BAAI: bge-large-en-v1.5
1 provider live- 1 live provider
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 in English.
- Context
- 512
- tokens
- Dimensions
- 1,024
- per vector
- Input
- $0.01
- per million tokens
Providers
Ranked by price and speed. Requests go to the best healthy provider, then fail over down the list.| # | Provider | Input |
|---|---|---|
| 1 | Data policy not declared | $0.01 |
Price history
No price changes since Oct 25, 2026.
- DeepInfra$0.01 in
Questions
How much does BAAI: bge-large-en-v1.5 cost?
BAAI: bge-large-en-v1.5 costs $0.01 per million input tokens at DeepInfra, the provider requests go to first.
Do providers train on my prompts?
Each provider declares it, and the table above shows every declaration. You can limit a request to providers that keep nothing, or to providers in the EU.
Similar models
Call it
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.llm.buzz/api/v1",
api_key=os.environ["LLM_BUZZ_KEY"],
)
result = client.embeddings.create(
model="baai/bge-large-en-v1.5",
input="Hello",
)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.llm.buzz/api/v1",
apiKey: process.env.LLM_BUZZ_KEY,
});
const result = await client.embeddings.create({
model: "baai/bge-large-en-v1.5",
input: "Hello",
});curl https://api.llm.buzz/api/v1/embeddings \
-H "Authorization: Bearer $LLM_BUZZ_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "baai/bge-large-en-v1.5", "input": "Hello"}'Endpoints
POST /api/v1/embeddingsAuthentication
Send your llm.buzz key as a Bearer token: Authorization: Bearer $LLM_BUZZ_KEY. Create keys in the console. The same key calls every model; each request pays the price of the provider that answered it.
More in Authentication and the API reference.
README
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 in English.
- Model ID
- baai/bge-large-en-v1.5
- Takes
- Text
- Answers
- Embeddings
- Context
- 512 tokens
- Dimensions
- 1,024
- Weights
- Closed
- On llm.buzz since
- Oct 25, 2026