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| 3 models | thenlpergte-largeRemove × | intfloatmultilingual-e5-large-instructRemove × | nvidiaNemotron-3-Embed-1B-BF16Remove × |
|---|---|---|---|
| What’s different | 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 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:”… | Nemotron-3-Embed-1B-BF16 is a compact multilingual text embedding model from NVIDIA, pruned and distilled from Ministral-3 to ~1B parameters, that maps text into 2048-dimensional dense vectors for retrieval… |
| Date | Nov 22, 2023Listed | Apr 17, 2025Listed | Jul 15, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes | See provider quotes |
| Provider endpoints | 1 endpoint → | 0 endpoints → | 0 endpoints → |
Benchmarks Each result retains its evaluation configuration | |||
| FreshStack · MTEB 2.14.3 ↗MTEB 2.14.3; test/default; dataset 7a20df1a | Not reported | Not reported | 0.4173 ↗Revision f8801746; test/defaultContributor reported |
Specifications | |||
| Context window | 512 | 512 | 32.8K |
| Max output | 460 | — | — |
| Parameters | — | — | 1B |
| Open weights | Supported | Not reported | Not reported |