Nomic Embed vs OpenAI Text Embedding 3

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

Comparação de características

Comparação de Nomic Embed e OpenAI Text Embedding 3
Característica	Nomic Embed	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	Nomic AI	OpenAI

As linhas sombreadas são idênticas nos dois; as linhas que diferem são as que sustentam a recomendação abaixo.

Qual deve escolher?

Gerado a partir das diferenças acima por regras estruturadas, não por um modelo.

Choose Nomic Embed if…

Nenhum dos atributos registados o distingue a seu favor.

Choose OpenAI Text Embedding 3 if…

Nenhum dos atributos registados o distingue a seu 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.

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.