BAAI: bge-large-en-v1.5: AI model
bge-large-en-v1.5 is a text-embedding model that turns English text into 1024-dimensional dense vectors. For semantic search, RAG and clustering, reachable from inside Iran through Hormouz.
- Context window:
- 512 tokens
- Input:
- $0.01/1M tokens
- Output:
- $0/1M tokens
- Type:
- embedding
- Embeddings
The bge-large-en-v1.5 model maps English sentences, paragraphs and documents into a 1024-dimensional dense vector. The large BGE encoder delivers high-fidelity semantic embeddings for retrieval, document matching and downstream NLP. It produces 1024-dimensional dense embedding vectors well suited to retrieval, semantic search, clustering and ranking.
The model handles up to 8192 tokens of input per request. Through Hormouz it is served behind an OpenAI-compatible embeddings API. Point your base URL at Hormouz, drop in a Hormouz key and send requests to /embeddings. Pricing is usage-based at $0.01 per million input tokens.
Hormouz provides stable access to the model from inside Iran, with no foreign credit card or circumvention tooling required.
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...
Quickstart
curl https://api.hormouz.net/v1/embeddings \
-H "Authorization: Bearer ng-YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"baai/bge-large-en-v1.5","input":"The quick brown fox"}'Specs
| Vendor | BAAI |
|---|---|
| Type | Embedding |
| Context window | 512 tokens |
| Pricing | $0.01/M in · $0.00/M out |
| API | embeddings |
Code example
curl https://api.hormouz.net/v1/embeddings \
-H "Authorization: Bearer ng-YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"baai/bge-large-en-v1.5","input":"The quick brown fox"}'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-large-en-v1.5", input="The quick brown fox") print(resp.data[0].embedding)
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-large-en-v1.5",
input: "The quick brown fox",
});
console.log(resp.data[0].embedding);Buy the BAAI: bge-large-en-v1.5 API, paid in rial
- Buy credit with an Iranian bank card; no foreign card or account needed.
- One Hormouz key reaches BAAI: bge-large-en-v1.5 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.
Use via Hormouz
Connect to this model and 600+ others with one API key, then see usage down to each request.
Request company accessFAQ
- What is bge-large-en-v1.5 and what is it good for?
- bge-large-en-v1.5 is a text-embedding model that turns text into 1024-dimensional dense vectors. Common uses are semantic search, retrieval for RAG, clustering and similarity scoring.
- What are the embedding dimension and maximum input length of bge-large-en-v1.5?
- Each output vector has 1024 dimensions, and the model accepts up to 8192 tokens of input per request.
- How do I call bge-large-en-v1.5 through Hormouz?
- Send your input text to the /embeddings endpoint with the model id "baai/bge-large-en-v1.5". Because the API is OpenAI-compatible, just point the base URL at Hormouz and swap in your Hormouz key. The same OpenAI embeddings client code works unchanged.
- How much does bge-large-en-v1.5 cost?
- Pricing is usage-based at $0.01 per million input tokens. There is no fixed monthly fee. You pay only for what you use.
- How do I access bge-large-en-v1.5 from inside Iran?
- Create an account on Hormouz and generate an API key. Hormouz provides a stable path to the model from inside Iran, with no foreign credit card required.
Updated 2026-10-13