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BAAI: bge-m3

Source description (English)

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.

View source on OpenRouter Hugging Face
Overview

Model specifications

Context
8,194 tokens
Maximum output
7,374 tokens
Architecture
text->embeddings
Tokenizer
Other
Knowledge cutoff
Not provided
Moderated
No
OPENROUTER

Complete pricing

Synchronized OpenRouter rates. Token prices are shown per one million tokens.

Input
$0.01
per 1M tokens
API

Quick start

Set OPENROUTER_API_KEY locally. Python requires requests; JavaScript runs in Node.js. Keep the key on the server.

API documentation

curl --fail-with-body https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baai/bge-m3","input":"Your text here"}'
Capabilities

Input → Output

Input
text
Output
embeddings
API

Supported API parameters

Available providers

2 providers

Checked: September 16, 2026

Live providers on OpenRouter

Provider availability, latency, throughput and routing can change continuously. Open the source page for current operational data.

OpenRouter

Parasail

Not provided
Context
8K
Maximum output
7K
Input
$0.01
Output
Free
Cache read
Not provided
Cache write
Not provided

DeepInfra

fp32
Context
8K
Maximum output
7K
Input
$0.01
Output
Free
Cache read
Not provided
Cache write
Not provided
baai

2 models

All models
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BAAI: bge-base-en-v1.5

The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features...

Context
1K
Input
text
Output
embeddings
Input: $0.005per 1M tokens
View model details →
baai logobaai

BAAI: bge-large-en-v1.5

The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks...

Context
1K
Input
text
Output
embeddings
Input: $0.01per 1M tokens
View model details →