Alibaba’s open-weight Qwen3.8-Max takes on long-horizon AI tasks with 2.4 trillion parameters

Alibaba’s open-weight Qwen3.8-Max takes on long-horizon AI tasks with 2.4 trillion parameters Jonathan Kemper View the LinkedIn Profile of Jonathan Kemper Aug 3, 2026 Key Points Alibaba has unveiled Qwen3.8-Max, a 2.4-trillion-parameter language model designed to complete complex tasks on its own over multiple days. In testing, the model autonomously built software, reproduced and improved on a research paper's results, and successfully ran a simulated e-commerce business. Internal benchmarks put its performance on par with top Western models. Qwen3.8-Max is available now, with weights set to drop next week. Ask about this article… Search Alibaba's new flagship model Qwen3.8-Max is built to handle complex tasks on its own over days at a time, from reproducing research papers to designing chips autonomously. The team plans to release the weights next week. Alibaba's Qwen team has unveiled Qwen3.8-Max, its most capable language model to date. The model scales to 2.4 trillion total parameters, with 95 billion active per query. Qwen3.8-Max builds on the Qwen3.5 architecture, and the team says the focus is on completing complex tasks independently over extended periods rather than just answering one-off prompts. Alibaba first announced the model in mid-July as a preview version available through Alibaba's Token Plan, Qoder, and QoderWork at ten percent of the standard price. Even then, the team cited 2.4 trillion parameters and ranked the model just behind Fable 5, but didn't share benchmarks. Qwen3.8-Max is the first model in the Qwen-Max class whose weights will be made publicly available.Ad Three autonomous coding runs put the model through its paces To show off Qwen3.8-Max's coding chops, the team presented three case studies in which the model worked without any human help.AdDEC_D_Incontent-1 In the first, Qwen3.8-Max spent 16 days building the command-line tool oh-my-cli. The model took incoming user requests, turned them into GitHub issues, assigned them to itself, wrote the code, ran tests, and improved the results iteratively. By July 30, 2026, it had racked up 265 commits, 127 pull requests, and 151 issues, all without a single human touch. In the second case, the model received the research paper "Unified Data Selection for LLM Reasoning" but no starter code. Its job was to reproduce the paper's results and then improve on them. Over roughly five days and about 125 hours of compute time, Qwen3.8-Max wrote 7,600 lines of code and ran 33 GPU training jobs, according to the team. It first reproduced all six of the paper's main results. Then it tested 18 of its own ideas across four rounds and beat the paper's method on the AIME24 math benchmark by 2.7 points.Ad The third case involved the WWW2025 Multimodal Dialogue Intent Recognition Challenge on Alibaba's Tianchi platform, where 526 human teams competed. Within 24 hours, the model fine-tuned several Chinese language models along with Qwen2.5-VL-7B for product screenshots and combined them into a voting system. Across 45 submissions, accuracy climbed from 0.60 to 0.853. That put Qwen3.8-Max ahead of 458 of the 526 human teams. Chip design and a simulated fiscal year test long-horizon planning Two more case studies target tasks that stretch across hundreds of interaction rounds. In the first, Qwen3.8-Max had to design a cryptographic building block for encryption schemes. The key efficiency metric for such a circuit is the number of logic gates it needs, the basic elements on a chip. Fewer gates mean a smaller, more efficient chip. The model started with a working but bloated design using 8,298 gates and whittled it down to 678 gates over roughly 500 iterations.AdDEC_D_Incontent-2 After an automated layout pass with the open-