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Jina Embeddings vs OpenAI Text Embedding 3

Embedding models designed around long inputs and multilingual coverage.

功能对比

Jina Embeddings 与 OpenAI Text Embedding 3 功能对比
维度 Jina Embeddings OpenAI Text Embedding 3
Dimensions 768 1536 by default, 256 to 3072 selectable
License Apache-2.0 Proprietary
Max tokens 8192 8191
Open weights Yes No
Provider Jina AI OpenAI

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 Jina Embeddings if…

已记录的属性中,没有任何一项显示它更有优势。

Choose OpenAI Text Embedding 3 if…

已记录的属性中,没有任何一项显示它更有优势。

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.

OpenAI Text Embedding 3

model · Embedding Models

Text Embedding 3 is OpenAI's hosted embedding family, notable for letting the caller choose a shorter output vector than the model natively produces. It solves the storage problem directly: embedding indexes are sized by dimensions times vectors, so trimming 3072 dimensions to 512 or 256 shrinks the index substantially at a small quality cost. It exists because that trade is one the caller is better placed to make than the model author, and it removes the need to retrain to change it.