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Nomic Embed vs Cohere Embed

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

Comparativa de características

Comparativa de Nomic Embed y Cohere Embed
Característica Nomic Embed Cohere Embed
Dimensions 768 1024
License Apache-2.0 Proprietary
Max tokens 8192 512
Open weights Yes No
Provider Nomic AI Cohere

Las filas sombreadas son idénticas en ambos; las filas que difieren son las que sustentan la recomendación de abajo.

¿Cuál deberías elegir?

Generado a partir de las diferencias anteriores mediante reglas estructuradas, no por un modelo.

Choose Nomic Embed if…

Ninguno de los atributos registrados lo distingue a su favor.

Choose Cohere Embed if…

Ninguno de los atributos registrados lo distingue a su favor.

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.