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.

• Context window: 8,192 tokens
• Input: text
• Output: embeddings
• Pricing: $0.01/M input tokens

View on OpenRouter. Model data sourced from OpenRouter.