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Comparison of Intfloat: Multilingual-E5-Large, embeddinggemma-2, Krea: K3 Edit
3 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×googleembeddinggemma-2Remove ×kreaKrea: K3 EditRemove ×
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,…Edits one to eight reference images from a text prompt, returning one image.
DateJun 4, 2024ListedOct 8, 2026ListedOct 15, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotesSee provider quotes
Provider endpoints1 endpoint →0 endpoints →0 endpoints →
Context window5128.2K65.5K
Max output460—59K
Parameters———
Image inputNot reportedNot reportedSupported
Open weightsSupportedNot reportedNot reported

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