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.
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Prompt
16.1M
Completion
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Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.