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Qwen3-VL excels at video understanding with enhanced capabilities for video OCR, long video comprehension, and temporal grounding. The model can process hours-long videos with full recall and second-level indexing.

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Capability Overview

The video understanding feature enables you to:
  • Perform video OCR and text extraction
  • Understand long videos (hours-long content)
  • Achieve second-level temporal indexing
  • Perform video grounding and localization
  • Extract events and actions from videos
  • Handle 256K context natively, expandable to 1M

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Example Usage

from transformers import AutoModelForImageTextToText, AutoProcessor

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": "video",
                "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
            },
            {"type": "text", "text": "Describe this video."},
        ],
    }
]

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

generated_ids = model.generate(**inputs, max_new_tokens=512)
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)

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Try it Yourself

Explore the full video understanding cookbook with interactive examples: Open in Colab View on GitHub