BAAI
BAAI: bge-m3
baai/bge-m3
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