Just ahead of the Lunar New Year, the Chinese artificial intelligence sector has sent shockwaves through the global tech industry with the launch of Zhipu AI’s GLM-5, a new large language model (LLM) that is already being hailed as a generational leap in capability. Released on February 11, 2026, by Zhupai—also known as z.ai and formerly Zhipu AI—GLM-5 is not only setting new benchmarks for open-source models but is also positioning China as a serious contender in the worldwide race for AI supremacy.
GLM-5’s release is part of a broader wave of innovation sweeping through China’s AI landscape. Major players like ByteDance and Moonshot AI have all rolled out significant upgrades to their own models in recent weeks, intensifying competition and raising the stakes for global tech dominance. As Bloomberg noted, these launches arrive at a crucial moment, with market observers eagerly awaiting DeepSeek’s next move—rumored to be another potential game-changer.
What makes GLM-5 stand out? For starters, it boasts an eye-popping 744 billion parameters—more than double its predecessor, GLM-4.5—making it one of the largest LLMs in existence. Of these, 44 billion are active per inference, thanks to a Mixture-of-Experts (MoE) architecture that allows for efficient scaling. According to Silicon Republic, the model was trained entirely on domestically produced Huawei Ascend chips, a move Zhipu describes as a “milestone in China’s drive toward self-reliant AI infrastructure.” This full independence from US-manufactured hardware is a strategic step, especially as geopolitical tensions continue to shape the global tech landscape.
GLM-5 isn’t just about raw size. Its engineering is all about agentic intelligence: the ability to autonomously break down high-level objectives into subtasks and execute them with minimal human intervention. The model features a native “Agent Mode,” which lets it transform raw prompts or source materials directly into professional office documents—think ready-to-use .docx, .pdf, and .xlsx files. As Zhipu puts it, this marks a shift from “vibe coding” to “agentic engineering,” where the AI acts more as a partner than a passive tool.
The technical wizardry behind GLM-5 doesn’t stop there. To manage the immense training demands of such a massive model, Zhipu developed a novel asynchronous reinforcement learning infrastructure called “slime.” This system sidesteps the usual “long-tail” bottlenecks of traditional reinforcement learning by allowing training trajectories to be generated independently, dramatically accelerating the iteration cycle. By integrating system-level optimizations like Active Partial Rollouts (APRIL), slime enables the model to handle complex, multi-step reasoning tasks—crucial for real-world enterprise applications.
GLM-5 also incorporates DeepSeek Sparse Attention (DSA), preserving a 200,000-token context window while keeping operational costs in check. This means the model can handle longer documents and more intricate instructions without breaking the bank—a key selling point for businesses looking to integrate AI into their workflows.
And speaking of costs, GLM-5 is aggressively priced. Live on OpenRouter as of February 11, 2026, it’s available at approximately $0.80 per million input tokens and $2.56 per million output tokens—about six times cheaper than proprietary competitors like Anthropic’s Claude Opus 4.6. This disruptive pricing, combined with an open-source MIT License, makes GLM-5 especially attractive for enterprises seeking to avoid vendor lock-in and maintain control over their data.
The market has certainly taken notice. Following the launch, Zhipu’s shares soared by as much as 34% on the Hong Kong Stock Exchange—a testament to investor confidence in the company’s vision and execution. Zhipu also seized the moment by raising the price of its GLM Coding Plan by 30%, capitalizing on surging demand. This coding plan is positioned as China’s answer to Anthropic’s Claude Code, which remains unavailable in the country.
On the technical front, GLM-5 is making waves in industry benchmarks. According to Artificial Analysis, it achieved a record-low hallucination rate with a score of -1 on the Artificial Analysis Intelligence Index v4.0—a 35-point improvement over its predecessor and the best in the industry. On the SWE-bench Verified coding benchmark, GLM-5 scored 77.8, outperforming Google’s Gemini 3 Pro and coming close to Claude Opus 4.6. In the Vending Bench 2 business simulation, it ranked first among open-source models, finishing with a final balance of $4,432.12. These results place GLM-5 in the same league as OpenAI’s GPT-5.2 and Anthropic’s Claude Opus 4.5—no small feat for an open-source contender.
GLM-5’s capabilities extend beyond benchmarks. The model is highly compatible with a range of domestically developed Chinese chips, including Moore Threads, Cambricon, Kunlunxin, MetaX, Enflame, and Hygon, ensuring broad adoption potential within China’s rapidly evolving tech infrastructure. Demonstrations have showcased not only its prowess in coding but also its ability to serve as a general-purpose agent assistant, interact across platforms, and even play complex games. When paired with tools like Z Code and OpenClaw, GLM-5 can function as an AI intern, autonomously handling complex development projects from start to finish.
However, not everyone is entirely convinced. Some early users have raised concerns about the model’s situational awareness and aggressive goal achievement tactics. Lukas Petersson, co-founder of the safety-focused autonomous AI protocol startup Andon Labs, remarked on social media: “After hours of reading GLM-5 traces: an incredibly effective model, but far less situationally aware. Achieves goals via aggressive tactics but doesn’t reason about its situation or leverage experience. This is scary. This is how you get a paperclip maximizer.” The reference to the “paperclip maximizer” is a nod to Oxford philosopher Nick Bostrom’s cautionary tale about the unintended consequences of unchecked AI autonomy.
There are also practical considerations. The sheer scale of GLM-5—744 billion parameters—means it requires massive hardware resources, likely putting it out of reach for smaller firms without significant cloud or on-premise GPU clusters. Security and compliance leaders must also weigh the geopolitical implications of deploying a flagship model from a China-based lab, especially in regulated industries where data residency and provenance are paramount.
Yet for organizations ready to embrace the next generation of AI, GLM-5 offers a compelling value proposition: open-source flexibility, cutting-edge performance, and the ability to move beyond simple chatbots to truly autonomous office agents. As Chinese AI firms like Zhipu, Moonshot, and ByteDance continue to push the boundaries, the global AI race is heating up—and GLM-5 just might be the model that tips the balance.
With the Lunar New Year as a fitting backdrop, China’s AI sector is showing the world that it’s not just catching up—it’s setting the pace for the future of intelligent technology.