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Nomic Embed vs Cohere Embed

Open embedding model with an auditable training set and long context.

Merkmalsvergleich

Merkmalsvergleich von Nomic Embed und Cohere Embed
Merkmal Nomic Embed Cohere Embed
Dimensions 768 1024
License Apache-2.0 Proprietary
Max tokens 8192 512
Open weights Yes No
Provider Nomic AI Cohere

Schattierte Zeilen sind bei beiden gleich; die abweichenden Zeilen begründen die Empfehlung unten.

Welche sollten Sie wählen?

Aus den Unterschieden oben durch feste Regeln erzeugt, nicht von einem Modell.

Choose Nomic Embed if…

Keines der erfassten Merkmale zeichnet es zu Ihren Gunsten aus.

Choose Cohere Embed if…

Keines der erfassten Merkmale zeichnet es zu Ihren Gunsten aus.

Nomic Embed

model · Embedding Models

Nomic Embed is an open embedding model released with an open training dataset and reproducible training run, as well as long-context support. It solves two problems at once: most open embedding models publish weights but not the data they were trained on, which makes their behaviour hard to reason about, and most cap inputs short of a document. It exists because an embedding model whose training data is inspectable can be evaluated for the biases that come from that data.

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.