Qwen3.6-27B

Description:

A multimodal dense model containing 27.78B parameters, 64 layers, a context length of 262,144 tokens (extensible to 1,010,000), 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. Qwen3.6-27B was developed by Alibaba Cloud as a part of Qwen. This model is ready for commercial/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 Alibaba Cloud Model Card.

License and Terms of Use:

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

Additional Information: Apache 2.0 License.

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.

Release Date:

NGC: 04/22/2026 via Qwen3.6-27B on NGC HuggingFace: 04/21/2026 via Qwen3.6-27B on HuggingFace

Reference(s):

• Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model
• vLLM PR 34330
• SGLang PR 18467
• HuggingFace model repository for Qwen3.6-27B

Model Architecture:

Architecture Type: Transformer Network Architecture: Qwen3_5ForConditionalGeneration (Dense Transformer with Gated DeltaNet and Gated Attention layers) This model was developed based on Qwen/Qwen3.6-27B. Number of model parameters: 27.78B

Input:

Input Type(s): Text, Image, Video Input Format(s): Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm Input Parameters: One-Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D) Other Properties Related to Input: The model supports up to 262,144 tokens of context length (extensible to 1,010,000) 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 Output Format: String Output Parameters: One-Dimensional (1D) Other Properties Related to Output: The model can preserve thinking traces across messages and supports configurable sampling parameters (temperature, top_p, top_k, etc.). 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): SGLang Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell
• NVIDIA Hopper

Supported Operating System(s): Linux

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.

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality:

• Text
• Image
• Video Image Training Data Size: Undisclosed Text Training Data Size: Undisclosed Video Training Data Size: Undisclosed Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed

Testing Dataset:

Data Collection Method by dataset: Undisclosed Labeling Method by dataset: Undisclosed Properties: Undisclosed

Evaluation Dataset:

Benchmark Results

Language Benchmarks

Vision Benchmarks

Data Collection Method by dataset: [Hybrid: Automated, Human] Labeling Method by dataset: [Hybrid: Automated, Human] Properties (Quantity, Dataset Descriptions, Sensor(s)): The model is evaluated on a broad suite of benchmarks covering agent capabilities, vision-language tasks, general VQA, STEM and reasoning, knowledge, and spatial intelligence. These benchmarks include both human-curated problems (e.g., AIME, HMMT, HumanEval) and automatically generated or web-crawled datasets (e.g., Terminal-Bench, SkillsBench).

Inference:

Acceleration Engine: SGLang Test Hardware:

• NVIDIA B200
• NVIDIA H100
• NVIDIA H200

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 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.

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

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