---
title: "bge-reranker-v2-m3 API hosted in the EU: multilingual reranking"
description: "Run bge-reranker-v2-m3 on inference.eu: a light multilingual reranker that sorts search and RAG results by relevance, processed in the EU."
canonical: "https://inference.eu/models/bge-reranker-v2-m3/"
language: "en"
---

Reranking · BAAI (Beijing Academy of Artificial Intelligence)

# Run the bge-reranker-v2-m3 reranker in the EU through our API

bge-reranker-v2-m3 is a lightweight multilingual reranker. It reads a query and a candidate passage together and returns a relevance score, so you can put the best results first after a fast vector search. On inference.eu you call it through our API, with your queries and documents processed in EU data centres and never used for training.

[Contact sales](https://inference.eu/contact/)[Example request](#example)

1.  01568M
2.  02EU data centres
3.  03Apache 2.0

## What bge-reranker-v2-m3 is

bge-reranker-v2-m3 was released in March 2024 by the Beijing Academy of Artificial Intelligence (BAAI) under the Apache 2.0 licence. It is built on the bge-m3 embedding model and has about 568 million parameters. BAAI describes it as a lightweight reranker with strong multilingual capabilities, easy to deploy and fast at inference.

Unlike an embedding model, which turns query and document into separate vectors, a reranker is a cross-encoder: it reads the query and the passage as one input and outputs a single score. That is slower per pair, but much more precise, which is why rerankers are used as a second step on a short list of candidates.

## What it does well, and where it falls short

The reranker sharpens retrieval in search and RAG systems. After bge-m3 or another embedding model returns the top 50 to 100 candidates, bge-reranker-v2-m3 reorders them so that the passages a chat model sees first are the ones that really answer the question. It works across many languages, including mixed-language queries and documents, and its small size keeps the extra step cheap.

It cannot search a whole corpus by itself: each query and passage pair needs its own pass, so it only suits short candidate lists. Scores are relative, not calibrated probabilities. BAAI's larger rerankers based on Gemma or MiniCPM can rank better on hard cases, at a higher compute cost.

## Calling bge-reranker-v2-m3 through the API

Set the base URL to the inference.eu API, use your inference.eu key and set the model to `bge-reranker-v2-m3`. Send a request to the rerank endpoint, `/v1/rerank`, with a `query` and a list of `documents`. The response gives a relevance score for each document, which you use to sort them before passing the best few to a chat model.

The raw score can be any real number. BAAI notes that a sigmoid function maps it to a value between 0 and 1, which makes it easier to set a cut-off for weak matches. Frameworks such as LangChain and LlamaIndex can call the reranker as a post-processing step after vector retrieval.

## Data residency for bge-reranker-v2-m3

A reranker sees the user's question and the full text of every candidate passage, often the most sensitive parts of your knowledge base. On inference.eu, bge-reranker-v2-m3 runs on infrastructure in the European Union, so queries and documents are processed in EU data centres, are not sent to China and are never used to train any model.

Every customer receives a data processing agreement under Article 28 of the GDPR and our subprocessor list, so the whole retrieval pipeline, from embedding to reranking to answer generation, can be documented with one provider. Billing is in euros. The Apache 2.0 licence allows commercial use without user limits.

## BGE Reranker v2 M3

Maker

BAAI (Beijing Academy of Artificial Intelligence)

Made in

China

Type

Reranking

Parameters

568M

Licence

[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

Released

March 2024

Model ID

bge-reranker-v2-m3

Processing

EU data centres

Training on your data

Never

Source

[huggingface.co](https://huggingface.co/BAAI/bge-reranker-v2-m3)

bge-reranker-v2-m3 · Python

```
import os
from openai import OpenAI

client = OpenAI(base_url="https://api.inference.eu/v1", api_key=os.environ["INFERENCE_EU_API_KEY"])

response = client.chat.completions.create(
    model="bge-reranker-v2-m3",
    messages=[{"role": "user", "content": "Summarise this contract in five bullet points."}],
)
print(response.choices[0].message.content)
```

## What teams use it for

-   1

    ### Better RAG answers

    Reorder retrieved passages so the chat model reads the most relevant evidence first and answers more accurately.

-   2

    ### Search result ranking

    Improve the top results of an internal or customer-facing search across several European languages.

-   3

    ### Filtering weak matches

    Drop candidate passages below a score threshold so that irrelevant text does not reach the prompt.

## BGE Reranker v2 M3: common questions

The questions buyers, developers and legal teams ask us most. Something missing? Ask us directly.

Compliance depends on how you process data. On inference.eu, reranking requests are processed in EU data centres, not used for training and covered by a data processing agreement under Article 28 GDPR with a published subprocessor list. Your queries and documents are not sent to the model's maker.

A reranker scores how well each candidate passage answers a query by reading both together. Vector search is fast but approximate; a reranker on the top results is slower per item but more precise. Adding one is often the simplest way to improve RAG answer quality.

Typically the top 20 to 100 results from vector or keyword search. Each document is scored separately with the query, so the cost grows with the list. Rerank enough candidates to catch the right passage, then pass only the best few to the chat model.

Yes. It is released under the Apache 2.0 licence, which allows commercial use, modification and redistribution without fees or user limits. You can use it in paid search products and internal assistants without a separate agreement with BAAI.

It is built on bge-m3, which supports more than 100 languages, and BAAI lists it as its multilingual reranker. It handles German, French, English and other European languages, including queries in one language and documents in another.

Higher scores mean higher relevance. Raw scores are unbounded; BAAI shows how a sigmoid function maps them into a range from 0 to 1. Use them to sort results and to set a threshold for weak matches, tuned on examples from your own data.

Yes. The reranker only reads text, so it works after bge-m3, Qwen3 Embedding 8B, multilingual-e5-large-instruct or keyword search such as BM25. It pairs naturally with bge-m3, which it is built on, but the first stage can be anything.

The Qwen3 reranker series is newer and comes in larger sizes that can rank better on hard queries. bge-reranker-v2-m3 is smaller, well established and cheap to run on long candidate lists. Compare them on a set of real queries with known answers.

bge-reranker-v2-m3 is an open model from BAAI. On inference.eu its weights run in EU data centres, so your queries and documents never go to China or to BAAI. A reranker only returns scores, not generated text, which limits concerns about content bias.

Use the inference.eu base URL and your inference.eu key, and send a POST request to \`/v1/rerank\` with \`model\` set to \`bge-reranker-v2-m3\`, a \`query\` and a list of \`documents\`. The response lists each document's index and relevance score.

[Ask a question](https://inference.eu/contact/)

## Related

[All models](https://inference.eu/models/#directory)

-   [BGE-M3Run bge-m3 on inference.eu: a compact multilingual embedding model for search and RAG, 100+ languages and 8192-token inputs, processed in the EU.Embeddings](https://inference.eu/models/bge-m3/)
-   [Qwen3 Embedding 8BRun Qwen3 Embedding 8B on inference.eu: multilingual text embeddings for search and RAG, up to 4096 dimensions and 32K tokens, processed in the EU.Embeddings](https://inference.eu/models/qwen3-embedding-8b/)
-   [multilingual-e5-large-instructRun multilingual-e5-large-instruct on inference.eu: compact embeddings in 100 languages for search, RAG and classification, processed in the EU.Embeddings](https://inference.eu/models/multilingual-e5-large-instruct/)

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