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Comparison of Intfloat: E5-Base-v2, Intfloat: Multilingual-E5-Large, Respan: Span-02
3 modelsintfloatIntfloat: E5-Base-v2Remove ×intfloatIntfloat: Multilingual-E5-LargeRemove ×respanRespan: Span-02Remove ×
What’s differentThe 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…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…Set a thinking deadline in milliseconds; returns typed decisions or direct numbers with confidence, and accepts up to four images.
DateNov 22, 2023ListedJun 4, 2024ListedOct 16, 2026Listed
Listed rate$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →1 endpoint →
Context window512512131.1K
Max output460460118K
Parameters———
Image inputNot reportedNot reportedSupported
Open weightsSupportedSupportedNot reported

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