Qwen3-VL: A Powerful Multimodal Large Language Model Series

Introduction

Qwen3-VL is the multimodal large language model series developed by the Qwen team at Alibaba Cloud. This latest generation represents the most powerful vision-language model in the Qwen series to date, delivering comprehensive upgrades across the board. It features superior text understanding and generation, deeper visual perception and reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures, with Instruct and reasoning-enhanced Thinking editions, Qwen3-VL offers flexible, on-demand deployment from edge to cloud.

Key enhancements include:

• Visual Agent: Operates PC/mobile GUIs, recognizing elements, understanding functions, invoking tools, and completing tasks.
• Visual Coding Boost: Generates Draw.io/HTML/CSS/JS from images/videos.
• Advanced Spatial Perception: Judges object positions, viewpoints, and occlusions, providing stronger 2D grounding and enabling 3D grounding for spatial reasoning and embodied AI.
• Long Context & Video Understanding: Native 256K context, expandable to 1M, handling books and hours-long video with full recall and second-level indexing.
• Enhanced Multimodal Reasoning: Excels in STEM/Math, performing causal analysis and providing logical, evidence-based answers.
• Upgraded Visual Recognition: Broader, higher-quality pretraining enables it to “recognize everything,” including celebrities, anime, products, landmarks, flora/fauna, etc.
• Expanded OCR: Supports 32 languages (up from 10), robust in low light, blur, and tilt, with better handling of rare/ancient characters and jargon, and improved long-document structure parsing.
• Text Understanding on par with pure LLMs: Seamless text-vision fusion for lossless, unified comprehension.

The model also features architectural updates such as Interleaved-MRoPE for enhanced long-horizon video reasoning, DeepStack for fusing multi-level ViT features, and Text-Timestamp Alignment for precise, timestamp-grounded event localization.

Installation

To get started with Qwen3-VL, ensure you have the necessary dependencies. The Qwen3-VL model requires transformers version 4.57.0 or higher.

pip install "transformers>=4.57.0"
pip install qwen-vl-utils==0.0.14

For users in mainland China, ModelScope is strongly advised for downloading checkpoints, as snapshot_download can help resolve download issues.

Examples

Here is a quick example demonstrating how to use Qwen3-VL for chat with the transformers library:

from transformers import AutoModelForImageTextToText, AutoProcessor

# Load the model on the available device(s)
model = AutoModelForImageTextToText.from_pretrained(
    "Qwen/Qwen3-VL-235B-A22B-Instruct", dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-235B-A22B-Instruct")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Qwen3-VL also supports multi-image and video inference, allowing for complex multimodal interactions.

Why Use Qwen3-VL

Qwen3-VL stands out as a leading multimodal large language model due to its extensive capabilities and robust architecture. It offers unparalleled performance in understanding and generating both text and visual content, making it suitable for a wide range of applications from visual agents to advanced multimodal reasoning. Its ability to handle long contexts and videos, coupled with enhanced spatial perception and broad object recognition, provides developers with a powerful tool for building intelligent systems. The availability of various model sizes and editions, along with comprehensive cookbooks and deployment options, ensures flexibility and ease of integration for diverse use cases.

Links

• GitHub Repository: https://github.com/QwenLM/Qwen3-VL
• Qwen Chat: https://chat.qwenlm.ai/
• Hugging Face: https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe
• ModelScope: https://modelscope.cn/collections/Qwen3-VL-5c7a94c8cb144b
• Paper: https://arxiv.org/pdf/2511.21631
• Demo: https://huggingface.co/spaces/Qwen/Qwen3-VL-Demo
• Cookbooks: https://github.com/QwenLM/Qwen3-VL/tree/main/cookbooks
• API Documentation: https://help.aliyun.com/zh/model-studio/developer-reference/qwen-vl-api