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Comparison of Intfloat: E5-Base-v2, Intfloat: Multilingual-E5-Large, Krea 3 Edit
3 modelsintfloatIntfloat: E5-Base-v2Remove ×intfloatIntfloat: Multilingual-E5-LargeRemove ×kreaKrea 3 EditRemove ×
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…Edits and combines reference images using text instructions, with optional prompt expansion, output-size controls, and reproducible generation through a seed.
DateNov 22, 2023ListedJun 4, 2024ListedOct 14, 2026Listed
Listed rate$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.025/ megapixelfal · all routes ↗Output + reference images
Provider endpoints1 endpoint →1 endpoint →1 endpoint →
Context window512512—
Max output460460—
Parameters———
Open weightsSupportedSupportedNot reported

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