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Introducing Nomic Embed V1

The Open-Weight 8K-Dimensional Embedding Model Revolutionizing Local Inference

shipped Nov 22, 2025buildpaid
Domain rating75Monthly visits3K/mo
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Why it matters

1Achieve state-of-the-art performance with an open-source model for better transparency.
2Handle both short and long context effectively, outperforming leading competitors.
3Utilize flexible embedding dimensions to optimize memory and storage without sacrificing quality.

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

Overview of Nomic Embed V1

Nomic Embed V1 is an innovative open-weight embedding model designed for high-performance local inference. With an extensive 8,192-token context window, this model outperforms other leading embedding solutions, ensuring both audibility and reproducibility in your projects.

  • First fully auditable text embedding model
  • Supports both short (MTEB) and long context (LoCo) benchmarks.
  • Open-source with accessible model weights and training data.

features

Key Features

Nomic Embed V1 comes packed with features that cater to a wide spectrum of needs, from enterprises to developers. Its latest updates, including Matryoshka Representation Learning, bring significant advantages in embedding dimensionality and efficiency.

  • Matryoshka Learning enables dimensions from 64 to 768.
  • Binary embeddings for reduced memory and storage usage.
  • Unified multimodal embedding for semantic searches across images and text.

use cases

Ideal Use Cases

Whether you're building an advanced search engine, enhancing retrieval-augmented generation, or conducting deep research, Nomic Embed V1 serves as the backbone for your needs. Its flexibility and performance make it suitable for various applications.

  • Scalable solutions for search and retrieval-augmented generation.
  • Effective clustering and classification capabilities.
  • Full transparency for researchers requiring reproducibility.

Policies

Pricing Page

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