Embedding model family that ranks well on retrieval benchmarks.
| Критерий | BGE | Cohere Embed |
|---|---|---|
| Dimensions | 1024 | 1024 |
| License | MIT | Proprietary |
| Max tokens | 512 | 512 |
| Open weights | Yes | No |
| Provider | BAAI | Cohere |
Закрашенные строки совпадают у обеих; различия в остальных строках и определяют рекомендацию ниже.
Сформировано из различий выше по фиксированным правилам, а не моделью.
Choose BGE if…
Ни одна из зафиксированных характеристик не говорит в его пользу.
Choose Cohere Embed if…
Ни одна из зафиксированных характеристик не говорит в его пользу.
model · Embedding Models
BGE is a family of embedding models trained specifically for retrieval, available in sizes small enough to run locally. It solves the problem that a general-purpose embedding model is not optimised for the asymmetric comparison a search query and a document require. It exists because retrieval quality is set at the embedding step, so a model trained for it improves everything downstream without changing anything else.
model · Embedding Models
Cohere Embed is a hosted embedding model that distinguishes the query from the document being indexed, and supports a hundred languages in one model. It solves the asymmetry problem explicitly: a question and a passage are not the same kind of text, and encoding them with the same mode is a compromise. It exists because multilingual retrieval otherwise means either a separate model per language or one model doing all of them badly.
