GTE vs Jina Embeddings

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

功能对比

GTE 与 Jina Embeddings 功能对比
维度	GTE	Jina Embeddings
Dimensions	1024	768
License	Apache-2.0	Apache-2.0
Max tokens	8192	8192
Open weights	Yes	Yes
Provider	Alibaba	Jina AI

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 Jina Embeddings 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.

Jina Embeddings

model · Embedding Models

Jina Embeddings are embedding models built with long-context inputs as a design goal rather than a limitation to work around, so a whole document section can be embedded instead of a truncated fragment. It solves the chunking problem: when the encoder caps at a few hundred tokens, long documents must be split, and the split loses the context that made the passage meaningful. It exists because retrieval quality is often limited by what had to be discarded before embedding rather than by the model itself.