Qwen3.8-Max
Qwen3.8-Max is Qwen’s most capable model to date, built as a Max-class AI model for coding, work, research, long-horizon tasks, and multimodal agents. It scales to 2.4 trillion parameters with 95 billion active parameters and is available through QwenCloud. The model is designed to complete complex, open-ended tasks end to end with greater reliability and minimal human involvement. Qwen3.8-Max supports autonomous coding workflows, agentic development, research reproduction, visual reasoning, document understanding, video analysis, and real-world productivity tasks. It can integrate with popular agent frameworks and coding assistants, including Claude Code, Codex, Qoder CLI, Qwen Code, and OpenClaw. Built for developers, researchers, enterprises, and AI agent builders, Qwen3.8-Max helps teams automate sophisticated work across code, documents, tools, interfaces, and multimodal content.
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HappyHorse 1.1
HappyHorse-1.1-T2V is a text-to-video generation model available through QwenCloud. The model turns text prompts into video output with improved semantic understanding, cinematic shot control, and dynamic motion rendering. HappyHorse-1.1-T2V is designed to capture creative intent more accurately while producing videos with smoother motion, richer details, and stronger visual consistency. It supports natural character actions, scene atmosphere, and physical dynamics for more realistic video generation. The model can be accessed through the QwenCloud API with configurable options such as resolution, aspect ratio, and duration. Built for developers, creators, and AI product teams, HappyHorse-1.1-T2V helps generate high-quality videos from text prompts at scale.
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SmolVLM
SmolVLM-Instruct is a compact, AI-powered multimodal model that combines the capabilities of vision and language processing, designed to handle tasks like image captioning, visual question answering, and multimodal storytelling. It works with both text and image inputs, providing highly efficient results while being optimized for smaller, resource-constrained environments. Built with SmolLM2 as its text decoder and SigLIP as its image encoder, the model offers improved performance for tasks that require integration of both textual and visual information. SmolVLM-Instruct can be fine-tuned for specific applications, offering businesses and developers a versatile tool for creating intelligent, interactive systems that require multimodal inputs.
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Qwen2.5-VL
Qwen2.5-VL is the latest vision-language model from the Qwen series, representing a significant advancement over its predecessor, Qwen2-VL. This model excels in visual understanding, capable of recognizing a wide array of objects, including text, charts, icons, graphics, and layouts within images. It functions as a visual agent, capable of reasoning and dynamically directing tools, enabling applications such as computer and phone usage. Qwen2.5-VL can comprehend videos exceeding one hour in length and can pinpoint relevant segments within them. Additionally, it accurately localizes objects in images by generating bounding boxes or points and provides stable JSON outputs for coordinates and attributes. The model also supports structured outputs for data like scanned invoices, forms, and tables, benefiting sectors such as finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B sizes, Qwen2.5-VL is accessible through platforms like Hugging Face and ModelScope.
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