跳到正文
菜单
简体中文

GTE vs Cohere Embed

Embedding model family from Alibaba, trained for retrieval and ranking.

功能对比

GTE 与 Cohere Embed 功能对比
维度 GTE Cohere Embed
Dimensions 1024 1024
License Apache-2.0 Proprietary
Max tokens 8192 512
Open weights Yes No
Provider Alibaba Cohere

灰底行表示两者相同;有差异的行才是下方推荐结论的依据。

该选哪个?

由上述差异按固定规则生成,不是模型生成的。

Choose GTE if…

已记录的属性中,没有任何一项显示它更有优势。

Choose Cohere Embed if…

已记录的属性中,没有任何一项显示它更有优势。

GTE

model · Embedding Models

GTE is a family of text embedding models trained for retrieval, with variants covering English and multilingual use and long inputs. It solves the same problem as the other retrieval-focused embedding families: a general-purpose encoder is not optimised for the asymmetric comparison of a short query against a long document. It exists because retrieval quality is decided at the embedding step, and a family that scores well on public retrieval benchmarks is the cheapest way to improve everything downstream.

Cohere Embed

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.