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Description

QwenCloud is an AI-native cloud platform that gives developers and organizations access to models, tools, apps, APIs, and cloud services out of the box. The platform supports AI agents, human-facing applications, and production AI workflows across text, image, video, audio, speech, and multimodal use cases. QwenCloud features models such as Qwen3.8-Max for advanced reasoning and vision-language tasks, HappyHorse-T2V and Wan-T2V for video generation, Qwen-Image-3.0-Pro for high-detail image generation, and CosyVoice for natural speech synthesis. Developers can use Try AI to experiment with models, get API keys, and follow documentation for building production agents. The platform also highlights Qoder as an agentic coding platform for desktop development, JetBrains workflows, CLI automation, and mobile remote control. QwenCloud offers token plans, free API credits, referral rewards, and access to advanced models for individual and team builders. Enterprise capabilities include isolated VPCs, dedicated infrastructure, global compliance coverage, guaranteed P95 first-packet latency, model evaluation, rapid experimentation, and deployment monitoring. QwenCloud also connects to broader cloud services such as Elastic Compute Service, Object Storage Service, ApsaraDB RDS, and Function Compute. By combining model access, cloud infrastructure, developer tools, agent workflows, multimodal APIs, and enterprise-grade deployment controls, QwenCloud helps teams build and scale AI-native applications.

Description

Qwen3-Omni is a comprehensive multilingual omni-modal foundation model designed to handle text, images, audio, and video, providing real-time streaming responses in both textual and natural spoken formats. Utilizing a unique Thinker-Talker architecture along with a Mixture-of-Experts (MoE) framework, it employs early text-centric pretraining and mixed multimodal training, ensuring high-quality performance across all formats without compromising on text or image fidelity. This model is capable of supporting 119 different text languages, 19 languages for speech input, and 10 languages for speech output. Demonstrating exceptional capabilities, it achieves state-of-the-art performance across 36 benchmarks related to audio and audio-visual tasks, securing open-source SOTA on 32 benchmarks and overall SOTA on 22, thereby rivaling or equaling prominent closed-source models like Gemini-2.5 Pro and GPT-4o. To enhance efficiency and reduce latency in audio and video streaming, the Talker component leverages a multi-codebook strategy to predict discrete speech codecs, effectively replacing more cumbersome diffusion methods. Additionally, this innovative model stands out for its versatility and adaptability across a wide array of applications.