Intfloat: E5-Base-v2
1 provider live- 1 live provider
The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring, retrieval and clustering.
- Context
- 512
- tokens
- Dimensions
- 768
- per vector
- Input
- $0.005
- 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 | $0.005 |
Price history
No price changes since Oct 25, 2026.
- DeepInfra$0.005 in
Questions
How much does Intfloat: E5-Base-v2 cost?
Intfloat: E5-Base-v2 costs $0.005 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="intfloat/e5-base-v2",
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: "intfloat/e5-base-v2",
input: "Hello",
});curl https://api.llm.buzz/api/v1/embeddings \
-H "Authorization: Bearer $LLM_BUZZ_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "intfloat/e5-base-v2", "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 e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring, retrieval and clustering.
- Model ID
- intfloat/e5-base-v2
- Takes
- Text
- Answers
- Embeddings
- Context
- 512 tokens
- Dimensions
- 768
- Weights
- Closed
- On llm.buzz since
- Oct 25, 2026