Zhipu AI, a publicly traded Chinese artificial intelligence firm, has unveiled its latest generation large language model, GLM-5, marking a significant milestone in the global AI landscape. This new model, featuring an impressive 745 billion parameters, represents not just a technical achievement but a strategic statement about technological independence.
The Beijing-based company, which originated as a spinoff from the prestigious Tsinghua University in 2019, has designed GLM-5 to tackle sophisticated tasks across multiple domains. The model employs a Mixture-of-Experts (MoE) architecture, a sophisticated approach that activates only a subset of its total parameters during each operation. While the model contains 745 billion parameters in total, it intelligently engages just 44 billion parameters per inference, utilizing 8 out of 256 available expert modules for each token processed. This design choice optimizes computational efficiency while maintaining high performance levels.
GLM-5 demonstrates particular strength in three critical areas: complex programming tasks, advanced logical reasoning, and autonomous agent systems. The model can handle extensive context windows of up to 200,000 tokens, enabling it to process and analyze long documents, codebases, or conversation histories with remarkable coherence. To manage these lengthy sequences efficiently, Zhipu AI has implemented the DeepSeek Sparse Attention mechanism, which reduces computational overhead while preserving model quality.
This release follows the company's established pattern of open-source availability. Zhipu AI has built a reputation for democratizing access to advanced AI models, with its current flagship GLM-4.7 already freely available on Hugging Face for commercial applications. The company plans to continue this tradition by releasing GLM-5 under the permissive MIT license, which allows unrestricted commercial use, modification, and distribution. This licensing strategy positions GLM-5 as a viable alternative to more restrictive proprietary models from Western tech giants.
Developers can already access the model weights through popular repositories like Hugging Face and ModelScope. For practical implementation, GLM-5 supports established inference frameworks including vLLM and SGLang, facilitating local deployment and customization. Beyond self-hosting, Zhipu AI offers the model through its API platforms at api.z.ai and BigModel.cn, providing flexible access options for different user needs.
One of GLM-5's most compelling advantages lies in its cost structure. The pricing for previous GLM-4.x models stands at approximately $0.11 per million tokens, dramatically undercutting competitors like GPT-5, which charges $1.25 per million input tokens and $10 per million output tokens. This tenfold price difference makes GLM-5 an attractive option for startups, researchers, and enterprises seeking to integrate advanced AI capabilities without prohibitive costs. Zhipu AI expects to maintain or even improve this pricing advantage with GLM-5.
However, the most strategically significant aspect of GLM-5 extends beyond its technical specifications or pricing. The model was trained exclusively on Huawei Ascend chips, completely bypassing NVIDIA hardware that dominates the global AI training landscape. This achievement represents a breakthrough in China's quest for technological self-reliance, demonstrating that world-class AI models can be developed using domestic semiconductor technology.
The development was powered by the MindSpore framework, Huawei's open-source AI computing framework, creating a fully Chinese technology stack from hardware to software. This independence from US-controlled technology components provides Zhipu AI and its domestic users with greater security and stability amid ongoing geopolitical tensions and export restrictions.
Zhipu AI's commitment to hardware diversity extends beyond Huawei's ecosystem. The company has optimized GLM-5 to run on various Chinese-made AI chips, including Moore Threads, Cambricon, Kunlun Chip, MetaX, Enflame, and Hygon. Through careful kernel optimization and model quantization techniques, the model achieves respectable throughput across these different platforms, fostering a broader domestic AI hardware ecosystem.
The financial foundation for this ambitious project was strengthened by Zhipu AI's successful initial public offering on the Hong Kong Stock Exchange on January 8, 2026. The company raised approximately 4.35 billion HKD ($558 million USD), providing crucial capital to accelerate GLM-5's development and fund research into next-generation AI architectures. This public market validation underscores investor confidence in China's AI capabilities and Zhipu AI's strategic vision.
According to preliminary benchmarks shared by Zhipu AI, GLM-5 achieves performance levels comparable to leading Western models across standard evaluation metrics. While independent verification remains pending, the company's claims suggest the model excels in Chinese language understanding and generation, coding tasks in multiple programming languages, and mathematical problem-solving.
The release of GLM-5 carries broader implications for the global AI industry. It challenges the narrative that cutting-edge AI development requires NVIDIA's ecosystem, potentially inspiring other nations and companies to invest in alternative hardware solutions. The model's open-source nature further accelerates this democratization, allowing developers worldwide to experiment with and build upon a non-NVIDIA-optimized architecture.
For the Chinese technology sector, GLM-5 represents more than just another AI model. It symbolizes the maturation of a complete, self-sufficient AI pipeline—from chip design and manufacturing to model development and deployment. This vertical integration could prove crucial as competition in AI intensifies and access to advanced Western technology becomes increasingly restricted.
Zhipu AI's approach also highlights a different philosophical stance on AI development. While many Western companies increasingly gatekeep their most capable models behind APIs and restrictive licenses, Zhipu AI embraces openness. This strategy could help the company build a global developer community and establish its technology as a foundational standard, particularly in regions seeking alternatives to US-dominated AI infrastructure.
Looking ahead, the success of GLM-5 will likely influence China's broader semiconductor strategy. If the model gains widespread adoption and demonstrates competitive performance, it could validate massive investments in domestic chip manufacturing and encourage further development of specialized AI accelerators. Conversely, any performance gaps could highlight areas where Chinese chip technology still needs to catch up.
The model's availability through multiple channels—open-source weights, API access, and support for diverse hardware—demonstrates Zhipu AI's understanding of different market needs. Researchers can download and study the model, startups can integrate it via affordable APIs, and large enterprises can deploy it on-premises using their preferred hardware platform.
In conclusion, GLM-5 emerges as a multifaceted milestone: a technically sophisticated large language model, a cost-effective alternative to Western offerings, and a strategic demonstration of China's AI independence. Its development on Huawei chips, combined with an open-source release strategy, positions it as a potential catalyst for reshaping the global AI landscape. As developers begin experimenting with the model and independent benchmarks emerge, the true impact of this China-trained, China-built AI system will become clearer. For now, it stands as a powerful statement that the future of AI may be more diverse and distributed than previously imagined.