BGE Large EN v1.5

Overview

1024-dimension English embeddings, and still one of the most widely benchmarked retrieval models in production. The 512-token window forces chunking, which is why the newer entries above exist — but an index already built on it stays comparable.

Specs

Field	Value
Model ID	`bge-large-en`
Best for	English-only retrieval, existing BGE indexes
Context window	512
Input modalities	Text
Output modalities	Text
Speed	Fast
Cost band	Cheap
Release stage	Stable

Pricing

	Per 1M tokens
Input	$0.0135
Output	$0.00

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.embeddings.create(
    model="bge-large-en",
    input=["first document", "second document"],
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=search-documents&quality_tier=strong"

​Overview

​Specs

​Pricing

​Example

​Agent query example

Overview

Specs

Pricing

Example

Agent query example