GLM-5.2 is Chinese AI lab Z AI’s latest flagship model for long-horizon tasks.

Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. — Read More

Most people know Xiaomi as the Chinese phone brand. The one that makes cheap electric scooters and air purifiers. Not exactly the company you’d expect to break a major AI inference speed record on a Monday morning.

And yet. Xiaomi just released MiMo-V2.5-Pro-UltraSpeed, a serving mode for its trillion-parameter flagship that hits over 1,000 tokens per second—peaking near 1,200 in demos. — Read More

Outside of China, Alibaba is mostly known as an e-commerce titan.

But inside the country, the company is obsessed over catching up to DeepSeek on its development of AI models, and catching up to Huawei on the chips that power them.

When Alibaba’s chip design unit T-Head unveiled its latest AI chip, the Zhenwu M890, last week, it also outlined a multi-year chip roadmap showing how the M890’s ​future successors would deliver massive performance gains in the next few years. Less than a year ago, Huawei had laid out a similar timeline that ran until 2028. — Read More

The Chinese companies building language models are set up as the perfect fast-followers for the technology, building on long-standing cultural traditions in education and work, along with subtly different approaches to building technology companies. When you look at the outputs, the latest, biggest models enabling agentic workflows, and the ingredients, excellent scientists, large-scale data, and accelerated computing, the Chinese and American labs look largely similar. The lasting differences emerge in how these are organized and conditioned.

long thought that a reason that the Chinese labs are so good at catching up and keeping up with the frontier is that they’re culturally aligned for this task, but without talking to people directly I felt like it wasn’t my place to attribute substantial influence to this hunch. Speaking with many wonderful, humble, and open scientists at the leading Chinese labs has crystallized a lot of my beliefs. — Read More

Chinese AI lab DeepSeek’s last model release was V3.2 (and V3.2 Speciale) last December. They just dropped the first of their hotly anticipated V4 series in the shape of two preview models, DeepSeek-V4-Pro and DeepSeek-V4-Flash.

Both models are 1 million token context Mixture of Experts. Pro is 1.6T total parameters, 49B active. Flash is 284B total, 13B active. They’re using the standard MIT license.

I think this makes DeepSeek-V4-Pro the new largest open weights model.  — Read More

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What is the Hunter Alpha AI Model? Hunter Alpha, a powerful artificial intelligence model, mysteriously appeared on the AI gateway platform OpenRouter recently. No one knows where it came from. It was described by the platform as a “stealth model”. There’s no official announcement or press release about this AI model, but it drew attention because of its specs of 1 trillion parameters, a 1 million token context window, and free access. — Read More

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“Vibecoding” doesn’t lend itself to easy translation. For now, Chinese speakers call it 氛围编程 fènwéi biānchéng, 氛围 being “atmosphere”/”vibes” and 编程 being coding. This is an awkward expression because 氛围 usually refers to the atmosphere of a space or environment, and doesn’t have the connotation of care-free DIY that “vibe” does in colloquial American English. 氛围编程 sounds nonsensical as a phrase — something like “coding up an atmosphere.”

But we make do, and oftentimes writers simply use the English word. Developers, creatives, and entrepreneurs in China have been creating many interesting coding projects with AI tools over the past year, utilizing not only popular tools by Silicon Valley giants like Cursor and Claude Code, but also domestic models as Chinese AI companies increasingly compete in the coding-agent market.

Tinkering culture has no borders, and companies are cashing in. This is a roundup of reports from Chinese media on how vibecoding is changing the landscape of technology in China. — Read More

This is the third and final blog in a three-part series on China’s open source community’s historical advancements since January 2025’s “DeepSeek Moment.” The first blog on strategic changes and open artifact growth is available here, and the second blog on architectural and hardware shifts is available here.

In this third article, we examine paths and trajectories of prominent Chinese AI organizations, and posit future directions for open source.

For AI researchers and developers contributing to and relying on the open source ecosystem and for policymakers understanding the rapidly changing environment, due to intraorganizational and global community gains, open source is the dominant and popular approach for Chinese AI organizations for the near future. Openly sharing artifacts from models to papers to deployment infrastructure maps to a strategy with the goal of large-scale deployment and integration.  — Read More

Observers often fixate on the most visible layer of China’s tech stack: consumer-facing conveniences like mobile payments, fifteen-minute food delivery, and dockless bikes. These can make for good investments — we regularly cover them at Tech Buzz China — but they are primarily business model innovations, increasingly familiar, and replicable with modest effort. In my opinion, they do not represent China’s true advantages, the ones that resist replication.

What proves far harder to replicate, and far more consequential, is the invisible layer: China’s manufacturing base. This is the part of the ecosystem that actually reshapes global supply chains, yet it remains the part most visitors never see and, in many cases, never think to see. — Read More