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| 3 models | intfloatmultilingual-e5-large-instructRemove × | BAAIbge-m3Remove × | 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:”… | BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in… | 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 | May 27, 2024Listed | Oct 8, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes |
| Provider endpoints | 0 endpoints → | 1 endpoint → | 0 endpoints → |
Specifications | |||
| Context window | 512 | 8.2K | 8.2K |
| Max output | — | 7.4K | — |
| Parameters | — | — | — |
| Open weights | Not reported | Supported | Not reported |