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Use of this model is governed by theNVIDIA Open Model License Agreement. Additional Information:Apache 2.0 license.

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Qwen3.6-35B-A3B

Description

A multimodal mixture-of-experts (MoE) model containing 35B total parameters (3B activated), 40 layers, and a context length of 262k tokens (extendable to 1.01M via YaRN), designed for agentic coding, multimodal reasoning, and long-context understanding across text, image, and video inputs. Key capabilities include thinking preservation across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines.\nQwen3.6-35B-A3B was developed by the Qwen team.\nThis model is ready for commercial or non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Qwen Model Card.

License and Terms of Use

GOVERNING TERMS: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: Apache 2.0.

Deployment Geography

Global

Use Case

Developers building AI agents, coding assistants, and multimodal applications that process text, images, and video, requiring advanced reasoning, agentic coding, and long-context understanding.

Get Help

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

Release Date

NGC 07/15/2026 via Qwen3.6-35B-A3B on NGC

Reference(s)

Model Architecture

Architecture Type: Transformer\nNetwork Architecture: Mixture of Experts (MoE) with 256 experts (8 routed + 1 shared per token)\nThis model was developed based on Qwen/Qwen3.6-35B-A3B.\nNumber of model parameters: 35B

Input

Input Type(s): Text, Image, Video\nInput Format(s): Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\nInput Parameters: One-Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\nOther Properties Related to Input: The model supports up to 262k tokens of context length (extensible to 1.01M via YaRN) and can operate in thinking or non-thinking modes via API flags. It accepts multimodal inputs (text, image, video) through the OpenAI-compatible chat endpoint.

Output

Output Type(s): Text\nOutput Format: String\nOutput Parameters: One-Dimensional (1D)\nOther Properties Related to Output: The model can preserve thinking traces across messages and supports configurable sampling parameters. Output length can be set up to 81,920 tokens.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): vLLM\nSupported Hardware Microarchitecture Compatibility:

NVIDIA DGX Spark

Precision: FP8, NVFP4\nSupported Operating System(s): Ubuntu 22.04, Ubuntu 24.04

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. Following the V-model methodology, 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.

This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s)

Qwen3.6-35B-A3B v3.6

Training, Testing, and Evaluation Datasets

Training Dataset

Data Modality:

Text
Image
Video\nImage Training Data Size: Undisclosed\nText Training Data Size: Undisclosed\nVideo Training Data Size: Undisclosed\nData Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Testing Dataset

Data Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Evaluation Dataset

Benchmark Results

Language Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Qwen3.5-27B	Gemma4-31B	Qwen3.5-35BA3B	Gemma4-26BA4B	Qwen3.6-35BA3B
SWE-bench Verified	75.0	52.0	70.0	17.4	73.4
SWE-bench Multilingual	69.3	51.7	60.3	17.3	67.2
SWE-bench Pro	51.2	35.7	44.6	13.8	49.5
Terminal-Bench 2.0	41.6	42.9	40.5	34.2	51.5
MMLU-Pro	86.1	85.2	85.3	82.6	85.2
GPQA Diamond	85.5	84.3	84.2	82.3	86.0
HLE	24.3	19.5	22.4	8.7	21.4
LiveCodeBench v6	80.7	80.0	74.6	77.1	80.4
AIME 2026	92.6	89.2	91.0	88.3	92.7
C-Eval	90.5	82.6	90.2	82.5	90.0

Vision Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Benchmark	Qwen3.5-27B	Claude-Sonnet-4.5	Gemma4-31B	Gemma4-26BA4B	Qwen3.5-35BA3B	Qwen3.6-35BA3B
MMMU	82.3	79.6	80.4	78.4	81.4	81.7
MMMU-Pro	75.0	68.4	76.9*	73.8*	75.1	75.3
Mathvista (mini)	87.8	79.8	79.3	79.4	86.2	86.4
RealWorldQA	83.7	70.3	72.3	72.2	84.1	85.3
MMBench EN-DEV-v1.1	92.6	88.3	90.9	89.0	91.5	92.8
OmniDocBench1.5	88.9	85.8	80.1	74.4	89.3	89.9
VideoMMMU	82.3	77.6	81.6	76.0	80.4	83.7
VideoMME (w sub.)	87.0	81.1	--	--	86.6	86.6
VideoMME (w/o sub.)	82.8	75.3	--	--	82.5	82.5
RefCOCO (avg)	90.9	--	--	--	89.2	92.0
ODInW13	41.1	--	--	--	42.6	50.8

Data Collection Method by dataset: [Hybrid: Automated, Manually-Collected]\nLabeling Method by dataset: [Hybrid: Automated, Manually-Labeled]\nProperties: The model is evaluated on benchmarks covering agent capabilities, vision-language tasks, general visual question answering, STEM and reasoning, knowledge, and spatial intelligence.

Inference

Acceleration Engine: vLLM\nPrecision: FP8, NVFP4\nTest Hardware:

NVIDIA DGX Spark

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 developer team to ensure these software components meet requirements for the relevant industry and use case and address 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.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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Key capabilities include thinking preservation across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines.\nQwen3.6-35B-A3B was developed by the Qwen team.\nThis model is ready for commercial or non-commercial use.\n\n## Third-Party Community Consideration\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Qwen Model Card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).\n\n## License and Terms of Use\n**GOVERNING TERMS:** Use of this container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of the model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). **ADDITIONAL INFORMATION:** [Apache 2.0](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE).\n\n## Deployment Geography\nGlobal\n\n## Use Case\nDevelopers building AI agents, coding assistants, and multimodal applications that process text, images, and video, requiring advanced reasoning, agentic coding, and long-context understanding.\n\n## Get Help\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n## Release Date\n**NGC** 07/15/2026 via [Qwen3.6-35B-A3B on NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/models/qwen3.6-35b-a3b)\n\n## Reference(s)\n* [Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All](https://qwen.ai/blog?id=qwen3.6-35b-a3b)\n* [vLLM PR 34330](https://github.com/vllm-project/vllm/pull/34330)\n* [Qwen3.6-35B-A3B Model Card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)\n\n## Model Architecture\n**Architecture Type:** Transformer\n**Network Architecture:** Mixture of Experts (MoE) with 256 experts (8 routed + 1 shared per token)\n**This model was developed based on Qwen/Qwen3.6-35B-A3B.**\n**Number of model parameters:** 35B\n\n### Input\n**Input Type(s):** Text, Image, Video\n**Input Format(s):** Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\n**Input Parameters:** One-Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\n**Other Properties Related to Input:** The model supports up to 262k tokens of context length (extensible to 1.01M via YaRN) and can operate in thinking or non-thinking modes via API flags. It accepts multimodal inputs (text, image, video) through the OpenAI-compatible chat endpoint.\n\n### Output\n**Output Type(s):** Text\n**Output Format:** String\n**Output Parameters:** One-Dimensional (1D)\n**Other Properties Related to Output:** The model can preserve thinking traces across messages and supports configurable sampling parameters. Output length can be set up to 81,920 tokens.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration\n**Runtime Engine(s):** vLLM\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA DGX Spark\n\n**Precision:** FP8, NVFP4\n**Supported Operating System(s):** Ubuntu 22.04, Ubuntu 24.04\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, 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.\n\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.\n\n## Model Version(s)\nQwen3.6-35B-A3B v3.6\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n\n**Data Modality:**\n* Text\n* Image\n* Video\n**Image Training Data Size:** Undisclosed\n**Text Training Data Size:** Undisclosed\n**Video Training Data Size:** Undisclosed\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Testing Dataset\n\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Evaluation Dataset\n\n**Benchmark Results**\n\n**Language Benchmarks**\n| Benchmark | Qwen3.5-27B | Gemma4-31B | Qwen3.5-35BA3B | Gemma4-26BA4B | Qwen3.6-35BA3B |\n|---|---|---|---|---|---|\n| SWE-bench Verified | 75.0 | 52.0 | 70.0 | 17.4 | 73.4 |\n| SWE-bench Multilingual | 69.3 | 51.7 | 60.3 | 17.3 | 67.2 |\n| SWE-bench Pro | 51.2 | 35.7 | 44.6 | 13.8 | 49.5 |\n| Terminal-Bench 2.0 | 41.6 | 42.9 | 40.5 | 34.2 | 51.5 |\n| MMLU-Pro | 86.1 | 85.2 | 85.3 | 82.6 | 85.2 |\n| GPQA Diamond | 85.5 | 84.3 | 84.2 | 82.3 | 86.0 |\n| HLE | 24.3 | 19.5 | 22.4 | 8.7 | 21.4 |\n| LiveCodeBench v6 | 80.7 | 80.0 | 74.6 | 77.1 | 80.4 |\n| AIME 2026 | 92.6 | 89.2 | 91.0 | 88.3 | 92.7 |\n| C-Eval | 90.5 | 82.6 | 90.2 | 82.5 | 90.0 |\n\n**Vision Benchmarks**\n| Benchmark | Qwen3.5-27B | Claude-Sonnet-4.5 | Gemma4-31B | Gemma4-26BA4B | Qwen3.5-35BA3B | Qwen3.6-35BA3B |\n|---|---|---|---|---|---|---|\n| MMMU | 82.3 | 79.6 | 80.4 | 78.4 | 81.4 | 81.7 |\n| MMMU-Pro | 75.0 | 68.4 | 76.9* | 73.8* | 75.1 | 75.3 |\n| Mathvista (mini) | 87.8 | 79.8 | 79.3 | 79.4 | 86.2 | 86.4 |\n| RealWorldQA | 83.7 | 70.3 | 72.3 | 72.2 | 84.1 | 85.3 |\n| MMBench EN-DEV-v1.1 | 92.6 | 88.3 | 90.9 | 89.0 | 91.5 | 92.8 |\n| OmniDocBench1.5 | 88.9 | 85.8 | 80.1 | 74.4 | 89.3 | 89.9 |\n| VideoMMMU | 82.3 | 77.6 | 81.6 | 76.0 | 80.4 | 83.7 |\n| VideoMME (w sub.) | 87.0 | 81.1 | -- | -- | 86.6 | 86.6 |\n| VideoMME (w/o sub.) | 82.8 | 75.3 | -- | -- | 82.5 | 82.5 |\n| RefCOCO (avg) | 90.9 | -- | -- | -- | 89.2 | 92.0 |\n| ODInW13 | 41.1 | -- | -- | -- | 42.6 | 50.8 |\n\n**Data Collection Method by dataset:** [Hybrid: Automated, Manually-Collected]\n**Labeling Method by dataset:** [Hybrid: Automated, Manually-Labeled]\n**Properties:** The model is evaluated on benchmarks covering agent capabilities, vision-language tasks, general visual question answering, STEM and reasoning, knowledge, and spatial intelligence.\n\n## Inference\n**Acceleration Engine:** vLLM\n**Precision:** FP8, NVFP4\n**Test Hardware:**\n* NVIDIA DGX Spark\n\n## Ethical Considerations\nNVIDIA 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 developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nPlease 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.\n\nUsers 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.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Qwen3.6-35B-A3B",["D",1785450301123],"publisher","Qwen","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/qwen/models/qwen3.6-35b-a3b","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",23690102536,"licenseTerms",{"_265":266,"_267":16,"_268":269,"_270":20,"_271":272},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[259],"displayLabels",[250,251],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_217":257,"_253":257,"_254":258,"_72":76},{"_217":252,"_253":252,"_254":255,"_72":76},"NSPECT-4L88-HB6N","value","filter",{"_256":76,"_253":252},"field","False",{"_256":76,"_253":257},{"_217":260,"_253":261,"_254":262,"_72":263},"NVIDIA NIM","nimmcro_nvidia_nim",{"_256":264,"_253":261},"feature","nvidia_nim","governingTerms","

Use of this model is governed by theNVIDIA Open Model License Agreement. Additional Information:Apache 2.0 license.

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These cookies may store a unique ID so that our system will remember you when you return. 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