intfloat

Access 3 intfloat models through the OpenRouter unified API including E5-Large-v2, E5-Base-v2, and Multilingual-E5-Large. Compare pricing, context windows, benchmarks, and capabilities between different intfloat models.

intfloat tokens processed on OpenRouter

Intfloat: E5-Large-v2E5-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.
by intfloat

intfloat

Access 3 intfloat models through the OpenRouter unified API including E5-Large-v2, E5-Base-v2, and Multilingual-E5-Large. Compare pricing, context windows, benchmarks, and capabilities between different intfloat models.

intfloat tokens processed on OpenRouter

Intfloat: E5-Large-v2E5-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.
by intfloat

Nov 18, 2025

512 context

$0.01

Intfloat: E5-Base-v2E5-Base-v2

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.

by intfloatNov 18, 2025512 context$0.005

Intfloat: Multilingual-E5-LargeMultilingual-E5-Large

The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and large-scale data search.

by intfloatNov 18, 2025512 context$0.01