Cosmos-Predict2.5-2B
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Cosmos-Reason1-7B
NVIDIANVIDIA 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.
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View details ↗Cosmos-Reason2-2B
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View details ↗Cosmos-Reason2-32B
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View details ↗Cosmos-Reason2-8B
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View details ↗**Cosmos-Tokenize1** is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation. This model is ready for commercial use. Our tokenizers come in two types: **Continuous** (C) and **Discrete** (D), each with **Image** (I) and **Video** (V) variants: * Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like Stable Diffusion. These embeddings are suitable for models that generate data by sampling from continuous distributions. * Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as VideoPoet. This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models. **Model**: Cosmos-Tokenize1-CV8x8x8-360p, a continuous video tokenizer with 8x temporal compression, and 8x8 spatial compression rate.
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View details ↗Cosmos-Tokenizer-Surg
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View details ↗Cosmos-Transfer-LidarGen
NVIDIACosmos-Transfer-LidarGen generates LiDAR point cloud range maps from multi-view RGB images, enabling 3-view RGB image to LiDAR point cloud generation that bridges the gap between visual perception and 3D spatial understanding. This model takes multi-view RGB images as input and generates corresponding LiDAR range maps with spatial correspondence. Cosmos LidarGen was developed by NVIDIA as a part of the Cosmos foundation model family. This model is released under the NVIDIA Open Model License. For a custom license, please contact cosmos-license@nvidia.com. Research related to autonomous driving; enabling users to generate LiDAR point clouds from multi-view RGB images for 3D spatial understanding and perception tasks.
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View details ↗Cosmos-Transfer1-7B
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View details ↗Cosmos-Transfer2.5-2B
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View details ↗Cosmos3-Edge
NVIDIA**Model Collection** | **Code** | **White Paper** | **Website** Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning. > **Update — August 25, 2026:** The Cosmos3-Edge generator checkpoint, runtime defaults, usage examples, and benchmark results have been updated. Users pulling from `main` should refresh their local snapshot. See the update announcement for compatibility and reproducibility details.
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