Nomic vs OpenAI Embeddings

Nomic vs OpenAI Embeddings

In the world of text embeddings, the Nomic vs OpenAI Embeddings debate marks a pivotal shift towards open-source alternatives. We stand on the brink of a new era in Natural Language Processing (NLP) as Nomic Embed bursts onto the scene, challenging the dominance of OpenAI’s embeddings. This is not just another text embedding model; this is the harbinger of an open source revolution in the NLP space.

Open Source, Open Data, Open Training Code

Key Features of Nomic vs OpenAI Embeddings. Nomic Embed is not just any text embedding model—it’s the first to be:

  • Open Source: In the spirit of collaborative innovation, Nomic Embed has been made completely open source, allowing developers and researchers to peek under the hood, tweak, and improve upon the existing model.
  • Open Data: The data used to train Nomic Embed is not shrouded in secrecy. It is open, providing transparency and the ability for audits, ensuring that it aligns with ethical AI guidelines.
  • Open Training Code: Reproducibility is key in scientific endeavors. By releasing the training code, Nomic ensures that results can be reproduced and verified by anyone, anywhere.

Understanding the Nomic vs OpenAI Embeddings Performance Gap

Model NameMTEB ScoreLoCo ScoreJinaLC Score
Nomic Embed62.3985.5354.16
Jina Base V262.3985.4551.9
The scores of different models on three different tasks or benchmarks. The ‘Nomic Embed’ model outperforms the ‘text-embedding-ada’ model from OpenAI in the MTEB and JinaLC benchmarks while showing a competitive edge in the LoCo benchmark.

A Leap in Context-Length

What sets Nomic Embed apart is its impressive 8192 context-length, outstripping OpenAI’s Ada-002 and text-embedding-3-small in both short and long context tasks. This is a monumental stride forward, as the ability to understand and encode longer contexts is crucial for complex NLP applications.

Fully Reproducible and Auditable

Transparency is not just a buzzword for Nomic Embed; it’s a foundational principle. By releasing the model weights and training code under an Apache-2 license, along with the curated training data, Nomic Embed ensures full reproducibility and auditability, fostering trust and reliability in its results.

Ready for Production and Enterprise

Nomic Embed transitions from theory to practice effortlessly with the Nomic Atlas Embedding API, offering general availability for production workloads with 1 million free tokens included. For enterprise solutions, Nomic Atlas Enterprise stands ready to deliver secure, compliant services.

The Future of Text Embeddings

Text embeddings play a critical role in modern NLP applications, from retrieval-augmented-generation (RAG) for Large Language Models (LLMs) to semantic search. They allow us to transform complex sentences or documents into low-dimensional vectors that can be used in a myriad of downstream applications like clustering, classification, and information retrieval.

Until now, OpenAI’s text-embedding-ada-002 has been the go-to for long-context text embedding models. However, its closed source nature and the inaccessibility of its training data have been limitations. Nomic Embed not only addresses these issues but also surpasses the performance benchmarks set by its predecessors.


Nomic-embed is changing the game. It’s not just challenging OpenAI’s embeddings; it’s setting a new standard for openness, transparency, and performance in the NLP field. It’s a giant leap for text embeddings, and potentially, a small step towards a more open, collaborative future for AI.

As we embrace this exciting new tool, one thing is clear: the future of NLP looks more accessible, auditable, and performant, thanks to Nomic Embed.

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