Models Model details

The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.

Input
$0.01

/ 1M tokens · Token reference

Output
$0

/ 1M tokens · Token reference

Context window
8.19K

tokens

Embeddings output · Token reference fields do not establish an output-unit price.

Limits

Context

8,194

tokens

Max output

7,374

90% of the context window

Model facts

Author

BAAI

Tokenizer

Other

Modality

Text -> Embeddings

License

MIT

Created

Nov 18, 2025

Hugging Face

BAAI/bge-m3

Publication status

Hugging Face: verifiedLicense: publishedParameter count: not published

Modalities

Input · TextOutput · Embeddings