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| 4 models | intfloatIntfloat: E5-Base-v2Remove × | intfloatIntfloat: E5-Large-v2Remove × | thenlpergte-baseRemove × | BAAIbge-m3-multi-8kRemove × |
|---|---|---|---|---|
| What’s different | The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as… | The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic… | 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… | BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval),… |
| Date | Nov 22, 2023Listed | Nov 22, 2023Listed | Nov 22, 2023Listed | Sep 3, 2026Listed |
API prices USD · provider configuration shown below | ||||
| Listed rate | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 1 endpoint → | 0 endpoints → |
Specifications | ||||
| Context window | 512 | 512 | 512 | 8.2K |
| Max output | 460 | 460 | 460 | — |
| Parameters | — | — | — | — |
| Open weights | Supported | Supported | Supported | Not reported |