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Comparison of multilingual-e5-large-instruct, embeddinggemma-2, Krea: K3 Edit
3 modelsintfloatmultilingual-e5-large-instructRemove ×googleembeddinggemma-2Remove ×kreaKrea: K3 EditRemove ×
What’s differentThe Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:”…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.
DateApr 17, 2025ListedOct 8, 2026ListedOct 15, 2026Listed
Listed rateSee provider quotesSee provider quotesSee provider quotes
Provider endpoints0 endpoints →0 endpoints →0 endpoints →
Context window5128.2K65.5K
Max output——59K
Parameters———
Image inputNot reportedNot reportedSupported

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