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Jina Embeddings vs Cohere Embed

Embedding models designed around long inputs and multilingual coverage.

Comparaison des critères

Comparaison de Jina Embeddings et Cohere Embed
Critère Jina Embeddings Cohere Embed
Dimensions 768 1024
License Apache-2.0 Proprietary
Max tokens 8192 512
Open weights Yes No
Provider Jina AI Cohere

Les lignes grisées sont identiques pour les deux ; ce sont les lignes qui diffèrent qui motivent la recommandation ci-dessous.

Lequel choisir ?

Généré à partir des différences ci-dessus par des règles structurées, et non par un modèle.

Choose Jina Embeddings if…

Aucun des attributs enregistrés ne le distingue à son avantage.

Choose Cohere Embed if…

Aucun des attributs enregistrés ne le distingue à son avantage.

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.