Qwen3-Omni-Thinking

Overview

Introduction

Qwen3-Omni is the natively end-to-end multilingual omni-modal foundation models. It processes text, images, audio, and video, and delivers real-time streaming responses in both text and natural speech. We introduce several architectural upgrades to improve performance and efficiency. Key features:

• State-of-the-art across modalities: Early text-first pretraining and mixed multimodal training provide native multimodal support. While achieving strong audio and audio-video results, unimodal text and image performance does not regress. Reaches SOTA on 22 of 36 audio/video benchmarks and open-source SOTA on 32 of 36; ASR, audio understanding, and voice conversation performance is comparable to Gemini 2.5 Pro.

• Multilingual: Supports 119 text languages, 19 speech input languages, and 10 speech output languages.

  • Speech Input: English, Chinese, Korean, Japanese, German, Russian, Italian, French, Spanish, Portuguese, Malay, Dutch, Indonesian, Turkish, Vietnamese, Cantonese, Arabic, Urdu.
  • Speech Output: English, Chinese, French, German, Russian, Italian, Spanish, Portuguese, Japanese, Korean.

• Novel Architecture: MoE-based Thinker–Talker design with AuT pretraining for strong general representations, plus a multi-codebook design that drives latency to a minimum.

• Real-time Audio/Video Interaction: Low-latency streaming with natural turn-taking and immediate text or speech responses.

• Flexible Control: Customize behavior via system prompts for fine-grained control and easy adaptation.

• Detailed Audio Captioner: Qwen3-Omni-30B-A3B-Captioner is now open source: a general-purpose, highly detailed, low-hallucination audio captioning model that fills a critical gap in the open-source community.

Model Architecture

Cookbooks for Usage Cases

Qwen3-Omni supports a wide range of multimodal application scenarios, covering various domain tasks involving audio, image, video, and audio-visual modalities. Below are several cookbooks demonstrating the usage cases of Qwen3-Omni and these cookbooks include our actual execution logs. You can first follow the QuickStart guide to download the model and install the necessary inference environment dependencies, then run and experiment locally—try modifying prompts or switching model types, and enjoy exploring the capabilities of Qwen3-Omni!

QuickStart

Model Description and Download

Below is the description of all Qwen3-Omni models. Please select and download the model that fits your needs.

During loading in Hugging Face Transformers or vLLM, model weights will be automatically downloaded based on the model name. However, if your runtime environment is not conducive to downloading weights during execution, you can refer to the following commands to manually download the model weights to a local directory:

# Download through ModelScope (recommended for users in Mainland China)
pip install -U modelscope
modelscope download --model Qwen/Qwen3-Omni-30B-A3B-Instruct --local_dir ./Qwen3-Omni-30B-A3B-Instruct
modelscope download --model Qwen/Qwen3-Omni-30B-A3B-Thinking --local_dir ./Qwen3-Omni-30B-A3B-Thinking
modelscope download --model Qwen/Qwen3-Omni-30B-A3B-Captioner --local_dir ./Qwen3-Omni-30B-A3B-Captioner

# Download through Hugging Face
pip install -U "huggingface_hub[cli]"
huggingface-cli download Qwen/Qwen3-Omni-30B-A3B-Instruct --local-dir ./Qwen3-Omni-30B-A3B-Instruct
huggingface-cli download Qwen/Qwen3-Omni-30B-A3B-Thinking --local-dir ./Qwen3-Omni-30B-A3B-Thinking
huggingface-cli download Qwen/Qwen3-Omni-30B-A3B-Captioner --local-dir ./Qwen3-Omni-30B-A3B-Captioner

Transformers Usage

Installation

The Hugging Face Transformers code for Qwen3-Omni has been successfully merged, but the PyPI package has not yet been released. Therefore, you need to install it from source using the following command. We strongly recommend that you create a new Python environment to avoid environment runtime issues.

# If you already have transformers installed, please uninstall it first, or create a new Python environment
# pip uninstall transformers
pip install git+https://github.com/huggingface/transformers
pip install accelerate

We offer a toolkit to help you handle various types of audio and visual input more conveniently, providing an API-like experience. This includes support for base64, URLs, and interleaved audio, images, and videos. You can install it using the following command and make sure your system has ffmpeg installed:

pip install qwen-omni-utils -U

Additionally, we recommend using FlashAttention 2 when running with Hugging Face Transformers to reduce GPU memory usage. However, if you are primarily using vLLM for inference, th