Z.ai, Formerly Zhipu AI: The Rise of an AI Tiger Reaching for AGI

Zhipu AI rebranded internationally as Z.ai in July 2025. In this article, we use Z.ai for the current company name and Zhipu AI when referring to its earlier history, older model releases, or sources that used the original name.

When we first covered it, the company was known as a Tsinghua-born generative AI unicorn valued at roughly $2.5B–$3B. Since then, the story has changed a lot: Z.ai has expanded its GLM model family, released more open-weight models, faced U.S. sanctions, prepared for a Hong Kong IPO, and become one of the clearest examples of China’s push to build independent AI infrastructure.

Occasionally, we will return and update our series about GenAI Unicorns. Today, for the first time, we examine a Chinese startup. Though Z.ai (Zhipu AI) is not a new company, recently it has started to pop up in the news more often due to its consistent model deliveries and updates, varied partnerships, everyone's belief that they are an OpenAI rival, and the fact that it recently became Generative AI unicorn with $2.5 billion valuation. The story of Z.ai – an academic startup and one of the four new AI tigers of China (along with Moonshot AI, Baichuan, and MiniMax) – is a fascinating examination of how things are done in China, and also about a special kind of startup started at the university and led by PhDs and professors.

Z.ai’s CEO is sure that by the end of the year, they will be on par with GPT-4 and very close to AGI. However true this is and whatever Z.ai offers, let’s learn together.

In today’s episode

In 2019, Zhipu AI (this is how Z.ai was called firstly) was established at Tsinghua University Science Park in Beijing, known as the "Center of the Universe" for tech startups. Founders Tang Jie and Li Juanzi, both professors at Tsinghua University's Department of Computer Science and Technology and active participants in the university's Knowledge Engineering Group (KEG), initially focused the company on building knowledge graphs to enhance research and innovation.

The early days of Zhipu in 2019 were challenging; as one of many academic startups without a clear business model, they struggled to secure initial investment. The administrative commission of Zhongguancun Science Park provided the team with rent-free office space for three months, which helped kickstart their development. But, in 2020 – before many others – in addition to their Knowledge Atlas business, they started to invest and develop the first large-model AI technologies, recognizing their potential. By September 2021, the company's shift in focus had paid off significantly with a major funding round, raising nearly $15 million from local venture capitalists.

The company's ethos is both academic and technologically idealistic. According to Zhang Peng, CEO of Zhipu and an alumnus of Tsinghua University, there is an internal saying: "No matter how much money we raise or how much money we make, it will be a hindrance on our road to AGI."

Currently, the company has more than 800 people, about 60 to 70% are related to research and development (R&D).

Zhang Peng is the CEO of Z.ai, formerly Zhipu AI. He is a Tsinghua University alumnus and has been one of the company’s most visible public voices, connecting Z.ai’s commercial strategy with its long-term AGI narrative.

Public profiles describe Zhang as being in his mid-40s and as a practical AI specialist with experience in large-scale pretrained model deployment, knowledge graphs, and enterprise AI applications. That background fits Z.ai’s origins: the company began from Tsinghua’s knowledge-engineering ecosystem before shifting more aggressively into large language models.

Zhang’s role is important because Z.ai is not only a research lab. It is trying to commercialize foundation models through MaaS, enterprise deployments, open-source developer adoption, and international expansion. His AGI language gives the company a grand mission, but its business depends on something more practical: turning GLM models into usable products for developers, companies, and institutions.

The biggest update is the name. Zhipu AI rebranded internationally as Z.ai in July 2025, especially around the release of GLM-4.5 and GLM-4.5-Air. The rebrand makes the company easier to position globally while keeping the GLM model family at the center of the story.

The second update is geopolitical. In January 2025, the U.S. Commerce Department added Z.ai and related entities to the Entity List, citing national security and foreign-policy concerns. This restricted the company’s access to some U.S. technologies and made compute strategy even more important. Z.ai’s response has been to lean harder into open models, domestic infrastructure, Chinese chip compatibility, and overseas expansion outside the U.S. market.

The third important shift is financial. In January 2026, Zhipu completed its H-share listing in Hong Kong. Then, in a July 2026 internal letter attributed to founder Tang Jie, the company framed the listing not as a finish line, but as a reset: a way to return fully to foundational model research and invest in the next generation of AGI-oriented systems.

For Z.ai, public-market financing is not only about growth. It is also about funding compute, research, enterprise adoption, open-source model releases, and the next generation of GLM models.

Together, these updates change the whole framing. Z.ai is much more than just a promising Chinese AI unicorn. It is becoming a test case for whether China’s leading AI startups can keep scaling under export controls, compete with domestic rivals like DeepSeek, Moonshot AI, MiniMax, and Baichuan, and build a global developer ecosystem around open-weight models.

Zhang Peng, CEO of Zhipu, said that “2024 will be the first year of AGI.” Our slogan is, he said in another interview, "Let machines think like humans", which is AGI.” And he thinks that these goals is what unites them with OpenAI (OpenAI recently switched to the term Superintelegence).

“Today, I feel that Z.ai is undergoing a qualitative change from quantitative change, especially in terms of the emergence of large models. Standing at this juncture, whether looking forward or backward, I feel that our team is quite fortunate. The market has given us, hard workers, many opportunities and chances. Although the road of independent innovation and research and development will be difficult, we are still persevering. In addition, the development of AI will focus more on general artificial intelligence (AGI), achieving super cognitive intelligence beyond human level, realizing AI’s self-explanation, self-assessment, and self-supervision. At the same time, to ensure that the model’s performance aligns with human values and safety standards, a superalignment technology is under development with the goal of achieving machine automatic alignment with human intelligence and human values to enable model self-reflection and control.” Zhang Peng said.

The direction toward AGI is the company's long-term mission. But in July 2026, Z.ai outlined a more concrete near-term strategy called the "Touch High" initiative. Over the next two years, the company plans to invest heavily in the next frontier of AGI through four core engines:

This makes it much clearer what we should expect from Z.ai in the near future. Here's what they already have today. →

The now famous ChatGLM conversational model open-sourced in 2023 won’t be possible without the General Language Model (GLM) model family forming its foundation. The GLM takes us back to 2021.

Like many other startups in China, Zhipu has emerged from and still is in tight connection with the University. Tsinghua KEG initiated the GLM in December 2021 to create a highly accurate, bilingual language model for both Chinese and English. Unlike earlier models like GPT-3, GLM was engineered to perform robustly on single-server systems equipped with suitable GPUs, making advanced natural language processing (NLP) capabilities more accessible globally.

GLM's journey began with the ambitious goal of creating a 100-billion-parameter bilingual model. Immediately, they ran into a bunch of problems:

Despite all these and with the aid of the Tsinghua PACMAN team (which focuses on parallel architecture and compiler technology), the project overcame hardware failures and software bugs during its early phases. Collaborative efforts with various tech platforms related to the university enabled the effective operation of pre-training algorithms across multiple systems.

By April 2022, GLM's training scaled across 96 A100 servers. It was featured at ACL 2022 under the paper titled “GLM: General Language Model Pretraining with Autoregressive Blank Infilling.” The model was open-sourced.

As with many other NLP models, GLM employs a Transformer-based architecture. The Transformer uses masked self-attention layers that allow it to consider the full context of the input data but GLM integrates innovative features:

These technical advancements enable GLM to excel in tasks that include natural language understanding and text generation, often outperforming traditional models like BERT and GPT with fewer resources.

In October 2023, the GLM model family saw an important update with the release of GLM-130B. This bilingual (English and Chinese) pre-trained language model, featuring 130 billion parameters, matches GPT-3 in size and capabilities but extends its accessibility and functionality through open-source availability and enhanced bilingual support. The model was prominently showcased in a paper “GLM-130B: An Open Bilingual Pre-trained Model” at ICLR 2023.

Key features and capabilities

This model was the sole entry from Asia to be recognized in Stanford's global evaluation in 2022.

The company has demonstrated significant progress in its ChatGLM series, a line of Chinese-English bilingual dialogue models based on the GLM architecture. Each generation of ChatGLM has built on the previous, refining and extending capabilities to better meet user needs and technological advancements.

Launched in March 2023, the ChatGLM-6B model incorporates advanced quantization technology and is designed to run efficiently on consumer-grade graphics cards with a minimum of 6GB video memory. This model, a derivative of the open-sourced GLM-130B, supports robust bilingual capabilities through training on approximately 1 trillion tokens in both Chinese and English. It is optimized for nuanced conversations, featuring techniques such as supervised fine-tuning and RLHF. The model is fully open for academic research and commercial use, provided users complete a registration questionnaire. You can try the online demo on Hugging Face Spaces.

Later, launched Zhipu Qingyan, an assistant-based ChatGLM2.

Released n June 2023, ChatGLM2-6B retains the user-friendly features of its predecessor while introducing significant enhancements:

In collaboration with Tsinghua University’s KEG Laboratory, Z.ai developed ChatGLM3, further enhancing the model's functionality:

The developers claim that each version of ChatGLM has been designed with ethical and safe usage in mind. They also notify that despite efforts to ensure data accuracy, the stochastic nature of these models means that output reliability cannot be guaranteed, and they are susceptible to being misled by user input.

The team continues to refine its models. On January 16, 2024, Z.ai unveiled GLM-4, their new large language model. It approaches the capabilities of OpenAI's GPT-4, featuring enhanced support for longer contexts, stronger multimodal interactions, and accelerated inference speeds. This advancement significantly reduces computational costs while increasing the system's ability to handle multiple requests concurrently.

GLM-4 is designed as a multilingual model, adept at Q&A, multi-turn dialogues, and code generation. It excels in extended contextual understanding and enables the creation of personalized intelligent agents, simplifying the use of sophisticated AI technologies. These enhancements bolster GLM-4's utility across diverse applications, making it a robust tool for both developers and end-users.

Performance metrics for GLM-4 are impressive, with achievements in benchmark datasets like MMLU, GSM8K, MATH, BBH, HellaSwag, and HumanEval, where it closely matches or reaches GPT-4 levels. Additionally, GLM-4 shows remarkable proficiency in following instructions, with high compliance in both Chinese and English evaluations.

The model also demonstrates superior alignment capabilities, particularly in Chinese, suggesting an optimized understanding of nuanced linguistic elements. In long text assessments, GLM-4 surpasses Claude 2.1, and in the challenging "needle in the haystack" scenario, it maintains a perfect recall rate over lengthy spans.

In terms of multimodal capabilities, preliminary tests indicate that CogView3, a component of GLM-4, substantially outperforms DALLE3 in various metrics, underscoring GLM-4’s capacity to integrate and interpret visual and textual data effectively.

This combination of advancements solidifies GLM-4 as a highly potent development in the field of large language models.

UPDATE from Sept 9, 2024:

Z.ai has introduced a real-time video call feature for its chatbot, ChatGLM, allowing users to engage in live video conversations with AI. Launched at KDD 2024, the feature will be available to select users from August 30, with wider release plans. The AI accurately identifies objects and provides lifelike interactions, but occasionally misidentifies characters. Alongside this, Z.ai has upgraded its core models, including GLM-4-Plus, rivaling top models like GPT-4o and Llama3.1.

Today Z.ai has moved well beyond the old ChatGLM chatbot format. GLM-4.5 and GLM-4.5-Air are open-source MoE models built mainly for agents, reasoning, and coding. The larger GLM-4.5 has 355B parameters in total, with 32B active, and performs strongly on TAU-Bench, AIME, and SWE-bench Verified.

For multimodal tasks, Z.ai introduced GLM-4.5V and GLM-4.6V that can work with images, video, interfaces, and long documents. They are useful for GUI agents, visual reasoning, coding, and document analysis. GLM-4.6V also supports a 128K context window.

Then came GLM-4.7 and GLM-4.7-Flash, with a more practical feature: reasoning can be turned on for difficult tasks and switched off when it is not needed.

Together, all of these model versions are building a broader open model family for agents, coding, reasoning, multimodal work and a path to advanced intelligence.

After GLM-4.7, Z.ai moved into the GLM-5 line, that is a transition from general reasoning and coding upgrades to long-horizon agentic engineering. GLM-5 for complex systems engineering, coding, and long-running agent tasks scaled the model family to 744B total parameters with 40B active parameters. GLM-5.1 then improved the same direction with stronger coding, repo generation, terminal tasks, and better performance over long multi-step sessions.

In June 2026, Z.ai introduced GLM-5.2 which is its most capable open-source model to date. It is a flagship model for long-horizon tasks with a solid 1M-token context window.

The most interesting things about this model are big architectural changes. First of all, it is IndexShare techniques, which lets the model reuse the same sparse-attention index across multiple transformer layers instead of recomputing it every time. That decreases long-context computation by nearly 3×, and that is what makes a 1M-token context genuinely usable. Z.ai also redesigned speculative decoding, increasing accepted draft lengths by up to 20%, and introduced flexible effort levels which lets developers trade latency for stronger reasoning when needed.

ChatGLM-Math: Improving Math Problem-Solving in LLMs with a Self-Critique Pipeline

The company has launched an ambitious initiative to support the open-source AI community and energize local R&D and commercialization efforts in the LLM field. The company has established several support mechanisms, including:

Its business model is centered around Model as a Service (MaaS), targeting the business-to-business (B2B) market. This approach focuses on selling AI-driven solutions and services directly to other businesses, leveraging their willingness to invest in advanced AI technologies. CEO Zhang Peng initially estimated a break-even point by 2026 or 2027, acknowledging that this timeline may need adjustment due to rapid changes in the market, such as rising computing costs and increasing competition.

Just a few recent partnerships:

Academic roots can be a powerful launchpad (as we’ve also seen in the story of Databricks). The rise of Z.ai from an academic project to an AI unicorn in just a few years is a testament to the unique dynamics and potential of China's AI startup ecosystem. Born out of Tsinghua University, the company leveraged its academic roots to attract top talent, forge key partnerships, and pioneer an open-source approach that has rapidly built a developer community and spurred adoption. Z.ai's idealistic mission to reach AGI, diverse industry partnerships, and B2B focus provide a unifying force and multiple paths to monetization. In 2026, that AGI mission became more concrete, with Z.ai organizing its roadmap around four engines: long-horizon task capability, autonomous agent systems, fully self-training AI, and safety governance. This also clarifies how Z.ai differs from many Western frontier labs like OpenAI: it is pursuing AGI through open models, long-context systems, agentic workflows, and domestic infrastructure shaped by Chinese industrial and governance priorities. As Z.ai's string of cutting-edge model releases demonstrates, China's AI startups are emerging as serious global contenders. It’s important to closely track the AI pioneers emerging from China's universities. More AI tigers are coming.

Z.ai, formerly Zhipu AI, offers some free access through its consumer products, demos, and open model releases, but its full commercial offering is not simply free. Developers and companies usually access its models through APIs, enterprise services, or paid deployments, depending on the use case.

There is no single “best” Chinese AI company or model. DeepSeek is known for efficient reasoning and coding models, Z.ai for the GLM family and open-weight strategy, Moonshot AI for long-context models, MiniMax for consumer and multimodal products, and Alibaba Qwen for a broad open-model ecosystem. The best choice depends on whether you need reasoning, coding, Chinese-language performance, multimodality, enterprise deployment, or open weights.

Partly. Z.ai has released several GLM-family models and related projects as open source or open weight, including models in the ChatGLM / GLM ecosystem. However, not every Z.ai model, product, dataset, or enterprise system is open source. The safest way to describe the company is: Z.ai has a strong open-model strategy, but it is not a fully open-source company.

When this article was first written, Zhipu AI was widely reported as a Chinese generative AI unicorn valued around $2.5B–$3B. Since then, the company has rebranded internationally as Z.ai and completed its H-share listing in Hong Kong in January 2026. The most accurate current valuation should now be tied to the latest public-market filings and trading data, not older private unicorn estimates.