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37:["$","$L3a",null,{"hasPartnerEndpoint":false}] 40:T434,| Field | Response | | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------- | | Participation considerations from adversely impacted groups [protected classes](https://calcivilrights.ca.gov/disputeresolution/protected-characteristics/) in model design and testing: | None | | Measures taken to mitigate against unwanted bias: | The training video sources contain multiple physical embodiments and environments including human, car, single arm robot, bimanual robot in indoor and outdoor environments. By training on numerous and various physical interactions and curated datasets, we strive to provide a model that does not possess biases towards certain embodiments or environments. |41:T172f,# Cosmos3-Nano-Reasoner Overview ## Description: NVIDIA Cosmos3-Nano is an open and customizable model for physical AI and robotics that 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. It is part of Cosmos3-Nano, an Omni model capable of reasoning and generation capabilities. The model is ready for commercial use. **Model Developer**: NVIDIA ## Model Versions Cosmos3-Nano is a part of the [Cosmos3 framework](https://github.com/nvidia/cosmos-framework) Cosmos3 includes the following models: * [Cosmos3-Nano](https://huggingface.co/nvidia/Cosmos3-Nano) (Reasoning tower): Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video. * [Cosmos3-Super](https://huggingface.co/nvidia/Cosmos3-Super) (Reasoning tower): Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video. ## Input: Input Type(s): Text, Text+Image, Text+Video Input Format(s): * Text: String * Image: jpg, png, jpeg, webp * Video: mp4 Input Parameters: * Text: One-dimensional (1D) * Image: Two-dimensional (2D) * Video: Three-dimensional (3D) Other Properties Related to Input: * Video inputs are recommended at a frame rate of 4 fps. * Long-context inputs are supported up to 256K tokens. * Image inputs may be passed as files or URLs. * Video inputs should be mp4 and follow the 4 fps recommendation for Reasoner usage. Input Size and Length Limits: * Text: up to 256K tokens in the context window. * Image: standard supported image formats passed as file or URL. * Video: mp4 at the recommended 4 fps. ## Output: Output Type(s): Text Output Format: String Output Parameters: Text: One-dimensional (1D) Other Properties Related to Output: * Default max\_tokens=4096+ is recommended for reasoning outputs; longer outputs may be requested. * Reasoning outputs may include structured reasoning, 2D/3D point localization, and bounding-box coordinates for vision-based tasks. * Outputs are not guaranteed to be correct and should not be treated as safety-certified decisions or ground truth. ## Software Integration: Runtime Engine(s): * vLLM for OpenAI-compatible Reasoner serving. * PyTorch and Cosmos framework workflows for local development and integration. Supported Hardware Microarchitecture Compatibility: * NVIDIA Ampere * NVIDIA Blackwell * NVIDIA Hopper Operating System(s): * Linux. Other operating systems have not been tested. Note: Only BF16 precision is tested. The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. ## Limitations: Cosmos3-Nano-Reasoner may produce incorrect reasoning in challenging scenarios. Object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred. Complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. ## Usage: See Cosmos for details: [https://github.com/nvidia/cosmos](https://github.com/nvidia/cosmos) ### Quality Benchmarks Please see the Cosmos3 technical paper for detailed evaluations of the base model: [https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf) ### License and Terms of Use: GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of the model is governed by [OpenMDW1.1 license](https://openmdw.ai/license/1-1/) 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. ### Deployment Geography: Global ### Release Date: Build.NVIDIA.com 5/31/2026 via [link](https://build.nvidia.com/nvidia/cosmos3-nano-reasoner) Huggingface 5/31/2026 via [link](https://huggingface.co/nvidia/Cosmos3-Nano) Downloadable NIM \- Cosmos3-Reasoner 5/31/2026 via [link](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/cosmos3-reasoner?version=1.7.0) ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).42:T1421,| Field | Response | | :-------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Intended Application & Domain: | Physical AI Reasoning | | Model Type: | Transformer | | Intended Users: | Physical AI developers | | Output: | Text | | Describe how the model works: | 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. | | Technical Limitations: | The model may not follow the video or text input accurately in challenging cases, where the input video shows complex scene composition and temporal dynamics. Examples of challenging scenes include: fast camera movements, overlapping human-object interactions, low lighting with high motion blur, and multiple people performing different actions simultaneously. | | Verified to have met prescribed NVIDIA quality standards: | Yes | | Performance Metrics: | Quantitative and Qualitative Evaluation. Cosmos-Reason2 proposes the embodied reasoning benchmark and physical common sense benchmark to evaluate accuracy with visual question answering. | | Potential Known Risks: | The model's output can generate all forms of texts, including what may be considered toxic, offensive, or indecent. | | Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). Additional Information: [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).43:T563,| Field | Response | | :------------------------------------------------------------------ | :----------------------------------------------------------------------------- | | Generatable or reverse engineerable personal data? | No | | Personal data used to create this model? | None Known | | Was consent obtained for any personal data used? | None Known | | How often is dataset reviewed? | Before Release | | Is there provenance for all datasets used in training? | Yes | | Does data labeling (annotation, metadata) comply with privacy laws? | Yes | | Applicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy) |44:T8e7,| Field | Response | | :---------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Model Application(s): | Physical AI common sense understanding and embodied reasoning | | Describe the life critical impact (if present). | None Known | | Use Case Restrictions: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). Additional Information: [Apache License 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md). | | Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog. |45:T541, Okay, let's see. The video has two robot arms. Robot Arm 1 is holding a yellow object on the left, not moving. Robot Arm 2 is on the right and has just picked up a white object from the table. The white object was near the air column wrap, which is on the right side next to the cardboard box. The air column wrap is partially unrolled, so maybe it's for packaging. Since Robot Arm 2 has lifted the white object, the next step would likely be moving it towards the cardboard box. The box is open and has plastic-wrapped items inside. The white object might be placed into the box, possibly to be packed with the other items. Since the air column wrap is nearby, maybe they use it to cushion the item. So the next action is probably moving the white object to the box and placing it inside, maybe using the air column wrap for protection. But since the air column wrap is stationary, maybe the robot just places the white object into the box. Alternatively, the robot might position the white object near the air column wrap to wrap it, but since the air column wrap is already there, maybe the next immediate step is placing the white object into the box. So the answer should be that Robot Arm 2 moves the white object towards the cardboard box to place it inside. Robot Arm 2 places the white object into the cardboard box46:T41f,The contents of the message.
Can only be `null` as part of a last request message with role=`assistant` (for "completion mode", i.e. providing the beginning of the assistant response).
To pass images (only with role=`user`):
- When content is a string, images can be passed together with the text with `img` HTML tags with base64 data: `

` . If the size of an image is more than 180KB, it needs to be uploaded to a presigned S3 bucket using NVCF Asset APIs. Once uploaded you can refer to it using the following format: `

` .
- When content is a list of objects, images can be passed with objects with type=`image_url`, and image_url containing the base64 image data: `data:image/{format};base64,{base64encodedimage}`. HTML `img` tags will not be parsed from objects with type=`text`.
- In both cases, images can be PNG, JPG or JPEG.
For `system` and `assistant` roles, the object list format is not supported. 47:T75f,## Step 1. Generate API Key ::generate-api-key ## Step 2. Pull and Run the NIM ```bash $ docker login nvcr.io Username: $oauthtoken Password: ``` Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure. ```bash export NGC_API_KEY= export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" chmod -R a+w "$LOCAL_NIM_CACHE" docker run -it --rm \ --gpus all \ --ipc host \ --shm-size=32GB \ -e NGC_API_KEY \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \ -u $(id -u) \ -p 8000:8000 \ nvcr.io/nim/nvidia/cosmos3-reasoner:latest ``` ## Step 3. Test the NIM You can now make a local API call using this curl command: ```bash curl -X 'POST' \ 'http://127.0.0.1:8000/v1/chat/completions' \ -H 'Accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "model": "nvidia/cosmos3-nano-reasoner", "messages": [ { "role": "system", "content": "You are a helpful assistant." }, { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://assets.ngc.nvidia.com/products/api-catalog/phi-3-5-vision/example1b.jpg" } }, { "type": "text", "text": "What is in this image?" } ] } ], "max_tokens": 256, "stream": false }' ``` For more details on getting started with this NIM, visit the [NVIDIA NIM Docs](https://docs.nvidia.com/nim/vision-language-models/latest/introduction.html). 48:T397e,

# NVIDIA Cosmos Cosmos is NVIDIA’s World Foundation Model Development Platform that provides the tools to either finetune existing models or train new models from scratch. ## Cosmos Model Family Cosmos World Foundation Models (WFM) are a family of highly-performant pre-trained models purpose-built for generating physics-aware videos used for training robots. With Cosmos, developers can simulate a world in which robots function and train them to act and react responsibly in the real world before actual deployment. Cosmos WFMs currently contain four main types of models: NeMo Curator, Cosmos Tokenizer, Cosmos Guardrail, and Cosmos World Foundation Model. NeMo Curator is a video curation pipeline that takes raw video frames, splits them into meaningful segments, and annotates them with semantic tags, object labels, and scene descriptions. The annotated images are then fed into the Cosmos Tokenizer, which produces a sequence of tokens. This step reduces data dimensionality enabling Cosmos World Foundation Model to effectively handle large or complex inputs for training. Cosmos WFM then consumes the curated/annotated video segments and learns the underlying physics and visual dynamics from real world data. When queried, Cosmos WFM outputs new token sequences that are then decoded back into high-resolution and physically realistic synthetic videos. Cosmos WFMs are pretrained on large-scale video datasets to expose them to a broad range of visual experiences, enabling them to serve as generalists. To construct a specialized WFM developers are expected to fine-tune Cosmos WFM using additional data collected from a specific use case. This additional data will help adapt Cosmos WFM to this intended use case, ensuring it can perform optimally under real-world conditions. ## Governing Terms/Terms of Use All Cosmos WFMs are deployed globally and are covered under NVIDIA’s [Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). This license agreement confirms that: * Models are commercially usable. * You are free to create and distribute derivative models. * NVIDIA does not claim ownership of any outputs generated using the models or derivative models. If users 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 contained in the Model, the user’s rights under the NVIDIA Open Model License Agreement will automatically terminate. If users are interested in a custom license, they may contact cosmos-license@nvidia.com. ## Specific Risk Areas and Mitigations WFMs can produce unrealistic outputs, generate unsafe content or may inadvertently amplify societal biases reflected in their training data. Collectively, these risks underscore the need for technical measures to mitigate risk and careful evaluation before leveraging Cosmos WFM in real-world applications. ### **Cosmos Guardrail** For the safe use of our world foundation models, we develop a comprehensive guardrail system. Cosmos Guardrail consists of two stages: the pre-Guard and the post-Guard stage. The pre-Guard stage leverages [Aegis-AI-Content-Safety-LlamaGuard-LLM-Defensive-1.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0), which is a fine-tuned version of [Llama-Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) trained on [NVIDIA’s Aegis Content Safety Dataset](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-1.0) and a blocklist filter that performs a lemmatized and whole-word keyword search to block harmful prompts. It then further sanitizes the user prompt by processing it through the Cosmos Text2World Prompt Upsampler. The post-Guard stage blocks harmful visual outputs using a video content safety classifier and a face blur filter. ![](https://assets.ngc.nvidia.com/products/api-catalog/cosmos/system-card-diagram.jpg) Cosmos pre-Guard first uses a simple blocklist-based checker for unsafe keyword detection. This is designed to block explicitly harmful generations by doing a keyword search on the prompt against a hard-coded blocklist of a large corpus of explicit and objectionable words. Input words are lemmatized using [WordNetLemmatizer](https://www.nltk.org/api/nltk.stem.WordNetLemmatizer.html?highlight=wordnet), a tool that uses a lexical database of the English language to extract the root word from its variants. These lemmatized words are then compared to the words in the hard-coded blocklist, and the entire prompt is rejected if any profanity is found. As the second line of defense, Cosmos pre-Guard uses [Aegis-AI-Content-Safety-LlamaGuard-LLM-Defensive-1.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0) to detect unsafe content in semantically-complex prompts. Aegis is able to classify prompts into13 critical safety risk categories: violence, sexual, criminal planning, weapons, substance abuse, suicide, child sexual abuse material, hatred, harassment, threat, and profanity. If the input prompt is categorized as unsafe by this prompt filter, the video is not generated, and an error message is displayed. Any prompt that does not fall into the above categories is considered safe from the prompt-filtering standpoint. Prior to passing the prompt to the world generation models, the prompt is further augmented and indirectly sanitized via the Cosmos Text2World Prompt Upsampler. This is a bespoke model that not only compensates for the lack of specificity in the prompt but also steers clear of objectionable denotations or connotations. Cosmos post-Guard is a vision-domain guardrail that is activated after the world content has been generated and comprises a video content safety filter and a face blur filter. Our video content safety filter used in the post-Guard stage has been trained on carefully-curated datasets and evaluated on human\- annotated datasets created by Cosmos Red Team. To calibrate model outputs for the intended use case in the robotics and autonomous vehicle domains, we also automatically detect and blur all faces. We use RetinaFace, a state-of-the-art face detection model, to identify facial regions with high confidence scores. For any generated face region larger than 20 × 20 pixels, we apply pixelation to obscure features while preserving the overall scene composition. Note that by blurring all generated human faces in the video, potential biases based on age, gender, race and ethnicity in the output video are reduced. ### **Balanced Datasets** Cosmos WFM is trained using both proprietary and publicly available video datasets. We curated about 100M clips of videos ranging from 2 to 60 seconds from a 20M hour-long video collection. For each clip, we use a VLM ([13B-parameter VILA model](https://huggingface.co/Efficient-Large-Model/VILA-13b)) to provide a video caption per 256 frames. As our goal is to create a VLM that is able to generate physically realistic videos, we use the video captions to curate the training dataset to cover various physical applications: * Driving (11%), * Hand motion and object manipulation (16%), * Human motion and activity (10%), * Spatial awareness and navigation (16%), * First person point-of-view (8%), * Nature dynamics (20%), * Dynamic camera movements (8%), * Synthetically rendered (4%) * Others (7%) To ensure effective distribution of the dataset we employ a taxonomy-based classifier to label video types and prune those that introduce unrealistic behaviors, such as purely animated or abstract patterns. Certain categories relevant to world foundation models (like human actions and interactions) are upsampled, while less critical ones (such as landscapes) are downsampled. A significant amount of the initial video data is either semantically redundant or contains different visual effects, which may induce unwanted artifacts in the generated videos if not appropriately handled. We therefore designed a sequence of data processing steps to find the most valuable parts of the raw videos for training. Shot boundary detection identifies where one shot ends and another begins, after which all footage is re-encoded into a uniform, high-quality MP4 format to ensure consistent loading and reduce codec discrepancies. The resulting video segments undergo several filtering processes. Motion filtering removes clips that are static or excessively shaky, and tags the remaining clips with camera motion types to enhance training signals. Visual quality filtering uses a video assessment model trained on [DOVER](https://github.com/VQAssessment/DOVER) to discard the bottom 15% in perceptual quality and applies an image aesthetic model exclude footage that is aesthetically poor. A deduplication step uses [InternVideo2](https://arxiv.org/abs/2403.15377) embeddings to identify near-duplicate content and preserves the highest-resolution version for minimal quality loss. ## **Evaluation Methods** We employ a dedicated red team to actively probe the system using both standard and adversarial examples that are collected in an internal attack prompt dataset. These video outputs are annotated by a team of expert annotators, who were specially trained for our task, to classify the generated video on a scale of 1-5 on multiple categories of harm related to the safety taxonomy. These annotations also specify the start and end-frames where the unsafe content is detected, thereby generating high-quality annotations. The red team also probed each guardrail component independently with targeted examples to identify weaknesses and improve performance in edge cases. As of the date of publication, the red team has tested and annotated over10, 000 distinct prompt-video pairs that were carefully crafted to cover a broad range of unsafe content. We separate out our safety testing into 4 categories: **Targeted unsafe testing** Targeted unsafe testing involves generating a corpus of manually curated unsafe prompts. These are intended to emulate common unsafe interactions that are performed by non-technical users of the system with basic or limited knowledge of multimodal AI attack vectors. These have a high likelihood of being caught by prompt filters, e.g. “Video of a naked person”. **Adversarial Attack Testing** Adversarial attack testing involves generating a corpus of unsafe prompts following the styles of AI attack published in literature. This type of testing will also leverage some (not all) of the prompts from content safety datasets like Aegis and automation tooling like [Garak](https://github.com/NVIDIA/garak). **Prompt Upsampler Toxicity** Prompt Upsampler Toxicity refers to a phenomenon where automated methods are used to “upsample” or expand a prompt by adding detail or context and inadvertently introduce unsafe content. Monitoring and mitigating Prompt Upsampler Toxicity ensures that content moderation systems remain effective throughout the entire generation pipeline, preserving user trust and upholding ethical standards. **Accidental Mishap Testing** Accidental mishap testing involves emulating the experience of a user prompting the model with a benign prompt, and getting unsafe content in return. This is the hardest category to test, since it does not have a fixed method or protocol for generation. ## Deployment Cosmos WFM is released under an open, permissive NVIDIA license, allowing users to download the model weights and run it on their own hardware. This means that developers can integrate the WFM into their existing workflows without dependency on external APIs. They can also tailor the model to specific domain needs, retrain or fine-tune the model with their private data. This approach fosters innovation especially for under-resourced stakeholders that cannot rely on paid services. Once downloaded, NVIDIA has less visibility into how or where Cosmos is deployed, reducing opportunities to enforce content policies or guardrails. Downloadable models grant complete control to users, but also transfer responsibility to the users for preventing misuse, and implementing safety mechanisms, such as watermarking and content moderation. Watermarking in the context of WFMs is crucial to ensure traceability, and user awareness that generated content might not be authentic. Watermarks allow viewers and downstream users to identify AI-generated or AI-manipulated videos, helping prevent misinformation and misuse. Even though watermarking is typically the responsibility of the user, we still encourage the use of open-source libraries for watermarking by downstream users of Cosmos WFM. NVIDIA has actively promoted watermarking and has worked in consortiums and standards bodies to define common protocols for watermarking synthetic media. Cosmos WFM is also hosted by NVIDIA at the NVIDIA API Catalog (build.nvidia.com) and accessible via a web-based user interface. In this case, NVIDIA manages infrastructure, updates, and safety features. End users with minimal machine learning expertise can harness powerful WFMs without worrying about infrastructure or setup. Hosted models give NVIDIA more oversight and moderation capabilities, for example: * Know Your Customer (KYC) and account verification ensures that users are who they claim to be, discouraging malicious actors and fostering accountability. * Usage monitoring securely records user activity and flags suspicious patterns, enabling traceability and compliance checks while helping identify harmful behavior. * Rate limiting prevents spamming and large-scale misuse, balancing computational resources and protecting against abuse or overwhelming the system. * Human review protocols provide an escalation path for questionable outputs or flagged user accounts. This is a dedicated moderation team for final decisions on content removals, user bans, or investigations. ## Ethical Considerations NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal supporting team to ensure this system meets requirements for the relevant industry and use case and addresses unforeseen product misuse. ## Getting Help/Support Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). 38:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L3b",null,{"alt":"","className":"object-cover object-center !left-1/2 z-10 !w-1/2 brightness-20 lg:brightness-75","fill":true,"priority":true,"src":"https://assets.ngc.nvidia.com/products/api-catalog/images/cosmos3-nano-reasoner.jpg","style":{"maskImage":"linear-gradient(to left, rgba(0,0,0,1), rgba(0,0,0,0))"}}],["$","div",null,{"className":"z-30 mx-auto flex w-full max-w-screen-xl min-w-0 flex-1 flex-col items-start justify-between gap-md lg:flex-row lg:gap-ml","children":["$","section",null,{"aria-describedby":"resource-description","aria-labelledby":"resource-name","className":"flex w-full min-w-0 flex-col lg:flex-1","children":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L3c",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/nvidia","children":"NVIDIA"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"cosmos3-nano-reasoner"}],[["$","span","AVAILABLE",{"className":"nv-badge nv-badge--kind-solid h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Downloadable"]}],["$","span","PREVIEW",{"className":"nv-badge nv-badge--kind-solid nv-badge--color-purple h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Free Endpoint"]}]],"$undefined"]}],["$","p",null,{"className":"max-w-[85ch] text-ms text-primary","id":"resource-description","children":"Vision language model that excels in understanding the physical world using structured reasoning on videos or images."}],["$","div",null,{"className":"flex w-full flex-col items-start gap-md lg:flex-row lg:items-end lg:justify-between","children":[["$","div",null,{"className":"flex w-full min-w-0 flex-wrap gap-sm lg:flex-1","children":["$","$L3d",null,{"artifactType":"ENDPOINT","density":"standard","labels":["Physical AI","autonomous vehicles","industrial","nimType:endpoint:nim_type_preview","nimType:endpoint:nim_type_run_anywhere","reasoning","robotics","smart cities","usecase:endpoint:usecase_sdg","video understanding","vision language model"],"searchRoutePath":"/models"}]}],["$","div",null,{"className":"z-30 flex w-full flex-row justify-start gap-ms sm:justify-end lg:w-auto lg:shrink-0","children":["$undefined",["$","$L3e",null,{"cta":{"text":"Download Now","url":"?modal=fine-tune"}}]]}]]}]]}]}]}],["$","div",null,{"className":"pointer-events-none absolute right-0 bottom-0 left-0 z-20 h-30 bg-gradient-to-t to-transparent from-manitoulinLightBlack via-manitoulinLightBlack/70"}]]}],["$","div",null,{"className":"px-4 mx-auto flex max-w-screen-xl flex-col pt-0 lg:px-0","data-body-background":"light-black","children":["$","section",null,{"className":"flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack sm:rounded-6 lg:rounded-8 p-ms lg:p-lg","children":["$","$L3f",null,{"artifact":{"artifactType":"ENDPOINT","name":"cosmos3-nano-reasoner","displayName":"cosmos3-nano-reasoner","publisher":"nvidia","shortDescription":"Vision language model that excels in understanding the physical world using structured reasoning on videos or images.","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/cosmos3-nano-reasoner.jpg","labels":"$38:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:0:props:children:props:labels","attributes":[{"key":"AVAILABLE","value":"true"},{"key":"PREVIEW","value":"true"},{"key":"last_month_api_invocation_count","value":"2442"},{"key":"last_month_download_count","value":"0"}],"updatedDate":"2026-06-01T04:48:10.668Z","bias":"$40","canGuestDownload":true,"createdDate":"2026-06-01T04:48:10.668Z","description":"$41","explainability":"$42","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy","privacy":"$43","safetyAndSecurity":"$44"},"endpointName":"cosmos3-nano-reasoner","endpointSpec":{"openAPISpec":{"openapi":"3.1.0","info":{"title":"NVIDIA NIM API for nvidia/cosmos-reason2-8b","description":"The NVIDIA NIM REST API. 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If uploading large images, this POST should be used in conjunction with the NVCF API which allows for the upload of large assets. \nYou can find details on how to use NVCF Asset APIs here: https://docs.api.nvidia.com/cloud-functions/reference/createasset","operationId":"invokeFunction","parameters":[{"in":"header","name":"NVCF-INPUT-ASSET-REFERENCES","schema":{"type":"string","maxLength":370,"format":"uuid"},"required":false,"description":"String of asset IDs separated by commas. Data is uploaded to AWS S3 using NVCF Asset APIs and associated with these asset IDs.If the size of an image is more than 180KB, it needs to be uploaded to a presigned S3 URL bucket. The presigned URL allows for secure and temporary access to the S3 bucket for uploading the image. Once the asset is requested, an asset ID is generated for it. Please include this asset ID in this header and to use the uploaded image in a prompt, you need to refer to it using the following format: `

`."}],"requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/NIMLLMChatCompletionRequest"}}},"required":true},"responses":{"200":{"description":"Invocation is fulfilled","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChatCompletionResponse"}},"text/event-stream":{"schema":{"$ref":"#/components/schemas/NIMLLMChatCompletionStreamResponse"}}}},"202":{"description":"Result is pending. 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Specify the 2D trajectory your end effector should follow in pixel space. Return the trajectory coordinates in JSON format like this: {\"point_2d\": [x, y], \"label\": \"gripper trajectory\"}.\n\nAnswer the question using the following format:\n\n\nYour reasoning.\n\n\nWrite your final answer immediately after the tag.","image":"https://assets.ngc.nvidia.com/products/api-catalog/cosmos-reason2/cr2_robot_tape.png"},"output":{"text":"\\nI will move my gripper from its current position at [680, 539] to the blue painter’s tape located at [517, 742] so I can grasp it securely. After picking up the tape, I’ll lift it and carry it over to the gray woven basket positioned at [477, 412]. 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