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| 3 models | googleembeddinggemma-2Remove × | thenlpergte-baseRemove × | intfloatIntfloat: Multilingual-E5-LargeRemove × |
|---|---|---|---|
| What’s different | 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,… | 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… | 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… |
| Date | Oct 8, 2026Listed | Nov 22, 2023Listed | Jun 4, 2024Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 0 endpoints → | 1 endpoint → | 1 endpoint → |
Specifications | |||
| Context window | 8.2K | 512 | 512 |
| Max output | — | 460 | 460 |
| Parameters | — | — | — |
| Open weights | Not reported | Supported | Supported |