
A state-of-the-art general purpose MoE VLM ideal for chat, agentic and instruction based use cases.

A general purpose VLM ideal for chat and instruction based use cases

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

FLUX.1 Kontext is a multimodal model that enables in-context image generation and editing.

Multilingual, cross-lingual embedding model for long-document QA retrieval, supporting 26 languages.

Multi-modal model to classify safety for input prompts as well output responses.

Multimodal question-answer retrieval representing user queries as text and documents as images.

Efficient multimodal model excelling at multilingual tasks, image understanding, and fast-responses

Powerful, multimodal language model designed for enterprise applications, including software development, data analysis, and reasoning.

A general purpose multimodal, multilingual 128 MoE model with 17B parameters.

A multimodal, multilingual 16 MoE model with 17B parameters.

The NV-EmbedCode model is a 7B Mistral-based embedding model optimized for code retrieval, supporting text, code, and hybrid queries.

Cutting-edge open multimodal model exceling in high-quality reasoning from images.

Cutting-edge open multimodal model exceling in high-quality reasoning from image and audio inputs.

Multilingual and cross-lingual text question-answering retrieval with long context support and optimized data storage efficiency.


Cutting-edge open multimodal model exceling in high-quality reasoning from images.

English text embedding model for question-answering retrieval.


Generates high-quality numerical embeddings from text inputs.