Qwen Image 2.1 Turbo

Qwen-Image-2.1-Pro is a unified text-to-image generation and image-editing model in the Tongyi Qianwen (Qwen) series. Its visual generation component contains only 7 billion parameters (a 32-layer single-stream DiT), achieving a good balance between generation quality, inference efficiency, and versatility. This release includes four key improvements: - Compact and efficient: adopts a lightweight architecture that combines hybrid-granularity attention and prefix KV cache reuse to deliver high-quality image generation with low computational cost. - Native transparency support, unified generation and editing: can generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photos—all handled by a single model. - Versatile editing: supports up to 10 reference images, allows specifying local edit regions via circular or freehand annotations or separate masks, and preserves identity features of people and products. - Photorealistic textures and refined aesthetics: improved typography, portrait lighting, and detail rendering for more visually appealing results.

Qwen3.8 Omni Flash is Alibaba Cloud Qwen's next-generation native multimodal model, supporting 1M-length sequences and able to directly accept text, images, audio, and video as input. It is based on the Qwen3.8-Flash-Next architecture. The model is designed for agent capabilities in real productivity scenarios: while offering agentic abilities such as programming, text knowledge work, and GUI operation, it also achieves significant results in audio/video-centered agentic applications—such as video editing, music video creation, film production and narration, audio/video-to-text-and-image summarization, and audio/video dialogue—that require integrated processing of text, images, audio, and video. It supports 2-channel and 4-channel spatial audio parsing, is compatible with DashScope and OpenAI protocols, and it is recommended to install the accompanying Qwen-MM-Plugins to facilitate agent frameworks' access to native multimodal capabilities.

Alibaba Cloud has launched the decision model decision-model-preview. This structured decision model is designed for high-frequency business judgments; it can concurrently perform classification, binary decisions, and scoring based on text or business state, and returns probability distributions and confidence levels. It is suitable for scenarios such as ticket routing, content moderation, agent routing, and result verification. Free for a limited time.

Qwen3.8-27b is an Alibaba-released dense vision-language model with open-source weights. Qwen3.8 was developed based on the architecture of Qwen3.5, achieving significant improvements in encoding capability, handling professional tasks, research tasks, and tasks that require long-term completion. Qwen3.8-27B integrates these advantages into a compact, easy-to-deploy dense model — a native vision-language model capable of understanding image and video information and featuring flexible cognitive control capabilities. This model can more reliably accomplish complex, multi-step tasks.

Qwen3.8-Max-0902 (also known as qwen3.8-max-2026-09-02) is a snapshot version of Alibaba Cloud Tongyi Qianwen's qwen3.8-max. It pushes encoding depth further, enabling it to handle more complex engineering-level projects and long-term autonomous development; collaborative agent capabilities are significantly enhanced, performing more confidently in multi-tool orchestration and end-to-end delivery; visual understanding is comprehensively improved, with more sensitive and accurate chart reasoning, document parsing, and multimodal perception. Continuing the 1-million-context window, reasoning modes, and a complete tool ecosystem, it continues to evolve at a higher level of intelligence.

Qwen3.8 Flash is Alibaba Cloud Qwen’s flagship native vision-language model for coding, office tasks, long-context reasoning, and agent workflows. It supports a 1M-token context, 128K output, web access, and tool calling. Compared with Qwen3.7-Plus, Qwen3.8-Flash significantly reduces training and inference costs—the training overhead is only about one-ninth of the former—while offering stronger capabilities on coding and office tasks.