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| 3 models | intfloatmultilingual-e5-large-instructRemove × | thenlpergte-largeRemove × | nvidiaNemotron-3-Embed-8BRemove × |
|---|---|---|---|
| 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:”… | 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… | Nemotron-3-Embed-8B is a multilingual text embedding model from NVIDIA, based on Ministral-3-8B, that maps text into 4096-dimensional dense vectors for retrieval and semantic similarity. Spanning 34… |
| Date | Apr 17, 2025Listed | Nov 22, 2023Listed | Jul 16, 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 | 512 | 32.8K |
| Max output | — | 460 | — |
| Parameters | — | — | 8B |
| Open weights | Not reported | Supported | Not reported |