Alibaba Cloud Model Studio Integrations

Qwen3.8-Max is a large-scale AI model from Qwen built for coding, coworking, research, long-horizon planning, and multimodal agent workflows. It is positioned as the most capable model in the Qwen family to date, with open weights announced for release after launch. The model uses a 2.4 trillion-parameter architecture with 95 billion active parameters and is available through QwenCloud. Qwen3.8-Max is designed to complete complex, open-ended goals end to end rather than only answer isolated prompts. In coding workflows, it can write and run code, create self-evolving harnesses, normalize requirements into issues, execute tasks through agents, run tests, trigger CI checks, and iterate through feedback. Its autonomous coding examples include a 10+ day project run, a research-paper reproduction and improvement loop, and a 24-hour online competition solution that beat most participating human teams. For professional work, Qwen3.8-Max is built to handle multi-step, tool-heavy workflows across compliance, design, food operations, engineering, rehabilitation, sports analytics, and quantitative research. The model also supports long-horizon decision-making, including autonomous chip-design optimization and extended e-commerce operations simulations. Its multimodal capabilities cover images, complex PDFs, long videos, visual production, interface inspection, frontend reconstruction, Blender visualization, interactive applications, and visual feedback loops. Qwen3.8-Max can be integrated through QwenCloud APIs and used with agent frameworks or coding assistants such as Claude Code, Codex, Qoder CLI, Qwen Code, and OpenClaw. By combining agentic coding, reasoning controls, multimodal understanding, visual self-correction, long-context workflows, tool use, and open-weight availability, Qwen3.8-Max helps developers and organizations build autonomous AI systems that can produce dependable deliverables.

HappyHorse-1.1-T2V is a text-to-video model on QwenCloud built to generate high-quality videos from natural language prompts. The model supports video generation workflows where the input is text and the output is video. HappyHorse-1.1-T2V is designed with improved semantic understanding so it can more accurately interpret creative instructions. It also supports cinematic shot control, helping users guide the style, composition, and feel of generated scenes. Dynamic motion rendering helps the model produce smoother movement and more natural video sequences. The model is positioned to create richer details, stronger visual consistency, natural character actions, convincing scene atmosphere, and realistic physical dynamics. Developers can access HappyHorse-1.1-T2V through the QwenCloud API using the DashScope video synthesis endpoint. API requests can specify parameters such as resolution, aspect ratio, duration, and the text prompt. The model supports 480P, 720P, and 1080P video generation with per-second pricing, along with rate limits for requests, concurrency, and async queue tasks. HappyHorse-1.1-T2V is a hosted model rather than an open source model, and it can be tested through QwenCloud’s Try AI experience or integrated with an API key. By combining prompt-based video creation, cinematic control, motion quality, visual consistency, scalable API access, and configurable output settings, HappyHorse-1.1-T2V helps creators and developers turn ideas into generated video.

OpenAI is committed to ensuring that artificial general intelligence (AGI)—characterized by its ability to perform most tasks that are economically important with a level of autonomy that surpasses human capabilities—benefits all of humanity. Our primary goal is to create AGI that is both safe and beneficial; however, we also view our mission as a success if we empower others to reach this same objective. You can take advantage of our API for numerous language-based functions, such as semantic search, summarization, sentiment analysis, content generation, translation, and much more, all achievable with just a few examples or a clear instruction in English. A simple integration gives you access to our ever-evolving AI technology, enabling you to test the API's features through these sample completions and uncover a wide array of potential uses. As you explore, you may find innovative ways to harness this technology for your projects or business needs.

Qwen3.6-27B stands as an open-source, dense multimodal language model within the Qwen3.6 lineup, crafted to deliver exceptional capabilities in coding, reasoning, and workflows driven by agents, all while utilizing a streamlined parameter count of 27 billion. This model is distinguished by its performance, often surpassing or closely rivaling larger models on critical benchmarks, especially in tasks that involve agent-based coding. It operates in two distinct modes—thinking and non-thinking—allowing it to adjust the depth of its reasoning and the speed of its responses to align with the specific demands of various tasks. Furthermore, it accommodates a broad range of input formats, which includes text, images, and video, demonstrating its adaptability. As an integral part of the Qwen3.6 series, this model emphasizes practical functionality, reliability, and the boost of developer efficiency, drawing on feedback from the community and the practical needs of real-world applications. Its forward-thinking design not only addresses current user requirements but also foresees future developments in the realm of artificial intelligence, ensuring that it remains relevant and effective over time. Thus, Qwen3.6-27B represents a significant step forward in the evolution of language models, integrating innovative features that enhance user interaction and streamline workflows.

Qwen is an advanced AI assistant and development platform powered by Alibaba Cloud’s cutting-edge Qwen model family, offering powerful multimodal reasoning and creativity tools for users at all skill levels. It provides a free and accessible interface through Qwen Chat, where anyone can generate images, analyze content, perform deep multi-step research, and build fully coded web pages simply by describing what they want. Using its VLo model, Qwen transforms ideas into detailed visuals and supports editing, style transfer, and complex multi-element image creation. Deep Research acts like an automated research partner, gathering information online, synthesizing insights, and generating structured reports in minutes. The Web Dev feature empowers users to create modern, ready-to-deploy websites with clean code using only natural language instructions. Qwen’s enhanced “Thinking” capabilities provide stronger logic, structured problem-solving, and real-time internet-aware analysis. Its Search tool retrieves precise results with contextual understanding, while multimodal intelligence enables Qwen to process images, audio, video, and text together for deeper comprehension. For developers, the Qwen API offers OpenAI-compatible endpoints, allowing seamless integration of Qwen’s reasoning, generation, and multimodal abilities into any application or product. This makes Qwen not only an AI assistant but also a versatile platform for builders and engineers. Across web, desktop, and mobile environments, Qwen delivers a unified, high-performance AI experience.

Qwen3.8-27B is an open-weights 27B-class model connected to Alibaba’s Qwen3.8 release, built for developers, researchers, and AI teams that need a capable but more deployable model size. Alibaba’s Qwen3.8 launch described the broader model family as optimized for coding and cowork scenarios, including software development, document processing, data analysis, and professional workflows. Reports state that Alibaba planned to open-source Qwen3.8-Max alongside Qwen3.8-27B, expanding access for developers and researchers. Qwen3.8-27B gives builders a smaller alternative to the 2.4T-parameter Qwen3.8-Max model, which third-party coverage describes as Qwen’s first Max-scale model planned for open weights. The model is well suited for coding assistance, local development, agent testing, workflow automation, data analysis, document understanding, and private AI experimentation. QwenCloud documentation lists Qwen3.8-Max as supporting a 1M context window, thinking, function calling, built-in tools, and structured output, showing the broader Qwen3.8 generation’s focus on advanced agent and application workflows. Qwen3.8-27B is especially useful for teams that want Qwen-family capabilities without the infrastructure demands of Max-scale deployment. Community posts around the release point to active interest in Hugging Face, Unsloth GGUF, Ollama, and local inference use cases. Third-party coverage also notes practical hardware discussions around quantized Qwen3.8-27B deployment, including claims that 4-bit variants can fit more easily on consumer or workstation GPUs. The model can be positioned for organizations that need open AI infrastructure, coding agents, local model evaluation, private deployments, and cost-controlled experimentation. By combining open-weight access, a practical 27B model size, Qwen3.8-era performance ambitions, coding-oriented workflows, and local deployment interest, Qwen3.8-27B gives developers a flexible foundation for building AI products and agents.

Qwen3.6-Max-Preview is a cutting-edge language model designed to elevate intelligence, adhere to instructions, and enhance the effectiveness of real-world agents within the Qwen ecosystem. Building on the Qwen3 series, this version features improved world knowledge, better alignment with user directives, and significant upgrades in coding capabilities for agents, enabling the model to proficiently handle complex, multi-step challenges and software development tasks. It is specifically tailored for situations that demand sophisticated reasoning and execution, allowing for an interactive approach that goes beyond simple response generation to include tool usage, management of extensive contexts, and structured problem-solving across disciplines such as coding, research, and business operations. The framework continues to reflect Qwen's dedication to creating large, efficient models capable of managing extensive context windows while ensuring dependable performance across multilingual and knowledge-driven initiatives. This innovative architecture not only aims to boost productivity but also fosters creativity in a wide range of applications, paving the way for future advancements in technology and collaboration.

Qwen3.7-Max signifies the pinnacle of innovation in Qwen's proprietary model series, specifically designed for the agent-centric era, and acts as a solid platform for a multitude of applications such as writing and debugging code, automating office workflows, and sustaining prolonged autonomous browsing sessions. This model excels in coding performance, showcasing exceptional skills in software engineering, terminal operations, graphical user interface interactions, web surfing, and the effective use of agentic tools. By improving the synergy between the model's intelligence and actual agent execution, Qwen3.7-Max supports sophisticated planning, reasoning over extended contexts, reliable function invocation, and the management of complex, multi-step tasks in intricate workflows. Additionally, it enhances multimodal and document-oriented tasks via Qwen Studio, which facilitates chatbot interactions, interprets images and videos, creates visuals, processes documents, develops presentations, provides coding assistance, performs thorough research, and supports web development. With this extensive array of capabilities, Qwen3.7-Max is positioned as a premier solution for various operational requirements in today's dynamic digital environment, ensuring users can efficiently tackle a wide range of challenges. As technology continues to evolve, the importance of such advanced models will only grow, making Qwen3.7-Max an invaluable asset for future endeavors.

Qwen3.5 is an advanced open-weight multimodal AI system built to serve as the foundation for native digital agents capable of reasoning across text, images, and video. The primary release, Qwen3.5-397B-A17B, introduces a hybrid architecture that combines Gated DeltaNet linear attention with a sparse mixture-of-experts design, activating just 17 billion parameters per inference pass while maintaining a total parameter count of 397 billion. This selective activation dramatically improves decoding throughput and cost efficiency without sacrificing benchmark-level performance. Qwen3.5 demonstrates strong results across knowledge, multilingual reasoning, coding, STEM tasks, search agents, visual question answering, document understanding, and spatial intelligence benchmarks. The hosted Qwen3.5-Plus variant offers a default one-million-token context window and integrated tool usage such as web search and code interpretation for adaptive problem-solving. Expanded multilingual support now covers 201 languages and dialects, backed by a 250k vocabulary that enhances encoding and decoding efficiency across global use cases. The model is natively multimodal, using early fusion techniques and large-scale visual-text pretraining to outperform prior Qwen-VL systems in scientific reasoning and video analysis. Infrastructure innovations such as heterogeneous parallel training, FP8 precision pipelines, and disaggregated reinforcement learning frameworks enable near-text baseline throughput even with mixed multimodal inputs. Extensive reinforcement learning across diverse and generalized environments improves long-horizon planning, multi-turn interactions, and tool-augmented workflows. Designed for developers, researchers, and enterprises, Qwen3.5 supports scalable deployment through Alibaba Cloud Model Studio while paving the way toward persistent, economically aware, autonomous AI agents.

Qwen3.5-Plus is a next-generation multimodal large language model built for scalable, enterprise-grade reasoning and agentic applications. It combines linear attention mechanisms with a sparse mixture-of-experts architecture to maximize inference efficiency while maintaining performance comparable to leading frontier models. The system supports text, image, and video inputs, generating high-quality text outputs suited for analysis, synthesis, and tool-augmented workflows. With a 1 million token context window and support for up to 64K output tokens, Qwen3.5-Plus enables deep, long-form reasoning across extensive documents and datasets. Its optional deep thinking mode allows for expanded chain-of-thought reasoning up to 80K tokens, making it ideal for complex analytical and multi-step problem-solving tasks. Developers can integrate structured outputs, function calling, prefix continuation, batch processing, and explicit caching to optimize both performance and cost efficiency. Built-in tool support through the Responses API includes web search, web extraction, image search, and code interpretation for dynamic multi-agent systems. High throughput limits and OpenAI-compatible API endpoints make deployment straightforward across global applications. With transparent token-based pricing and enterprise-level monitoring, Qwen3.5-Plus provides a powerful foundation for building intelligent assistants, multimodal analyzers, and scalable AI services.

Qwen-Audio-3.0-TTS-Plus is the advanced iteration of Qwen-Audio-3.0-TTS, crafted to significantly improve the naturalness and fidelity of voice outputs when prioritizing quality over rapidity. This version supports 16 languages and provides exceptional accuracy for multiple Chinese dialects, facilitating strong multilingual comprehension. A key highlight is its ability to preserve speaker characteristics across all languages, enabling cloned voices to remain both recognizable and consistent in a variety of linguistic environments. Developers are empowered to use simple natural-language commands, removing the complexity of manually tweaking acoustic settings, while having the ability to effortlessly manage emotions, roles, pacing, projection, and tone. Moreover, inline tags offer precise control over non-verbal cues like breaths, laughter, and shifts in emotion, making it ideal for applications in narration, gaming, character dialogue, and dubbing projects. This model not only enhances audio production quality but also provides a versatile solution that can adapt to a wide range of creative needs, ensuring an immersive experience for listeners.

Qwen-Audio-3.0-TTS-Flash is a real-time adaptation of Qwen-Audio-3.0-TTS, tailored for interactive environments with an initial packet delay of approximately 300 milliseconds. This version supports 16 languages and provides enhanced audio fidelity for multiple Chinese dialects. In multilingual evaluations, Flash stands out with the lowest average word and character error rates in its class, measured at 3.87, showcasing remarkable clarity while preserving the distinct characteristics of various speakers across different languages. Developers have the convenience of managing output through simple language instructions, eliminating the need for manual adjustment of acoustic settings; this feature empowers them to fine-tune elements such as emotion, role, scenario, pace, projection, and tone using intuitive commands. Furthermore, inline tags facilitate the integration of specific non-verbal cues, making the model exceptionally suitable for a broad range of applications, such as conversational agents, storytelling, gaming, dubbing, and other expressive speech situations. Notably, the voice cloning capabilities are adept at functioning effectively even with suboptimal reference audio; this is achieved through targeted acoustic simulation that minimizes background noise and reverberation while preserving the tonal qualities of the original voice. As a result, this cutting-edge technology not only enhances versatility but also enriches the overall audio experience across diverse platforms and applications, making it a valuable tool for developers and content creators alike.

Wan3.0-Video is a comprehensive video generation platform developed by Qwen Cloud that combines multiple creative features within a single interface, including the ability to convert text into video, turn images into video, and create videos inspired by references, alongside capabilities for editing, duplication, and motion guidance. This versatile model supports an array of inputs like audio, images, text, and videos, which empowers creators to shape the generation process using diverse sources beyond simple text prompts. With the capacity to create videos lasting up to 30 seconds, it provides omni-modal reference support, thereby enhancing user creativity by facilitating the integration of visual elements, movements, characters, and various artistic cues into the final output. Additionally, Wan3.0-Video can process files, analyze web pages, and interpret complex images during its creation phase. Its image-to-video functionality allows for both first-frame and first-and-last-frame generation, offering users the ability to dictate the beginning of a sequence or anchor both ends of a shot, thus significantly enriching the storytelling potential of the generated videos. Moreover, this all-encompassing model fosters innovative creative possibilities by enabling the effortless combination of different media types throughout the video production workflow, ultimately transforming the way creators engage with their projects.

VPC provides the ability to develop a private network space within Alibaba Cloud, which permits the modification of IP address ranges, network segments, routing tables, and gateways. It also supports the integration of VPC with conventional Internet Data Centers (IDCs) through leased lines, VPNs, or GRE connections, thereby enabling hybrid cloud capabilities. The implementation of the service system can take place in both local and cloud-based IDCs. Various service modules are built on Alibaba Cloud VPC to create entirely isolated cloud environments. Moreover, the interaction between cloud and non-cloud services occurs effortlessly over the Internet, improving operational adaptability and resource integration. This interconnected framework empowers organizations to effectively utilize the advantages of both cloud and on-premises infrastructures, fostering innovation and growth. As a result, businesses can optimize their resource allocation while ensuring secure and efficient operations across multiple platforms.

Qwen3.6-Plus is a cutting-edge AI model developed by Alibaba Cloud, designed to enable real-world intelligent agents, advanced coding workflows, and multimodal reasoning. It represents a major evolution in the Qwen series, offering enhanced performance across coding, reasoning, and tool-based tasks. With a default 1 million token context window, the model can process extremely large inputs and maintain context across long interactions. It excels in agentic coding, supporting tasks such as debugging, terminal operations, and large-scale repository management. The model integrates reasoning, memory, and execution capabilities, allowing it to function as a highly autonomous and reliable AI agent. Qwen3.6-Plus also features strong multimodal capabilities, enabling it to analyze images, videos, documents, and UI elements for deeper understanding and action. It supports real-world applications such as workflow automation, visual reasoning, and interactive task execution. Developers can access the model via API and integrate it with tools like OpenClaw, Qwen Code, and other coding assistants. Features like preserved reasoning context improve performance in complex, multi-step tasks and reduce redundant processing. The model is optimized for enterprise use, offering stability, scalability, and high accuracy across diverse domains. It also supports multilingual environments, making it suitable for global applications. Overall, Qwen3.6-Plus provides a powerful foundation for building next-generation AI agents capable of perception, reasoning, and action.

Happy Horse is an AI video generation and editing platform designed to help creators transform prompts, images, references, and first-frame ideas into cinematic video content. The platform gives users multiple ways to begin a project, including text-based generation, reference-driven generation, first-frame input, and video editing. Creators can generate videos from imaginative concepts, then modify details to refine the final result. Happy Horse is built for visual experimentation, storytelling, and AI cinema, making it useful for artists who want to explore ideas quickly without traditional production barriers. Its creative environment includes featured projects, community videos, short AI films, and showcase content from different creators. The platform also highlights AI cinema events, encouraging users to submit and celebrate AI-made cinematic work. Users can sign in to receive free credits and take advantage of special offers for additional generation access. Happy Horse supports short-form video experimentation, concept development, visual storytelling, and creative exploration. The platform’s tools help users turn sparks of imagination into videos that can be shared, refined, or developed into larger creative projects. Its combination of generation, reference input, first-frame control, editing, and community inspiration makes it a practical workspace for AI video creators. Happy Horse helps filmmakers, designers, artists, and everyday creators bring visual ideas to life with speed, flexibility, and expressive control.

Qwen3.7-Plus represents a cutting-edge multimodal agent model that effectively merges vision and language into a flexible foundation for intelligent agents. Building on the agentic capabilities of Qwen3.7, it expands its functionality to encompass visual understanding, reasoning, grounded interactions, and the utilization of diverse multimodal tools, enabling agents to interpret, analyze, and navigate through text, images, documents, screens, and complex real-world environments. This model is specifically designed for dynamic tasks that extend beyond simple question answering, facilitating a range of activities such as visual searches, document comprehension, evaluations of charts and tables, screen analysis, GUI interactions, image-based reasoning, and workflows that integrate perception, planning, and action. Qwen3.7-Plus strengthens the connection between linguistic reasoning and visual signals, equipping users to ask questions about images, interpret intricate multimodal data, extract structured information, and generate replies that blend contextual and visual components, thereby enhancing the potential for interactive AI applications. With these advancements, users are empowered to engage in more complex and refined interactions with the system, transforming it into a highly effective tool for a multitude of practical uses across various fields. The model’s ability to adapt to different scenarios further solidifies its relevance in today’s rapidly evolving technological landscape.

Qwen3.8-2.4T-A95B emerges as the largest open model in the Qwen3.8 series, presenting advanced Qwen-Max-class capabilities in a format that is accessible to the public. Built on the robust foundation of Qwen3.5, this model offers marked improvements in performance across various domains, including coding, professional applications, research, and complex, extended agentic tasks, underscoring its ability to reliably execute intricate, multi-step workflows to completion. With its innovative mixture-of-experts architecture, it features a remarkable total of 2.4 trillion parameters, of which 95 billion are activated, utilizing 512 experts and allowing for simultaneous engagement of 10 routed experts alongside one shared expert. The model supports a native context length of 262,144 tokens, extendable to about 1.01 million tokens, thereby enabling considerable adaptability for diverse applications. Additionally, enhancements in agent execution, such as superior autonomous planning and improved responsiveness to environmental cues, enhance its overall efficiency. Its extensive compatibility with popular agent frameworks and development tools further aids in smooth integration into current systems, making it an appealing option for both developers and researchers. This versatility is particularly beneficial for those seeking to leverage advanced AI capabilities in their projects.

By integrating both elements, you achieve a superior analytics experience that enhances decision-making across your organization. With a robust model accessible to all, discovering and disseminating key metrics becomes a streamlined process. Setting up Omni is quick, taking only a few minutes, and it begins learning from each interaction, continually improving its performance over time. Our cutting-edge business intelligence solution automatically constructs a model based on your queries and generates metrics that can be easily shared. The intuitive user interface ensures that individuals at any skill level can effortlessly access, investigate, share, and collaborate with the necessary data. Furthermore, Omni allows for the application of specific queries directly to the data model, thereby expanding and enriching your shareable insights. Ultimately, Omni transforms business intelligence from dynamic data exploration into a structured framework that promotes both efficient and precise data utilization, fostering a culture of data-driven decision-making. As a result, organizations can leverage their data more effectively to drive growth and innovation.