アリババ、2.4兆パラメータのオープンウェイト「Qwen3.8-Max」を公開へ

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.

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.

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.

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.

After an automated layout pass with the open-source tool OpenROAD, the physical chip area shrank from 106x106 to 46x46 micrometers, an 81 percent reduction. The Qwen team says the model kept making deep structural changes even after hundreds of iterations instead of settling for surface-level tweaks.

The second case study is E-Commerce-Bench, a simulation of an entire fiscal year in online retail based on anonymized data from Taobao and Tmall. The model starts with 100,000 yuan in capital and has to run multiple online stores in parallel for a full year. That means buying products, negotiating with suppliers in natural language, adjusting prices, managing returns, and dealing with crises like typhoons or supply chain disruptions.

Hidden in the supplier pool are 152 scammers that the model has to spot. Qwen3.8-Max ended up with a balance of 416,252 yuan, quadrupling its starting capital. That's 38 percent more than the runner-up GLM 5.2 and more than 2.5 times what its predecessor Qwen3.7-Max managed. The model invested aggressively early in the year and pulled in a net profit of over 100,000 yuan during the holiday season.

Multimodal skills and app reconstruction without source code

For multimodal tasks, Qwen3.8-Max can process documents with over 200 pages and videos longer than 100 hours, the team says. They're also introducing RecreationBench, a new benchmark that requires the model to rebuild running applications without access to source code.

The model can only observe the target app through interaction, meaning clicks and keyboard input. Testing covers Ubuntu, macOS, Windows, Android, and the web. Alongside this, the team is releasing Qwen-MM-Plugins, an extension library that adds image and video processing, visual tool use, and multimodal memory to existing agent systems.

In the benchmark tables the Qwen team published, the model lands near or above Claude Opus 4.8, Claude Fable 5, and GPT-5.6 Sol across many categories. On PaperBench, Qwen3.8-Max hits 93, the highest score in the comparison. On TerminalBench 2.1, it scores 86.6, trailing GPT-5.6 Sol's 88.8. As is typical with self-reported numbers from model makers, these results come from internal runs. Independent verification is still pending.

The Qwen team attributes the model's ability to sustain such long-running tasks to a major expansion of training environments during reinforcement learning. Training no longer focused on single tasks alone but also covered multi-day workflows, nested directory structures instead of individual files, and a variety of agent harnesses.

The team's internal score index across more than ten benchmarks rose from 0.474 to 0.725. The model performed best at around 4,000 environments, after which scores dipped slightly.

China's open-model race heats up

Qwen3.8-Max's most direct rival also comes from China. Moonshot AI released Kimi K3 with open weights on Hugging Face on July 27, a multimodal mixture-of-experts model with 2.8 trillion parameters and a one-million-token context window. Along with the weights, Moonshot also published parts of its own infrastructure, including attention kernels, an MoE communication library, and tools for running agents at scale. Independent testing tempered the company's claims, though. K3 fell well short of top Western models in both cyber capabilities and complex math.

Qwen3.8-Max is available now through QwenCloud. The weights are set to go live on Hugging Face and ModelScope next week. The model supports both OpenAI's Chat Completions format and Anthropic's API protocol, so it plugs directly into Claude Code, Codex, Qoder CLI, Qwen Code, and OpenClaw. A parameter called reasoning_effort lets users choose between three levels that trade speed for thoroughness.

Qwen3.8-Max isn't the only piece Alibaba has added recently. A few weeks ago, the team introduced Qwen-Image-3.0, an image generator for information-dense layouts that handles inputs up to 4,500 tokens and renders readable text as small as ten pixels. At the other end of the scale, Alibaba continues to push small open models like Qwen3.6-35B-A3B, which has 35 billion total parameters but only activates three billion at a time.