---
id: "intfloat/e5-large-v2"
also: ["intfloat/e5-large-v2-20251117", "intfloat/e5-large-v2"]
cheapest_list_usd_per_m: "0.01 in, 0.0 out, at deepinfra on openrouter"
cheapest_effective_usd_per_m: "0.0028 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
---
# Intfloat: E5-Large-v2

The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.