# BGE-M3

> 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.

**ID**: `baai/bge-m3`  
**Creator**: baai  
**Category**: embedding  
**Context**: 8K tokens  
**Input**: $0.01/M  
**Output**: $0  
**Released**: 2024-01-27  
**Web page**: https://anyrouter.dev/model/baai/bge-m3

**Input modalities**: text  
**Output modalities**: embeddings  

**Tokenizer**: Other

**Capabilities**: embedding

**Supported parameters**: input, model, encoding_format

## Usage

**Endpoint**: `POST https://anyrouter.dev/api/v1/embeddings`

```bash
curl https://anyrouter.dev/api/v1/embeddings \
  -H "Authorization: Bearer $ANYROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "baai/bge-m3",
  "input": "The quick brown fox jumps over the lazy dog"
}'
```

Pass `input` as a string or an array of strings to embed a batch in one call.

## Providers

| Provider | Input | Output |
| --- | --- | --- |
| ovhcloud-byok | $0 | $0 |

## Source

- Upstream docs: https://huggingface.co/BAAI/bge-m3