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Comparison of embeddinggemma-2, Intfloat: Multilingual-E5-Large, Krea: K3 Edit
3 modelsgoogleembeddinggemma-2Remove ×intfloatIntfloat: Multilingual-E5-LargeRemove ×kreaKrea: K3 EditRemove ×
What’s differentEmbeddingGemma 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,…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…Edits one to eight reference images from a text prompt, returning one image.
DateOct 8, 2026ListedJun 4, 2024ListedOct 15, 2026Listed
Listed rateSee provider quotes$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotes
Provider endpoints0 endpoints →1 endpoint →0 endpoints →
Context window8.2K51265.5K
Max output—46059K
Parameters———
Image inputNot reportedNot reportedSupported
Open weightsNot reportedSupportedNot reported

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