[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"academy-blogs-en-1-1-all-qwen3-8-max-ai-coding-cowork-all--*":3,"academy-blog-translations-puvlk6qt2wlffcx":94},{"data":4,"page":93,"perPage":93,"totalItems":93,"totalPages":93},[5],{"alt":6,"collectionId":7,"collectionName":8,"content":9,"cover_image":10,"cover_image_path":11,"created":12,"created_by":13,"expand":14,"id":87,"keywords":88,"locale":59,"published_at":89,"scheduled_at":75,"school_blog":83,"short_description":90,"status":81,"title":91,"updated":92,"updated_by":13,"slug":84,"views":86},"Cover image of Qwen3.8-Max AI model article for coding and cowork","sclblg987654321","school_blog_translations","\u003Cp>The recent competition among AI models has not stopped at merely answering questions, summarizing texts, or helping to write short code snippets anymore. The new goal for many companies is shifting towards building AI Agents capable of taking on complex tasks, planning, using tools, and executing continuous, multi-step workflows without waiting for humans to issue new commands at every step.\u003C\u002Fp>\u003Cp>On August 3, 2026, the Qwen team published an introductory article for \u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\u002F\u002Fqwen.ai\u002Fblog?id=qwen3.8\">Qwen3.8-Max\u003C\u002Fa> titled \"A New Bar for Coding and Cowork,\" positioning this model to stand out in software development, real-world enterprise work, long-horizon tasks, and multimodal agent operations.\u003C\u002Fp>\u003Cp>One of the figures that has garnered the most attention is the model's increased size to 2.4 trillion parameters. However, what is even more interesting than its size is the approach \u003Ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https:\u002F\u002Fqwen.ai\u002Fhome\">Qwen \u003C\u002Fa>is taking to push the model from a mere question-answering assistant to a system that can take on greater responsibility for real-world workflows.\u003C\u002Fp>\u003Cblockquote>\u003Cp>\u003Cstrong>Note:\u003C\u002Fstrong> The information in this article was verified as of August 5, 2026. All capabilities and test results are based on data officially disclosed by the Qwen team on their launch page.\u003C\u002Fp>\u003C\u002Fblockquote>\u003Ch2>What is Qwen3.8-Max?\u003C\u002Fh2>\u003Cp>Qwen3.8-Max is the new generation AI model from the Qwen team, developed under Alibaba. The launch page states that the model has a total size of 2.4 trillion parameters and has been developed to enhance capabilities across several core areas, including:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>Writing and editing Source Code\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Software development tasks requiring continuous, multi-step execution\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Real-world enterprise tasks or Knowledge Work\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Long-horizon tasks\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Multimodal Agent operations\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Integration with Agent Frameworks and developer tools\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Qwen chose the phrase \"Coding and Cowork\" in the title of their launch article to reflect that the model is not designed solely to generate code, but is intended to collaborate with humans across a broader workflow—ranging from software engineering to data analysis and various professional tasks.\u003C\u002Fp>\u003Ch2>What is the difference between Coding and Cowork?\u003C\u002Fh2>\u003Cp>In the context of Qwen3.8-Max, the term \"Coding\" does not merely mean asking the AI to write a function or generate a short code snippet. It encompasses tasks that require understanding the project, utilizing tools, verifying results, and iteratively modifying the work over multiple cycles.\u003C\u002Fp>\u003Cp>An example workflow might include:\u003C\u002Fp>\u003Col>\u003Cli>\u003Cp>Reading project requirements\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Exploring the repository and file structure\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Planning modifications\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Writing or editing multiple sections of Source Code\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Running Build or Test commands\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Checking for Errors\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Returning to fix the code\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Creating a Commit or Pull Request\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Verifying the final results before delivering the work\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Fol>\u003Cp>On the other hand, the term \"Cowork\" refers to having AI assist in executing operations beyond programming, such as data analysis, document reading, and tasks in finance, law, medicine, or other productivity-enhancing missions.\u003C\u002Fp>\u003Cp>The Qwen team utilizes an internal evaluation called CoWorkBench, which covers long-horizon tasks across various fields such as Computer Science, Finance, Law, Medicine, and other productivity domains. However, since CoWorkBench is Qwen's internal benchmark, it should be viewed as data used by the developers to indicate the model's direction, rather than an independent test result.\u003C\u002Fp>\u003Ch2>Key Features of Qwen3.8-Max\u003C\u002Fh2>\u003Ch3>1. Scaling the model to 2.4 trillion parameters\u003C\u002Fh3>\u003Cp>Qwen states that Qwen3.8-Max possesses a total of 2.4 trillion parameters, an increase in model size aimed at elevating capabilities in coding, real-world tasks, and agentic workflows.\u003C\u002Fp>\u003Cp>However, parameter count alone should not be used as the sole evidence of a model's superiority. The performance of an AI system also depends on the training methodology, the preparation of tools, Agent Frameworks, prompts, environments, and the nature of the tasks being tested.\u003C\u002Fp>\u003Cp>For developers, the crucial question is not just how many parameters a model has, but rather how well it can operate within the repository and toolchain currently utilized by their team.\u003C\u002Fp>\u003Ch3>2. Focus on Long-horizon Coding\u003C\u002Fh3>\u003Cp>Long-horizon coding involves having the AI perform software development tasks continuously over an extended period, rather than concluding after generating a single response or code snippet. Qwen's launch page presents an experiment where the model was allowed to run fully autonomously for over 10 days. The system attempted to build and improve its own Agent Harness while taking on tasks from the community and working continuously.\u003C\u002Fp>\u003Cp>Data recorded by Qwen as of July 30, 2026, indicates that after running fully autonomously for about 16 days, the experimental repository accumulated:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>265 Commits\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>127 Pull Requests\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>These numbers demonstrate that Qwen aims to test the model's capabilities at the workflow level, rather than merely measuring whether the AI can generate code that passes test cases in a single shot.\u003C\u002Fp>\u003Ch3>3. Experimenting with Agents improving their own workflows\u003C\u002Fh3>\u003Cp>Another fascinating concept in the launch article is the creation of a Self-evolving Harness, an agentic system capable of refining its own tools and operational processes during the experiment.\u003C\u002Fp>\u003Cp>A \"Harness\" in the context of a Coding Agent is a suite of systems that connects the model to external tools, such as:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>File System\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Terminal\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Source Control\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Test Runner\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Browser\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Image and Video Processing\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Memory\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Tool Calling\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Qwen states that the long-term experiment provided the Agent with the opportunity to continuously develop components of its Harness, accepting new tasks and adjusting its processes based on the intermediate outcomes.\u003C\u002Fp>\u003Cp>However, the term \"Self-evolving\" here does not imply that the model can independently alter its weights or retrain itself. Instead, it refers to the iterative improvement of code, tools, and the Agent's system architecture within the defined environment.\u003C\u002Fp>\u003Ch3>4. Prioritizing real-world tasks over single prompts\u003C\u002Fh3>\u003Cp>A key aspect of Qwen3.8-Max is the shift from measuring capabilities using single prompts toward evaluating multi-step tasks that require a significant amount of time.\u003C\u002Fp>\u003Cp>Tasks of this nature may require the model to:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>Plan before execution\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Select appropriate tools\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Manage data from multiple sources\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Remember the state of previous tasks\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Check for errors\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Revise the plan when outcomes fall short of expectations\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Work continuously until a verifiable deliverable is achieved\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>This approach reflects a paradigm shift in the AI industry: moving away from chatbots that wait for commands one at a time, towards Agents that strive to take responsibility for longer, continuous work processes.\u003C\u002Fp>\u003Cimg src=\"https:\u002F\u002Fpb.tumwebsme.com\u002Fapi\u002Ffiles\u002Fpbc_2997280662\u002Ff3txvgwt1happju\u002F3_11zon_lrhv0y695t.webp\" style=\"display: block; margin: 0px auto;\">\u003Ch2>How well does Qwen3.8-Max code?\u003C\u002Fh2>\u003Cp>The Qwen team presented various evaluation results to showcase its capabilities in coding and agentic workflows, but interpreting these scores requires considering the test conditions. Details on the launch page indicate that one evaluation suite covered a total of 87 tasks, reporting the average score from testing each task three times. Furthermore, the models compared may have operated through different Agent Harnesses, such as Claude Code or other Coding Agent tools.\u003C\u002Fp>\u003Cp>Therefore, even though Qwen3.8-Max performed well in the benchmarks presented by the company, one should not conclude that this model is definitively better at coding than others in all scenarios. Performance can be influenced by several factors, including:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cp>The specific Agent Harness used\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>The number of turns the Agent is allowed to operate\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>The tools and permissions the model has access to\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>The allocated time limits\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>The scoring methodology\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>The Test Environment\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>Whether Reasoning processes are toggled on or off\u003C\u002Fp>\u003C\u002Fli>\u003Cli>\u003Cp>How Context is managed during the operation\u003C\u002Fp>\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The most reliable approach for development teams is to test the model with their own actual repositories, requirements, and toolchains.\u003C\u002Fp>\u003Ch2>How does Qwen Code work with Qwen3.8-Max?\u003C\u002Fh2>\u003Cp>Within the launch page, Qwen introduces Qwen Code as one of the tools for utilizing the Qwen model family for software development tasks via the terminal.\u003C\u002Fp>\u003Cp>The installation command specified by Qwen is:\u003C\u002Fp>\u003Cp>Bash\u003C\u002Fp>\u003Cpre>\u003Ccode>npm install -g @qwen-code\u002Fqwen-code@latest\nqwen\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>Qwen Code is a programming agent specifically optimized to work with the Qwen model family. It can help explore codebases, edit files, execute commands, and carry out software development workflows entirely via the terminal.\u003C\u002Fp>\u003Cp>The launch page also mentions integrating the model with other Agent Frameworks, such as OpenClaw, reflecting that Qwen does not intend to restrict the model exclusively to its own interfaces.\u003C\u002Fp>\u003Cp>However, before deploying it to real repositories, you should carefully review the Agent's permissions—especially its rights to run commands, modify files, access secrets, or connect to production systems.\u003C\u002Fp>\u003Ch2>Who is Qwen3.8-Max for?\u003C\u002Fh2>\u003Cp>Based on the direction presented by Qwen, this model is likely to attract the following user groups:\u003C\u002Fp>\u003Ch3>1. Developers experimenting with Coding Agents\u003C\u002Fh3>\u003Cp>It is suited for those who want to experiment with letting an AI explore repositories, modify code, run tests, and interact with the terminal, rather than just using AI for line-by-line code completion.\u003C\u002Fp>\u003Ch3>2. Teams handling large-scale projects\u003C\u002Fh3>\u003Cp>Projects containing numerous files that require simultaneous modifications across multiple sections may benefit from an Agent capable of planning and executing continuous, multi-step tasks.\u003C\u002Fp>\u003Ch3>3. Teams building Agent Platforms\u003C\u002Fh3>\u003Cp>Developers looking to build Harnesses, tool-calling mechanisms, memory architectures, or inter-agent coordination systems could use Qwen3.8-Max as one of their experimental models.\u003C\u002Fp>\u003Ch3>4. Organizations interested in AI Cowork\u003C\u002Fh3>\u003Cp>Teams wanting to leverage AI for analyzing documents, data, and professional workflows may want to further monitor its Cowork capabilities and results from the CoWorkBench tests.\u003C\u002Fp>\u003Cp>Nevertheless, high-impact tasks such as those in law, medicine, finance, or modifying production systems—still strictly require human experts to verify the outputs before any real-world deployment.\u003C\u002Fp>\u003Ch2>Limitations to consider before concluding Qwen3.8-Max is the best\u003C\u002Fh2>\u003Cp>Even though the launch page showcases Qwen3.8-Max's capabilities across various fronts, there are caveats to consider when interpreting the data:\u003C\u002Fp>\u003Ch3>1. Test results are from the model's developers\u003C\u002Fh3>\u003Cp>The benchmarks and case studies in the article were presented by the Qwen team, the developers of the model themselves. Therefore, it is advisable to wait for additional test results from independent developers and third-party organizations.\u003C\u002Fp>\u003Ch3>2. CoWorkBench is an internal benchmark\u003C\u002Fh3>\u003Cp>Qwen explains that CoWorkBench is an internal evaluation suite for long-horizon tasks across multiple fields. The specifics of the dataset and the evaluation methodology are crucial for correctly interpreting the scores.\u003C\u002Fp>\u003Ch3>3. Different models may use different Agent Harnesses\u003C\u002Fh3>\u003Cp>The launch page indicates that some models were evaluated using entirely different frameworks or Coding Agents. Variations in tooling can directly impact the final scores.\u003C\u002Fp>\u003Ch3>4. 16 days of continuous operation does not guarantee production readiness\u003C\u002Fh3>\u003Cp>The volume of Commits and Pull Requests shows that the Agent can maintain continuous activity. However, the code's quality, security, accuracy, and maintainability must still be evaluated independently of the sheer quantity of work generated.\u003C\u002Fp>\u003Cp>Therefore, Qwen3.8-Max should be viewed as another step forward in the evolution of Long-horizon Agents, rather than rushing to the conclusion that AI can already replace software engineers or experts in various professional fields.\u003C\u002Fp>\u003Ch2>Frequently Asked Questions (FAQ) about Qwen3.8-Max\u003C\u002Fh2>\u003Ch3>How many parameters does Qwen3.8-Max have?\u003C\u002Fh3>\u003Cp>Qwen states that Qwen3.8-Max has a total of 2.4 trillion parameters. It is a model developed by the company to boast enhanced capabilities in coding, cowork, long-horizon tasks, and multimodal agent operations.\u003C\u002Fp>\u003Ch3>Can Qwen3.8-Max be used for coding?\u003C\u002Fh3>\u003Cp>Yes, it can. A key focal point of the launch page is its capabilities as a Coding Agent, especially for tasks that involve exploring repositories, utilizing tools, editing code, and executing continuous, multi-step workflows.\u003C\u002Fp>\u003Ch3>What is Long-horizon Coding?\u003C\u002Fh3>\u003Cp>Long-horizon coding refers to software development tasks that do not end with a single output. It requires planning, modifying code, running tests, analyzing errors, and iteratively improving the work over multiple cycles until the specified outcome is achieved.\u003C\u002Fp>\u003Ch3>How long can Qwen3.8-Max work autonomously?\u003C\u002Fh3>\u003Cp>In the case study presented by Qwen, the Agent was left to run autonomously for about 16 days. As of July 30, 2026, the repository had accumulated 265 Commits and 127 Pull Requests. However, it should be noted that this is a result of the experimental environment set up by Qwen.\u003C\u002Fp>\u003Ch3>What is CoWorkBench?\u003C\u002Fh3>\u003Cp>CoWorkBench is Qwen's internal benchmark designed to evaluate long-horizon tasks across various fields, such as Computer Science, Finance, Law, Medicine, and other productivity enhancement domains.\u003C\u002Fp>\u003Ch3>Is Qwen3.8-Max better than Claude or GPT?\u003C\u002Fh3>\u003Cp>It cannot be concluded yet that it is superior in all aspects. Even though Qwen reports outstanding evaluation results, each model might have been tested using different Agent Harnesses and under differing conditions. A more appropriate approach is for teams to test the models with their own real-world tasks.\u003C\u002Fp>\u003Ch3>Through which tools can Qwen3.8-Max be used?\u003C\u002Fh3>\u003Cp>The launch page offers recommendations for using it in conjunction with Qwen Code and mentions connecting it to Agent Frameworks like OpenClaw. Details regarding model access and availability should be verified again through official Qwen channels before usage.\u003C\u002Fp>\u003Ch3>Does Qwen3.8-Max support Thai well?\u003C\u002Fh3>\u003Cp>The launch page used as the source for this article did not specifically report Thai language benchmark results, so one should not yet draw conclusions about its Thai language proficiency. If you plan to use it in practice, you should directly test it with your system's prompts, documents, and specific terminology.\u003C\u002Fp>\u003Cdiv data-type=\"horizontalRule\">\u003Chr>\u003C\u002Fdiv>\u003Ch2>Summary\u003C\u002Fh2>\u003Cp>Qwen3.8-Max is a new generation AI model from the Qwen team, scaled up to 2.4 trillion parameters and focused on capabilities in Coding, Cowork, Long-horizon Tasks, and Multimodal Agents.\u003C\u002Fp>\u003Cp>What is more fascinating than the model's size is the experiment where the Agent was left to perform continuous software development for about 16 days, generating a massive amount of Commits and Pull Requests while evolving the Harness it uses to manage its own workflows during operation. This approach reflects that Coding AI is transitioning from mere code-completion assistants to Agents capable of planning, utilizing the terminal, verifying results, and taking responsibility for longer-term tasks.\u003C\u002Fp>\u003Cp>However, all the information from the launch phase relies on self-reported results from the Qwen team, and certain benchmarks like CoWorkBench are internal testing suites. Determining whether Qwen3.8-Max is suitable for a given system should rely on experimentation with actual repositories, tools, requirements, costs, and security standards of real-world tasks, rather than looking solely at parameter counts or rankings on a benchmark.\u003C\u002Fp>","2e190ugbsu8_rt7d89y2z4.png","https:\u002F\u002Ftwsme-r2.tumwebsme.com\u002Fsclblg987654321\u002Fa2guksxxf0iaumw\u002F2e190ugbsu8_rt7d89y2z4.png","2026-08-05 03:26:38.810Z","423vhnv3ckczcyn",{"keywords":15,"locale":53,"school_blog":63},[16,22,26,30,36,40,45,49],{"collectionId":17,"collectionName":18,"created":19,"created_by":13,"id":20,"name":21,"updated":19,"updated_by":13},"sclkey987654321","school_keywords","2026-08-05 02:46:06.109Z","hpp3n988gwkgfs9","Qwen3.8-Max",{"collectionId":17,"collectionName":18,"created":23,"created_by":13,"id":24,"name":25,"updated":23,"updated_by":13},"2026-08-05 02:46:13.419Z","jlpuooolms61w5g","Qwen AI",{"collectionId":17,"collectionName":18,"created":27,"created_by":13,"id":28,"name":29,"updated":27,"updated_by":13},"2026-08-05 02:46:19.741Z","tww04a0rhvml67m","Alibaba",{"collectionId":17,"collectionName":18,"created":31,"created_by":32,"id":33,"name":34,"updated":35,"updated_by":32},"2026-05-21 16:32:55.489Z","76qprkevbgfdps8","rdv13qjgom4ml7p","AI Coding","2026-06-07 06:49:18.676Z",{"collectionId":17,"collectionName":18,"created":37,"created_by":13,"id":38,"name":39,"updated":37,"updated_by":13},"2026-08-05 02:46:36.420Z","ezzywmfbece1sbu","Coding Agent",{"collectionId":17,"collectionName":18,"created":41,"created_by":32,"id":42,"name":43,"updated":44,"updated_by":32},"2026-03-04 08:44:26.139Z","dlm8aajwkiz9tae","AI Agent","2026-06-07 06:46:33.227Z",{"collectionId":17,"collectionName":18,"created":46,"created_by":13,"id":47,"name":48,"updated":46,"updated_by":13},"2026-08-05 02:46:51.783Z","t8pvu9zxl10jz00","Cowork",{"collectionId":17,"collectionName":18,"created":50,"created_by":13,"id":51,"name":52,"updated":50,"updated_by":13},"2026-07-08 04:24:36.696Z","p61c51jxh4xf15q","Developer Tools",{"code":54,"collectionId":55,"collectionName":56,"created":57,"flag":58,"id":59,"is_default":60,"label":61,"updated":62},"en","pbc_1989393366","locales","2026-01-22 11:00:02.726Z","twemoji:flag-united-states","qv9c1llfov2d88z",false,"English","2026-04-10 15:42:46.825Z",{"category":64,"collectionId":65,"collectionName":66,"created":67,"expand":68,"id":83,"slug":84,"updated":85,"views":86},"pkuzfil3b4ug2ea","pbc_2105096300","school_blogs","2026-08-05 02:56:47.163Z",{"category":69},{"blogIds":70,"collectionId":71,"collectionName":72,"created":73,"created_by":32,"id":64,"image":74,"image_alt":75,"image_path":76,"label":77,"name":78,"priority":79,"publish_at":80,"scheduled_at":75,"status":81,"updated":82,"updated_by":32},[],"sclcatblg987654321","school_category_blogs","2026-03-04 08:31:47.860Z","3w9eadde0ql_vquww3nx7o.png","","https:\u002F\u002Ftwsme-r2.tumwebsme.com\u002Fsclcatblg987654321\u002Fpkuzfil3b4ug2ea\u002F3w9eadde0ql_vquww3nx7o.png",{"en":78,"th":78},"Cutting-Edge Tech",0,"2025-01-27 08:43:38.395Z","published","2026-06-07 06:45:02.895Z","puvlk6qt2wlffcx","qwen3-8-max-ai-coding-cowork","2026-08-06 13:22:55.068Z",123,"a2guksxxf0iaumw",[20,24,28,33,38,42,47,51],"2026-08-05 05:08:51.856Z","Discover Qwen3.8-Max, a 2.4T parameter AI model by Alibaba focusing on coding, cowork, and long-horizon agentic workflows.","What is Qwen3.8-Max? The 2.4T Parameter AI Model for Coding & Cowork","2026-08-05 05:08:51.857Z",1,{"th":84,"en":84}]