BGE-M3 (Multilingual)

EmbeddingsUnavailable

by Communitybge-m3-multilingual

BAAI multilingual embedding model covering 100+ languages with an 8192-token context. M3 stands for its multi-functionality (dense, sparse and ColBERT-style multi-vector retrieval), multilinguality and multi-granularity over long documents. Returns 1024-dim dense vectors and is a strong open choice for cross-lingual and long-text retrieval.

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Status: Unavailable
Context: 8,192 tokens
Input → output: Text → Vector
Developer: Community
Updated: September 23, 2026

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Input & output

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Text

0 / 8,000

Text to embed (any of 100+ languages, up to long documents)

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Output

The vector appears here.

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About BGE-M3 (Multilingual)

TL;DRAs of September 23, 2026

BGE-M3 (Multilingual) is a model by Community in the Embeddings category. BGE-M3 (Multilingual) is currently not available on Railwail. The context window holds 8,192 tokens.

bge-m3 from the Beijing Academy of Artificial Intelligence is a single model that supports three retrieval modes at once: dense embeddings, sparse lexical weights and ColBERT-style multi-vector matching. It handles more than 100 languages and inputs up to 8192 tokens, which makes it suitable for long-document and cross-lingual retrieval where shorter English-only models fall short. The dense output is 1024-dim. Through the Hugging Face feature-extraction pipeline you get the dense embedding, which mean-pools or uses the [CLS] vector depending on configuration; it is widely used as an open multilingual retrieval backbone.

Pricing

Currently unavailable. There is no price for this model at the moment, so it cannot be run.

API

Call BGE-M3 (Multilingual) with your Railwail API key. Use this model ID in the request:

bge-m3-multilingualAPI documentation Get an API key

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The model has no verified price or is deactivated; API calls are refused.

Specifications

Model ID: bge-m3-multilingual
Developer: Community
Category: Embeddings
Input: Text
Output: Vector
Context window: 8,192 tokens
Catalog entry updated: September 23, 2026

Input parameters

Inputs and settings from the model's input schema. The example in the API section shows which of them the API accepts.

ParameterTypeDefaultAllowed values

inputsrequired
Text to embed (any of 100+ languages, up to long documents)
Type: Text
Default: –
Allowed values: up to 8,000 characters

Use cases

Related guides

Embedding Models for Search & Science

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Frequently asked questions

What is BGE-M3 (Multilingual)?

BGE-M3 (Multilingual) is a model by Community in the Embeddings category. It is listed on Railwail but cannot be run at the moment.

How much does BGE-M3 (Multilingual) cost on Railwail?

BGE-M3 (Multilingual) cannot be run on Railwail at the moment, so there is no current price. Available alternatives with prices are listed further down this page.

What is the context window of BGE-M3 (Multilingual)?

The context window of BGE-M3 (Multilingual) holds 8,192 tokens.

How fast is BGE-M3 (Multilingual)?

There are not enough measured runs of BGE-M3 (Multilingual) on Railwail yet to state a run time. It depends on the input, the settings and the load at the provider.

Is BGE-M3 (Multilingual) better than OpenAI text-embedding-3-large?

That depends on the task. BGE-M3 (Multilingual) (Community) and OpenAI text-embedding-3-large (OpenAI) are both models in the Embeddings category. The comparison page shows their prices and specifications side by side.

Compare BGE-M3 (Multilingual) and OpenAI text-embedding-3-large

Can I use BGE-M3 (Multilingual) right now?

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