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BGE-M3

baai
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BAAI's BGE-M3 is a multilingual text embedding model that handles dense, sparse, and multi-vector retrieval in one model, across 100+ languages and an 8,192-token input window. It returns a 1,024-dimension dense vector.

Providers
Capabilities
OVHcloud
ovhcloud-byok
$0
$0
Unavailable
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API & code

Embeddings API

/api/v1/embeddings

Generate embedding vectors for search, RAG, and clustering.

Uptime & Health
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BGE-M3 share card
BGE-M3
OVHcloud upstream share card
OVHcloud upstream
Credits
Use your own key

Run BGE-M3 on your own key — your requests are billed by the provider. Pool callers pay AnyRouter credits.

No BYOK keys configured for this model yet.

Share a key with the pool to earn credits for every request it serves, covering your plan cost.

Embedding vectors
Vector dimensions1,024
Max input8,192 tokens
Price$0.01 / 1M tokens
Request parameters
inputmodelencoding_format
ArchitectureTransformer
Categoryembedding
ReleasedJan 27, 2024
Modalities
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Capabilities
Embeddings are fixed-length vectors — compare them with cosine similarity for semantic search, RAG retrieval, clustering, and deduplication. Embed queries and documents with the same model, or the distances are meaningless.
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