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| 3 models | intfloatmultilingual-e5-large-instructRemove × | googleembeddinggemma-300mRemove × | googleembeddinggemma-2Remove × |
|---|---|---|---|
| What’s different | The 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 is a 300M parameter multilingual open embedding model from Google DeepMind, designed for efficient deployment even on low-resource devices, producing high-quality text vector representations… | 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,… |
| Date | Apr 17, 2025Listed | Sep 16, 2025Listed | Oct 8, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | See provider quotes | See provider quotes |
| Provider endpoints | 0 endpoints → | 0 endpoints → | 0 endpoints → |
Specifications | |||
| Context window | 512 | 2K | 8.2K |
| Max output | — | — | — |
| Parameters | — | 300M | — |