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Comparison of Intfloat: Multilingual-E5-Large, embeddinggemma-2, Respan: Span-02
3 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×googleembeddinggemma-2Remove ×respanRespan: Span-02Remove ×
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…EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,…Set a thinking deadline in milliseconds; returns typed decisions or direct numbers with confidence, and accepts up to four images.
DateJun 4, 2024ListedOct 8, 2026ListedOct 16, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotes$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →0 endpoints →1 endpoint →
Context window5128.2K131.1K
Max output460—118K
Parameters———
Image inputNot reportedNot reportedSupported
Open weightsSupportedNot reportedNot reported

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