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BAAI: bge-m3: AI model

bge-m3 is a BAAI embedding model that turns text into 1024-dimension vectors for multilingual search and retrieval, with an 8K context. Reachable from inside Iran through Hormouz.

Context window:
8,194 tokens
Input:
$0.01/1M tokens
Output:
$0/1M tokens
Type:
embedding
  • Embeddings

bge-m3 is an embedding model from BAAI that encodes sentences, paragraphs and long documents into a 1024-dimensional dense vector space. Those vectors capture semantic meaning, so texts with similar meaning end up close together.

It is optimised for multilingual retrieval, semantic search and large-context applications, which makes it a strong default for building retrieval-augmented generation (RAG) pipelines, semantic search and clustering. It accepts text input and returns embedding vectors, with an 8K-token context.

Pricing is usage-based: $0.01 per million input tokens and $0 per million output tokens. Hormouz provides stable access to bge-m3 from inside Iran, with no foreign credit card or circumvention tooling required.

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.

Quickstart

curl https://api.hormouz.net/v1/embeddings \
  -H "Authorization: Bearer ng-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baai/bge-m3","input":"The quick brown fox"}'

Specs

VendorBAAI
TypeEmbedding
Context window8,194 tokens
Pricing$0.01/M in · $0.00/M out
APIembeddings

Code example

cURL
curl https://api.hormouz.net/v1/embeddings \
  -H "Authorization: Bearer ng-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baai/bge-m3","input":"The quick brown fox"}'
Python
from openai import OpenAI
client = OpenAI(api_key="ng-YOUR_API_KEY", base_url="https://api.hormouz.net/v1")
resp = client.embeddings.create(model="baai/bge-m3", input="The quick brown fox")
print(resp.data[0].embedding)
JavaScript
import OpenAI from "openai";
const client = new OpenAI({ apiKey: "ng-YOUR_API_KEY", baseURL: "https://api.hormouz.net/v1" });
const resp = await client.embeddings.create({
  model: "baai/bge-m3",
  input: "The quick brown fox",
});
console.log(resp.data[0].embedding);

Buy the BAAI: bge-m3 API, paid in rial

  • Buy credit with an Iranian bank card; no foreign card or account needed.
  • One Hormouz key reaches BAAI: bge-m3 and 600+ other models, and you pay only for what you use.
  • No VPN and no risk of a banned account; just change the base URL to api.hormouz.net/v1.
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FAQ

How much does bge-m3 cost?
Billing is usage-based: $0.01 per million input tokens and $0 per million output tokens. There is no fixed monthly fee. You pay only for what you use, at the rates shown in the Hormouz dashboard.
How do I access bge-m3 from inside Iran?
Create an account on Hormouz and generate an API key. Hormouz provides a stable path to bge-m3 from inside Iran, with no foreign credit card or circumvention tooling required.
Is bge-m3 compatible with OpenAI code?
Yes. Hormouz exposes an OpenAI-compatible API. Just change the base URL to Hormouz and swap in your Hormouz key. Your existing OpenAI SDKs and code run without a rewrite.
What is bge-m3 used for?
It converts text into 1024-dimension embedding vectors for semantic search, retrieval-augmented generation (RAG) and clustering. It is optimised for multilingual retrieval and long-context inputs, with an 8K-token context.

Updated 2026-10-13