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Comparison of Intfloat: Multilingual-E5-Large, embeddinggemma-2, Krea 3 Edit
3 modelsintfloatIntfloat: Multilingual-E5-LargeRemove ×googleembeddinggemma-2Remove ×kreaKrea 3 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 and combines reference images using text instructions, with optional prompt expansion, output-size controls, and reproducible generation through a seed.
DateJun 4, 2024ListedOct 8, 2026ListedOct 14, 2026Listed
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotes$0.025/ megapixelfal · all routes ↗Output + reference images
Provider endpoints1 endpoint →0 endpoints →1 endpoint →
Context window5128.2K—
Max output460——
Parameters———
Open weightsSupportedNot reportedNot reported

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