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| 3 models | intfloatIntfloat: Multilingual-E5-LargeRemove × | thenlpergte-baseRemove × | googleGoogle: Gemini Embedding 2Remove × |
|---|---|---|---|
| What’s different | 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… | The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE… | Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It… |
| Date | Jun 4, 2024Listed | Nov 22, 2023Listed | May 20, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.20 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 1 endpoint → |
Specifications | |||
| Context window | 512 | 512 | 8.2K |
| Max output | 460 | 460 | 7.4K |
| Parameters | — | — | — |
| Image input | Not reported | Not reported | Supported |
| Audio input | Not reported | Not reported | Supported |
| Video input | Not reported | Not reported | Supported |
| Open weights | Supported | Supported | Not reported |