Cosmos-Reason1

Cosmos | Code | Paper | Paper Website

Model Overview

Description:

NVIDIA Cosmos Reason – an open, customizable, 7B-parameter reasoning vision language model (VLM) for physical AI and robotics - enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next.

Cosmos Reason excels at navigating the long tail of diverse scenarios of the physical world with spatial-temporal understanding. Cosmos Reason is post-trained with physical common sense and embodied reasoning data with supervised fine-tuning and reinforcement learning. It uses chain-of-thought reasoning capabilities to understand world dynamics without human annotations.

Given a video/image and a text prompt, the model first converts the video/image into tokens using a vision encoder and a special translator called a projector. These video tokens are combined with the text prompt and fed into the core model, which uses a mix of LLM modules and techniques. This enables the model to think step-by-step and provide detailed, logical responses.

Cosmos Reason can be used for robotics and physical AI applications including:

• Data curation and annotation — Enable developers to automate high-quality curation and annotation of massive, diverse training datasets.
• Robot planning and reasoning — Act as the brain for deliberate, methodical decision-making in a robot vision language action (VLA) model. Now robots such as humanoids and autonomous vehicles can interpret environments and given complex commands, break them down into tasks and execute them using common sense, even in unfamiliar environments.
• Video analytics AI agents — Extract valuable insights and perform root-cause analysis on massive volumes of video data. These agents can be used to analyze and understand recorded or live video streams across city and industrial operations.

The model is ready for commercial use.

Model Developer: NVIDIA

Model Versions

The Cosmos-Reason1 includes the following model:

• Cosmos-Reason1-7B: Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video.

License:

This model is released under the NVIDIA Open Model License. Additional Information: Apache License 2.0.

For a custom license, please contact cosmos-license@nvidia.com.

Under the NVIDIA Open Model License, NVIDIA confirms:

• Models are commercially usable.
• You are free to create and distribute Derivative Models.
• NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.

Important Note: If You bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism (collectively “Guardrail”) contained in the Model without a substantially similar Guardrail appropriate for your use case, your rights under this Agreement NVIDIA Open Model License Agreement will automatically terminate.

Deployment Geography:

Global

Use Case:

Physical AI: Space, time, fundamental physics understanding and embodied reasoning, encompassing robotics, and autonomous vehicles (AV).

Release Date:

• Github: 05/17/2025
• Huggingface:
  • 08/01/2025. Shipped a few improvements which include captions with temporal timestamp, Set of Mark prompting.
  • 06/10/2025. Enhanced critic capability for physical plausibility.
  • 05/17/2025. Initial release.

Model Architecture:

Architecture Type: A Multi-modal LLM consists of a Vision Transformer (ViT) for vision encoder and a Dense Transformer model for LLM. Network Architecture: Qwen2.5-VL-7B-Instruct.

Cosmos-Reason-7B is post-trained based on Qwen2.5-VL-7B-Instruct and follows the same model architecture.

Number of model parameters:

Cosmos-Reason1-7B:

• Vision Transformer (ViT): 675.76M (675,759,104)
• Language Model (LLM): 7.07B (7,070,619,136)
• Other components (output projection layer): 545.00M (544,997,376)

Input

Input Type(s): Text+Video/Image

Input Format(s):

• Text: String
• Video: mp4
• Image: jpg

Input Parameters:

• Text: One-dimensional (1D)
• Video: Three-dimensional (3D)
• Image: Two-dimensional (2D)

Other Properties Related to Input:

• Use FPS=4 for input video to match the training setup.
• Append Answer the question in the following format: \nyour reasoning\n\n\n\nyour answer\n. in the system prompt to encourage long chain-of-thought reasoning response.

Output

Output Type(s): Text

Output Format: String

Output Parameters: Text: One-dimensional (1D)

Other Properties Related to Output:

• Recommend using 4096 or more output max tokens to avoid trun