specific data. The Mixedbread mxbai-rerank-large-v2 model card provides Transformers and Sentence Transformers examples.
Mixedbread is a sensible choice when local inference or data locality is important and the team can afford more compute than a small encoder baseline. It is not automatically the best open-weight model: public model-card claims and positioning should be checked against the same queries and relevance judgments used for every other candidate.
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6. BAAI bge-reranker-v2-m3: best local multilingual baseline
BAAI bge-reranker-v2-m3 is the practical local baseline for multilingual RAG teams that want a familiar open model and Apache-2.0 licensing. BAAI describes the model as a multilingual reranker based on bge-m3 that is designed for relatively easy deployment and efficient inference. Unlike an embedding model, BGE reranking takes the query and document together and outputs a relevance score. The official BGE model card documents this query-document scoring behavior.
BGE is useful when data must remain inside the organization, when infrastructure costs need to be predictable, or when the application already uses the Hugging Face ecosystem. The model card identifies an Apache-2.0 license and an XLM-RoBERTa-based sequence-classification architecture in its model details.
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The trade-off is that BGE is a pairwise reranker, so scoring a large candidate set means evaluating many query-document combinations. BGE also does not offer the same long-context positioning as Cohere v4, Voyage 2.5, or Jina v3 in the supplied documentation. For long documents, chunking quality and candidate-pool size may matter as much as the model choice.
7. Alibaba-NLP gte-multilingual-reranker-base: best efficiency-oriented local model
Alibaba-NLP gte-multilingual-reranker-base is the efficiency-oriented open alternative when more than 70 languages and an 8K input window are sufficient. Alibaba-NLP’s model card, dated July 29, 2024, describes an encoder-only reranker with 306M parameters, support for more than 70 languages, and up to 8,192 input tokens. The model is Apache-2.0 licensed and can be served through Transformers, Text Embeddings Inference, or compatible reranking endpoints. See the Alibaba-NLP GTE multilingual reranker model card.
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Choose GTE when efficiency, Apache-2.0 licensing, and local serving are the priority. Choose BGE instead when ecosystem familiarity or an existing BGE-based stack matters more, and evaluate Mixedbread when larger-model quality or code-search behavior justifies additional local resources.
How do pairwise, listwise, and multimodal rerankers differ?
Pairwise rerankers score one query-document combination at a time, listwise rerankers compare multiple candidates in a shared context, and multimodal rerankers can use image as well as text evidence.
| Ranking approach | Examples from this list | Best fit | Operational implication |
|---|---|---|---|
| Pairwise text ranking | BGE, GTE, Mixedbread; many hosted APIs can expose scores this way | Standard text passages and straightforward local serving | Candidate count directly affects the number of query-document evaluations |
| Listwise ranking | Jina Reranker v3 | Overlapping candidates where relationships among documents matter | Latency and ranking stability should be tested at the intended list sizes |
| Multimodal ranking | NVIDIA Llama Nemotron Rerank VL 1B v2 | Slides, screenshots, scanned pages, charts, and image-bearing passages | Requires visual inputs and substantially more specialized deployment infrastructure |
The distinction is not merely marketing terminology. A listwise model may make different decisions when the candidate set changes, while a pairwise model’s score for a given query-document pair may be independent of the other candidates. That difference is why production tests should use realistic candidate-set sizes and should measure ranking stability, not only a single offline score.
How should you evaluate the seven rerankers?
A useful comparison holds the first-stage retriever constant, feeds every reranker the same candidate passages, and measures both retrieval quality and final-answer behavior.
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- Build a representative test set. Use the application’s real query types, languages, document lengths, metadata, and difficult edge cases. Include relevance judgments that identify which passages actually answer each query.
- Keep retrieval fixed first. Run the same lexical, embedding, or hybrid retriever before every reranker. This isolates the reranker’s contribution and prevents a better first-stage retriever from being mistaken for a better reranker.
- Test candidate-pool sizes separately. A reranker cannot recover missing documents, but too many low-quality candidates can increase cost and latency. Test the candidate counts the production system is likely to use rather than relying on a vendor’s maximum request limit.
- Measure ranking quality at the actual context cutoff. Record MRR and nDCG, especially at the number of passages ultimately sent to the generator. A model that ranks well at the top 20 may not be the best model at the application’s final top 5 or top 8 cutoff.
- Measure answer-level outcomes. Track groundedness, citation correctness where citations are used, answer relevance, and failure modes such as boilerplate outranking the passage that contains the answer.
- Measure operational cost. Record latency percentiles, token cost for hosted APIs, GPU cost for local models, memory use, throughput, error rates, and the effect of long documents or multilingual queries.
- Review licenses and data handling. Confirm whether the model can be used commercially, where query and document data are processed, how long the service retains data if applicable, and which model version is actually deployed.
Do not combine vendor-reported BEIR, MIRACL, or internal benchmark values into one definitive leaderboard. The supplied official sources report different datasets, metrics, model versions, and evaluation setups. Jina’s reported 61.94 nDCG@10 BEIR result and Alibaba-NLP’s claimed 10x speed advantage are useful vendor claims to investigate, but neither substitutes for an evaluation on the application’s own workload.
What is the best reranker for each deployment scenario?
| Scenario | First model to test | Reasonable alternative | Decision checkpoint |
|---|---|---|---|
| Enterprise API with multilingual records | Cohere Rerank 4.0 Pro | Voyage rerank-2.5 | Compare relevance, data-handling requirements, latency, and processed-token cost |
| Long documents and overlapping candidates | Jina Reranker v3 | Cohere Rerank 4.0 Pro | Confirm commercial licensing and test listwise latency at real candidate sizes |
| High-volume hosted retrieval | Voyage rerank-2.5-lite | Cohere’s Fast variant | Verify that lower latency does not remove answer-bearing passages from the final cutoff |
| Visual document evidence | NVIDIA Llama Nemotron Rerank VL 1B v2 | A text reranker after OCR or layout extraction | Measure whether visual ranking adds enough quality to justify GPU and NIM operations |
| Self-hosted commercial RAG | Mixedbread mxbai-rerank-large-v2 | BGE or GTE | Compare GPU memory, batching, license requirements, and domain quality |
| Small local multilingual service | GTE multilingual reranker base | BGE reranker v2 m3 | Check the 8,192-token input limit against the application’s chunk sizes |
What should you read before implementing reranking?
Reranking is one stage of a larger RAG system, so implementation references are most useful when they cover chunking, retrieval, evaluation, orchestration, and grounded generation as well. The publisher catalog for A Simple Guide to Retrieval Augmented Generation documents a general RAG reference. O’Reilly also lists Retrieval-Augmented Generation in Production with Haystack, which is directly relevant to production pipelines.
O’Reilly’s catalog lists Hands-On RAG for Production with a May 1, 2026 date, so readers should verify release and availability before relying on that title. A book can provide implementation context, but it should not replace a measured comparison on the queries, languages, candidate passages, and latency budget of the actual RAG application.
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What are the main limitations of this shortlist?
This list is a practical shortlist, not an immutable ranking. Model names, API availability, prices, context limits, licenses, hardware requirements, and commercial programs can change. Recheck each official model card or API document before publication, procurement, or deployment.
The seven models also do not represent identical products. Cohere and Voyage are managed APIs; Jina is available through an API and as a model artifact but carries a material licensing question; NVIDIA’s model is designed for multimodal NIM deployment; and Mixedbread, BGE, and GTE are open-weight models that shift serving and monitoring work to the operator. Comparing them fairly requires an application-specific test rather than a single number copied from different vendor pages.
Frequently Asked Questions
Can a reranker find a document that the first-stage retriever missed?
No. A reranker can reorder only the passages returned by the first-stage retriever. If the retriever omits the passage containing the answer, the reranker cannot recover it, so candidate-pool recall must be evaluated separately.
Is Jina Reranker v3 licensed for commercial use?
Jina Reranker v3 is released under CC-BY-NC 4.0 according to Jina’s documentation, so commercial use may require contacting Jina or arranging commercial terms. Teams should resolve the license before deploying the model commercially.
Which reranker is best for multimodal RAG?
NVIDIA Llama Nemotron Rerank VL 1B v2 is the strongest fit in this shortlist when evidence includes images, scanned pages, slides, screenshots, or other visual content. Text-only RAG usually does not need its additional multimodal infrastructure.
How many passages should a RAG reranker process?
There is no universal best candidate count. Test the candidate-pool sizes used by the application while keeping the first-stage retriever fixed, and measure final-cutoff relevance, answer groundedness, latency, and hosted token or local GPU cost.
The Bottom Line
For most text-only production RAG systems, start with Cohere Rerank 4.0 Pro or Voyage rerank-2.5, then compare the two on the same candidate pool. Choose Jina v3 for long-context listwise ranking only after licensing and latency checks, NVIDIA Nemotron Rerank VL for genuinely visual evidence, and Mixedbread, BGE, or GTE when local inference and deployment control outweigh managed-service convenience.
Quick Recap
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