---
id: "baai/bge-large-en-v1.5"
also: ["baai/bge-large-en-v1.5-20251117", "BAAI/bge-large-en-v1.5"]
cheapest_list_usd_per_m: "0.01 in, 0.0 out, at deepinfra on openrouter"
cheapest_effective_usd_per_m: "0.0083 in, 0.0 out, at deepinfra/us on openrouter"
context: 512
max_output: 460
input: ["text"]
output: ["embeddings"]
released: "2025-11-18"
api: ["/api/v1/embeddings"]
endpoints: 2
gateways: "openrouter (2)"
no_training: 1
trains: 0
retains_prompts: 0
---
# BAAI: bge-large-en-v1.5

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...