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Comparison of Intfloat: Multilingual-E5-Large, Intfloat: E5-Large-v2, Google: Gemini Embedding 2
3 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×intfloatIntfloat: E5-Large-v2Remove ×googleGoogle: Gemini Embedding 2Remove ×
What’s differentThe 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…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…Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It…
DateJun 4, 2024ListedNov 22, 2023ListedMay 20, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.20 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →1 endpoint →
Context window5125128.2K
Max output4604607.4K
Parameters———
Image inputNot reportedNot reportedSupported
Audio inputNot reportedNot reportedSupported
Video inputNot reportedNot reportedSupported
Open weightsSupportedSupportedNot reported

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