BGE M3 Multilingual

Model Information

Display Name: BGE M3 Multilingual

API Model ID: BAAI/bge-m3

Category: Text To Embedding

Description: BGE-M3 is a versatile multilingual embedding model supporting 100+ languages with multiple retrieval functionalities. **Key Features:** - Multi-Functionality: Dense, sparse, and multi-vector retrieval - Multi-Linguality: 100+ languages supported - Multi-Granularity: Handle inputs up to 8192 tokens - State-of-the-art multilingual performance **Use Cases:** - Multilingual semantic search - Cross-language document retrieval - Global RAG applications - International content matching **Performance:** - Best open-source multilingual embedding model - Excellent for Portuguese and other languages

Context Window: 8,192 tokens

How to Use This Model

To use BGE M3 Multilingual via the HInow.ai API, use the model ID: BAAI/bge-m3

API Request Example (Chat/Text)


POST https://api.hinow.ai/v1/chat/completions
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

{
  "model": "BAAI/bge-m3",
  "messages": [
    {"role": "user", "content": "Your message here"}
  ]
}
              

Pricing

  • input: $1.35

Quick Reference

To use this model, set: "model": "BAAI/bge-m3"

Featured: No

Documentation: https://hinow.ai/models/BAAI/bge-m3

API Endpoint: https://api.hinow.ai/v1

Back to Models
BGE M3 Multilingual

BGE M3 Multilingual

BAAI/bge-m3

$1.35
input

About

BGE-M3 is a versatile multilingual embedding model supporting 100+ languages with multiple retrieval functionalities.

Key Features:

  • Multi-Functionality: Dense, sparse, and multi-vector retrieval
  • Multi-Linguality: 100+ languages supported
  • Multi-Granularity: Handle inputs up to 8192 tokens
  • State-of-the-art multilingual performance

Use Cases:

  • Multilingual semantic search
  • Cross-language document retrieval
  • Global RAG applications
  • International content matching

Performance:

  • Best open-source multilingual embedding model
  • Excellent for Portuguese and other languages

Capabilities

Text To Embedding
Context8K tokens

Code Examples

curl -X POST https://api.hinow.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $HINOW_API_KEY" \
  -d '{
    "model": "BAAI/bge-m3",
    "input": "Your input here"
  }'