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 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.
