BGE M3

Name: BGE M3
Author: BAAI

by BAAI Embeddings

BGE-M3 is BAAI's versatile embedding model supporting dense, sparse, and multi-vector retrieval in a single model. It handles more than 100 languages and long inputs, making it a strong multilingual default for search and RAG.

Publisher: BAAI (opens in a new tab)
API model name: bge-m3
Context window: 8K tokens
Sizes: 568M
Licence: MIT

Run BGE M3

Install it on a Pendra worker, then call it through our SDK with your pdr_sk_ API key.

Read the quickstart

from pendra import Pendra

client = Pendra(api_key="pdr_sk_...")

response = client.embeddings.create(
    model="bge-m3",
    input="The quick brown fox",
)

print(response.data[0].embedding)

import { Pendra } from "pendra";

const client = new Pendra({ apiKey: "pdr_sk_..." });

const response = await client.embeddings.create({
  model: "bge-m3",
  input: "The quick brown fox",
});

console.log(response.data[0].embedding);

Related models

BGE Reranker v2 M3

BAAI bge-reranker-v2-m3

BGE Reranker v2 m3 is BAAI's multilingual reranking model, scoring how well each candidate document answers a query to sharpen retrieval results. It's a lightweight, effective second stage for RAG pipelines across many languages.

568M 8K context

Run BGE M3 on infrastructure you trust.

Deploy a worker, install BGE M3, and start serving it through one private API.

Try it free Read the quickstart