Qwen3.5-Omni - Alibaba Tongyi's Omni-modal Large Model

What is Qwen3.5-Omni

Qwen3.5-Omni is an omni-modal large model launched by Alibaba Tongyi Lab, capable of simultaneously understanding text, images, audio, and audio-video inputs. The model adopts a Thinker-Talker division-of-labor architecture and Hybrid-MoE technology, achieving SOTA results in 215 audio/audio-video tasks, surpassingGemini-3.1 Pro. The model supports a 256K ultra-long context, semantic interruption, voice cloning, and voice control, natively integrates WebSearch and Function Call, and possesses naturally emergent Audio-Visual Vibe Coding capabilities, able to directly generate runnable code based on audio-video instructions.

Main Features of Qwen3.5-Omni

• Omni-modal understanding: The model natively and seamlessly processes text, images, audio, and audio-video inputs, and supports timestamped fine-grained description generation.
• Intelligent video analysis: The model can generate structured video notes, identifying visual content, dialogue, shot transitions, and sensitive information.
• Vibe Coding: Without specialized training, code generation capabilities naturally emerge based on audio-video instructions.
• Human-level conversation: Supports semantic interruption and voice control, can distinguish environmental noise from genuine interjections, and adjusts emotion and speech rate in real time.
• Voice cloning: Upload a recording to customize an exclusive AI voice, supporting natural generation in multiple languages.
• Intelligent task execution: Natively integrates WebSearch and Function Call, autonomously judging and invoking tools to complete complex tasks.

Technical Principles of Qwen3.5-Omni

• Thinker-Talker division-of-labor architecture: Thinker is responsible for multimodal understanding, receiving visual and audio signals and encoding positional information via TMRoPE; Talker is responsible for speech generation, using RVQ encoding based on Thinker's output to achieve efficient speech synthesis, with the two collaborating to separate understanding and generation.
• Hybrid-Attention MoE：Tasks such as listening, seeing, and understanding are assigned to different expert networks, avoiding interference between modalities, and achieving 215 SOTA performances while maintaining text and visual capabilities without degradation.
• ARIA dynamic alignment technology: The model adaptively adjusts the rate of text and speech units, solving the problems of missing characters and unclear pronunciation of numbers caused by traditional fixed ratios, and supporting real-time voice control responses.

How to Use Qwen3.5-Omni

• API call: VisitAlibaba Cloud Model Studioand search for Qwen3.5-Omni on the official website to call the API, which offers three sizes—Plus, Flash, and Light—to meet the performance and cost needs of different scenarios.
• Online experience: Directly on Qwen Chat experience all the capabilities of Qwen3.5-Omni, with no deployment required for a quick start.

Key Information and Usage Requirements of Qwen3.5-Omni

• Publisher: Alibaba Tongyi Lab
• Model positioning: Omni-modal large model (text/image/audio/audio-video)
• Version specifications: Offers three sizes: Plus, Flash, and Light
• Performance results: 215 SOTA items, comprehensively surpassing Gemini-3.1 Pro
• Context length: 256K (supports 10 hours of audio / 1 hour of video)
• Language support: 74 speech recognition languages + 39 dialects
• Core architecture: Thinker-Talker division of labor + Hybrid-MoE

Core Advantages of Qwen3.5-Omni

• Native Full-Modality Unification: Truly seamless understanding of text, images, audio, and audio-video.
• Top-Tier Performance: 215 SOTA benchmarks topped, with audio/audio-video capabilities comprehensively surpassing Gemini-3.1 Pro.
• Ultra-Long Context: 256K context length, supporting 10 hours of audio or 1 hour of video processing.
• Natural Interaction: Supports semantic interruption, voice control, and voice cloning, delivering a conversation experience close to that of a real person.
• Emergent Capabilities: Possesses Audio-Visual Vibe Coding without specialized training, able to generate code based on audio and video.
• Intelligent Execution: Native support for WebSearch and Function Call, seamlessly connecting chat to task execution.
• Multilingual Coverage: 74 languages of speech recognition + 39 dialects, breaking down language barriers.

Comparison of Qwen3.5-Omni with Similar Competitors

Qwen3.5-Omni Application Scenarios

• Video Creation and Editing: Automatically generates structured descriptions with timestamps, recognizes scenes, dialogue, and shot transitions, detects sensitive content, and turns long videos into searchable notes.
• Smart Meeting Assistant: Transcribes meeting content in real time, distinguishes speakers, generates meeting minutes, and supports multilingual recognition and translation.
• Code-Assisted Development: Directly generates front-end pages or Python code based on design mockups or spoken requirements, enabling Vibe Coding.
• Personalized Voice Assistant: Clones a专属 voice to create a digital avatar, supports voice control of volume and emotion, and provides companion-style interaction.
• Multilingual Real-Time Communication: The model supports 74 languages of recognition + 39 dialects, enabling cross-language real-time conversation and translation.
• Intelligent Task Execution: Combine WebSearch with tool invocation to complete complex tasks such as checking the weather, booking hotels, and searching for information.