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Jina Embeddings vs Cohere Embed

Embedding models designed around long inputs and multilingual coverage.

Сравнение по критериям

Сравнение Jina Embeddings и Cohere Embed
Критерий Jina Embeddings Cohere Embed
Dimensions 768 1024
License Apache-2.0 Proprietary
Max tokens 8192 512
Open weights Yes No
Provider Jina AI Cohere

Закрашенные строки совпадают у обеих; различия в остальных строках и определяют рекомендацию ниже.

Что выбрать?

Сформировано из различий выше по фиксированным правилам, а не моделью.

Choose Jina Embeddings 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.

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.

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.