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Nomic Embed vs OpenAI Text Embedding 3

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

Merkmalsvergleich

Merkmalsvergleich von Nomic Embed und OpenAI Text Embedding 3
Merkmal 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

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

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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 OpenAI Text Embedding 3 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.

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.