# Zhipu AI

> Source: https://aiwiki.ai/wiki/zhipu_ai
> Updated: 2026-06-21
> Categories: AI Companies, Chinese AI, Large Language Models
> From AI Wiki (https://aiwiki.ai), a free encyclopedia of artificial intelligence. Quote with attribution.

Zhipu AI (智谱AI), now branded internationally as [Z.ai](/wiki/z_ai), is a Chinese [artificial intelligence](/wiki/artificial_intelligence) company headquartered in Beijing. Founded on June 11, 2019, the company originated as a spin-off from the Knowledge Engineering Group (KEG) at [Tsinghua University](/wiki/tsinghua_university).[4] Zhipu develops the GLM (General Language Model) family of [large language models](/wiki/large_language_model), multimodal models, and AI agent systems. In January 2026, Zhipu became the first Chinese AI foundation model company to go public, listing on the Hong Kong Stock Exchange under the ticker 02513 and the legal entity name Knowledge [Atlas](/wiki/atlas_robot) Technology.[6][7]

Often described as "China's [OpenAI](/wiki/openai)," Zhipu is recognized as one of China's "Six Little Tigers" (六小虎), a group of prominent AI startups that emerged during the generative AI wave.[10] By the time of its IPO, the company had raised approximately $1.5 billion in total funding from investors including [Alibaba](/wiki/alibaba_cloud), Tencent, Meituan, Xiaomi, HongShan (formerly Sequoia China), and Saudi Arabia's Prosperity7 Ventures.[7] The shares debuted at HK$116.20 in January 2026 and, on May 28, 2026, briefly touched HK$1,993 intraday, valuing the company at more than HK$880 billion (about $112 billion), nearly 1,600% above the IPO price, before falling back.[36] In June 2026 Zhipu's board approved a plan for a secondary listing on Shanghai's STAR Market, and the company released GLM-5.2, an open-weight model with a 1-million-token context window.[37][38][41]

## History

### Founding and Early Years (2019-2021)

Zhipu AI was established on June 11, 2019, by Tang Jie and Li Juanzi, both professors in the Department of Computer Science and Technology at Tsinghua University.[4] The company grew out of the university's Knowledge Engineering Group (KEG), a research lab focused on [knowledge graphs](/wiki/knowledge_graph), [natural language processing](/wiki/natural_language_processing), and social network analysis.[4] Tang Jie, a Fellow of the ACM, IEEE, and AAAI, had led the KEG for years and mentored a generation of Chinese AI researchers, including several founders of rival AI startups.[14] Tang also serves as vice director of the Beijing Academy of Artificial Intelligence (BAAI), an institution that played a role in early Chinese large language model research.

The company was initially located in the Tsinghua University Science Park in Beijing's Zhongguancun district, a technology hub often compared to Silicon Valley. In its earliest days, Zhipu struggled to secure funding as one of many academic spin-offs without a proven business model.[14] The administrative commission of Zhongguancun Science Park provided the founding team with rent-free office space for three months, giving them room to develop their initial products.[14] The company's first commercial focus was building knowledge graphs to support enterprise research and innovation workflows.

In 2020, even before the wider large language model boom triggered by the success of [GPT-3](/wiki/gpt-3), Tang Jie and his team began investing in large-model AI research alongside their existing knowledge graph business.[14] At a time when most Chinese AI companies were focused on computer vision or speech recognition, this early bet on foundation model technology would prove to be a defining strategic decision that set Zhipu apart from its peers.

During this period, the team also published CogView, a 4-billion-parameter [Transformer](/wiki/transformer)-based text-to-image model, presented at [NeurIPS](/wiki/neurips) 2021.[3] CogView used a VQ-VAE tokenizer and achieved state-of-the-art FID scores on the blurred MS [COCO dataset](/wiki/coco_dataset), outperforming prior GAN-based approaches.[3] It was among the first Chinese models to compete with OpenAI's [DALL-E](/wiki/dall-e) in image generation from text descriptions.

### GLM-130B and International Recognition (2022)

In August 2022, Zhipu and Tsinghua's KEG released GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.[1] This made Zhipu the developer of China's first self-developed pre-trained language model of this scale. The model was published as a paper that was accepted at ICLR 2023 ("GLM-130B: An Open Bilingual Pre-trained Model").[1] GLM-130B used the General Language Model framework, which combines autoregressive blank infilling with bidirectional attention, a unique approach distinct from both the GPT-style autoregressive paradigm and the [BERT](/wiki/bert)-style masked language modeling approach.[1]

GLM-130B was trained on approximately 400 billion tokens (roughly 200 billion each in English and Chinese) and featured 70 Transformer layers with a hidden dimension of 12,288 and a bilingual tokenizer containing 150,000 tokens.[1] The pre-training objective consisted of 95% self-supervised training using autoregressive blank infilling over the public Pile corpus for English and several Chinese corpora, plus 5% multi-task instruction pre-training across 70 different datasets.[1]

On benchmarks, GLM-130B outperformed GPT-3 175B on LAMBADA (+5.0%), OPT-175B (+6.5%), and [BLOOM](/wiki/bloom)-176B (+13.0%).[1] It performed comparably to GPT-3 175B on MMLU (+0.9%). For Chinese-language tasks, it significantly exceeded ERNIE TITAN 3.0 260B on zero-shot CLUE datasets (+24.26%) and FewCLUE datasets (+12.75%).[1] The model could run inference on a single server with 8 A100 (40GB) or 8 V100 (32GB) GPUs, making it relatively accessible for a model of its size.[1]

Also in 2022, CogView2 was released, a hierarchical Transformer-based text-to-image model that achieved a 10x speedup over the original CogView while producing competitive image quality against DALL-E 2.

These releases established Zhipu as a serious competitor in the global large language model landscape and attracted growing investor interest.

### ChatGLM and the Open-Source Surge (2023)

On March 14, 2023, Zhipu open-sourced ChatGLM-6B, a 6.2-billion-parameter conversational model derived from the GLM architecture.[2] This lightweight model was optimized for Chinese question-answering and dialogue. It was trained on approximately one trillion tokens of Chinese and English corpus, supplemented by supervised [fine-tuning](/wiki/fine_tuning), feedback bootstrap, and [reinforcement learning from human feedback](/wiki/reinforcement_learning) ([RLHF](/wiki/rlhf)).[2] The model could run on consumer-grade GPUs using INT4 quantization with as little as 6 GB of GPU memory. The release attracted massive attention in the Chinese developer community, accumulating over 10 million downloads on [Hugging Face](/wiki/hugging_face) within its first year and becoming one of the most popular open-source language models globally during 2023.[14]

ChatGLM-6B was quickly followed by ChatGLM2-6B in June 2023, an updated version with stronger benchmark performance, extended context length (from 2K to 32K tokens), and more efficient inference (a 42% speedup over the original).[2]

On October 22, 2023, Zhipu released ChatGLM3-6B, a third-generation model that natively supported function calling, code interpretation, and agent-based workflows in complex scenarios.[2] ChatGLM3 was released in multiple variants: ChatGLM3-6B (standard), ChatGLM3-6B-Base, ChatGLM3-6B-32K, and ChatGLM3-6B-128K. With the ChatGLM3 launch, Zhipu's consumer chatbot product, Zhipu Qingyan (智谱清言), became the first large model product in China with built-in code interaction capability. Also at the October 2023 China National Computer Conference, Zhipu open-sourced CogVLM-17B, a multimodal vision-language model.

Also in October 2023, Zhipu secured a major funding round of approximately $342 million with participation from Alibaba, Tencent, and other major Chinese tech firms, valuing the company at around $2.8 billion.[5] This round represented one of the largest single investments in a Chinese AI startup at the time.[5]

### GLM-4 and Rapid Expansion (2024)

In January 2024, Zhipu released GLM-4, its fourth-generation base model.[2] GLM-4 represented a 60% performance improvement over GLM-3 and achieved scores comparable to [GPT-4](/wiki/gpt-4) on general benchmarks including [MMLU](/wiki/mmlu), [GSM8K](/wiki/gsm8k), [MATH](/wiki/math), BBH, [GPQA](/wiki/gpqa), and [HumanEval](/wiki/humaneval).[2] The model supported a 128K token context window and achieved 100% accurate recall in needle-in-a-haystack testing at that context length.[2] It also scored up to 90% on the IFEval instruction-following benchmark. GLM-4 was pre-trained on over ten trillion tokens across Chinese, English, and 24 additional languages.[2]

The open-source GLM-4-9B-Chat variant offered features such as web browsing, code execution, custom tool calling (function call), and long-context reasoning for the broader developer community.[2]

In May 2024, Zhipu raised approximately $400 million in a round that included Saudi Arabia's Prosperity7 Ventures, marking the company's first foreign investor participation.[4] The round valued Zhipu at roughly $3 billion.

In June 2024, Zhipu slashed GLM-4 API prices by over 50%, joining a broader price war among Chinese AI providers that saw providers such as Alibaba (Qwen), Baidu (ERNIE), and ByteDance ([Doubao](/wiki/doubao)) aggressively cutting prices to attract developers.[4]

Later in 2024, Zhipu released GLM-4-Plus, a next-generation proprietary model with performance comparable to GPT-4o. In Chinese-language benchmarks, GLM-4-Plus ranked fourth in the SuperCLUE evaluation as of November 2024.[4] The company also launched GLM-4V, a multimodal vision-language model comparable to GPT-4V, and GLM-4-Voice, a model with real-time voice interaction capabilities that brought a "video call" feature to the Zhipu Qingyan app.[4]

In November 2024, Zhipu introduced AutoGLM, a smartphone AI agent capable of executing multi-step tasks such as ordering food delivery, booking flights, and navigating apps by interpreting on-screen content and simulating human-like taps, swipes, and text input.[4] AutoGLM could handle operation sequences exceeding 50 steps across multiple applications and supported browser-based website automation.

In December 2024, the company completed a Series D funding round of approximately $420 million, its largest single round to date.

### IPO, Z.ai Rebrand, and GLM-5 (2025-2026)

In January 2025, in the final days of the Biden administration, the U.S. Commerce Department's Bureau of Industry and Security added Zhipu's corporate parent, Beijing Zhipu Huazhang Technology, and nine affiliated companies to the [Entity List](/wiki/export_controls), stating that they had advanced China's military modernization through the development and integration of advanced AI research; the rule took effect on January 16, 2025.[15][16] Listed entities are barred from buying U.S. technology and components without special government approval. Zhipu said it strongly disagreed with the decision and that the designation would not have a substantial impact on its operations.[16]

In April 2025, Zhipu pre-filed for an initial public offering on the Hong Kong Stock Exchange, becoming the first of China's "Six Little Tigers" to pursue a public listing. Shortly after, the company open-sourced the GLM-4-0414 model series under the MIT license, including a 32-billion-parameter base model (GLM-4-32B-0414) pre-trained on 15 terabytes of high-quality data, a reasoning model (GLM-Z1-32B-0414) that used cold-start and extended [reinforcement learning](/wiki/reinforcement_learning) strategies, and a rumination model (GLM-Z1-Rumination-32B-0414) designed for multi-step deliberation on complex problems.[8] The reasoning model, with only 32 billion parameters, achieved performance on certain tasks comparable to [DeepSeek](/wiki/deepseek)-R1, which has 671 billion parameters.[8]

In July 2025, the company released GLM-4.5 and GLM-4.5 Air and formally rebranded its international identity as Z.ai while retaining the Zhipu AI (智谱AI) name in the Chinese market. Usage of the subscription service grew tenfold in the two months following the GLM-4.5 launch.

The [GLM-4.5](/wiki/glm_4_5) weights, released on July 28, 2025, were published on Hugging Face under the MIT license: the flagship used a mixture-of-experts design with 355 billion total parameters and 32 billion active per token, while GLM-4.5 Air offered 106 billion total and 12 billion active parameters.[20][21] Both models implemented hybrid "thinking" and "non-thinking" response modes with a 128K context window, and Zhipu reported a third-place aggregate score, across both open and proprietary models, on its composite of 12 industry benchmarks.[20][21] The accompanying technical report, "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models," positioned the series around agentic applications.[20] In August 2025, the company followed with GLM-4.5V, an open vision-language model built on the GLM-4.5 Air architecture that reported state-of-the-art results among similarly sized open models across 41 public multimodal benchmarks.[22]

Throughout 2025, Zhipu also received several rounds of government-backed investment. In March 2025, the company raised approximately $257 million from funds backed by the cities of Chengdu, Zhuhai, and Hangzhou.[17] In July 2025, Shanghai state funds contributed an additional $140 million.[19]

On September 30, 2025, Zhipu released [GLM-4.6](/wiki/glm_4_6), which extended the context window from 128K to 200K tokens and improved real-world coding, long-context processing, reasoning, search, and agentic performance.[23] On the company's extended CC-Bench evaluation, in which human evaluators ran multi-turn coding tasks in isolated Docker environments, GLM-4.6 reached near parity with Anthropic's [Claude](/wiki/claude) Sonnet 4 (a 48.6% win rate) while using about 15% fewer tokens per task than GLM-4.5, and the weights were again released openly for local deployment.[23]

In December 2025, Zhipu open-sourced the GLM-4.6V series, a pair of vision-language models with native function-calling capabilities.[27] GLM-4.6V (106B parameters) targeted cloud deployments, while GLM-4.6V-Flash (9B parameters) was designed for local, low-latency use. Both supported a 128K-token training context window, enough to process approximately 150 pages of dense documents or one hour of video in a single pass.[27]

Also in December 2025, Z.ai open-sourced GLM-4.7, a large language model built for real development workflows, including coding tasks.[28] The company also released CogKit, a fine-tuning and inference framework for the CogView4 and [CogVideoX](/wiki/cogvideo) model families.[12] GLM-4.7, released on December 22, 2025, scored 73.8% on [SWE-bench Verified](/wiki/swe_bench_verified), the highest result among open-source models at the time, and 84.9% on LiveCodeBench, and it ranked first among open-source and Chinese models in the Code Arena blind-testing leaderboard.[28][29]

On January 8, 2026, Zhipu listed on the Hong Kong Stock Exchange under the legal name Knowledge Atlas Technology (知识图谱科技).[7][9] The IPO raised approximately HK$4.35 billion (roughly $558 million) at a share price of HK$116.20, valuing the company at about $6.5 billion.[6][7] On its first day of trading, shares surged as high as HK$130 (an 11.88% gain), pushing the market capitalization above HK$57 billion ($7.4 billion).[7] The Hong Kong public offering was oversubscribed 1,159 times, while the international offering saw 15.28 times oversubscription.[6] Zhipu became both the first Chinese AI software company to go public and the world's first publicly traded company focused specifically on AGI foundation models.[9]

In February 2026, Zhipu released GLM-5, its flagship model featuring approximately 744 billion parameters in a [Mixture of Experts](/wiki/mixture_of_experts) (MoE) architecture with 256 experts and 40 billion active parameters per inference token.[31] GLM-5 uses DeepSeek Sparse [Attention](/wiki/attention) for efficient long-context handling and supports a 200K-token context window.[30][31] The model was trained entirely on 100,000 domestically produced Huawei Ascend 910B chips using the MindSpore framework, a notable milestone for Chinese AI self-sufficiency given ongoing U.S. semiconductor export restrictions on [NVIDIA](/wiki/nvidia) GPUs.[31][32] GLM-5 was released under the MIT license.[31]

[GLM-5](/wiki/glm_5) was released on February 11, 2026, accompanied by a technical report titled "GLM-5: from Vibe Coding to Agentic Engineering," which describes pre-training on 28.5 trillion tokens (up from 23 trillion for GLM-4.5) and an asynchronous reinforcement learning infrastructure used in post-training.[30][31] At release, GLM-5 scored 77.8% on SWE-bench Verified, the highest result among open-weight models at the time and within roughly three points of GPT-5.2 and Claude Opus 4.5, and it ranked first among open models on the [LMArena](/wiki/lmarena) text leaderboard.[31][32] Zhipu's shares rose 34% on the day of the release, and by mid-February 2026 the stock had gained more than 500% from its IPO price, lifting the market capitalization past $40 billion.[31][32]

Zhipu's first annual results as a listed company, covering fiscal 2025 and released at the end of March 2026, showed revenue of RMB 724 million, up 132% year over year, alongside a net loss that widened to RMB 4.72 billion; the shares nonetheless rose 32% to HK$915 on April 1, 2026, after the results were published.[33] After the results, CEO Zhang Peng said Zhipu would continue pursuing the path of being "China's Anthropic," with API revenue serving as the foundation for sustained, long-term expansion.[33]

On April 8, 2026, Zhipu released and open-sourced [GLM-5.1](/wiki/glm_5_1), which it described as its most intelligent flagship model to date, with stronger programming performance and the ability to work continuously on a single task for more than eight hours.[34] The company said the model ranked first among open-source and Chinese models, and third overall, on the combined average of [SWE-bench Pro](/wiki/swe_bench_pro), [Terminal-Bench](/wiki/terminal_bench), and NL2Repo.[34] Alongside the launch, Zhipu raised cloud API prices by roughly 10% on average, its second price increase of 2026, and its shares rose nearly 19% intraday on the day of the announcement.[34] In May 2026, the company added a high-speed GLM-5.1 API tier that it said delivered output speeds of around 400 tokens per second.[35]

The model releases and surging API demand drove an extended stock rally. On May 28, 2026, the shares briefly touched HK$1,993, valuing the company at more than HK$880 billion (about $112 billion), nearly 1,600% above the IPO price.[36] The rally then partially reversed: by June 12, 2026, the shares had fallen to HK$1,097, down about 44.9% from the peak, a pullback attributed in part to looming lock-up expirations, with stock held by Zhipu's first batch of cornerstone investors (about 25.68 million shares, roughly 11.9% of H-share capital) becoming eligible for sale on July 8, 2026.[39]

### STAR Market plan and GLM-5.2 (June 2026)

On June 1, 2026, Zhipu's board unanimously approved a proposal for a second listing on Shanghai's STAR Market, seeking to raise up to RMB 15 billion (about $2.1 billion) through an A-share issuance representing between 2% and 8% of the post-issue share capital, a move that would create a dual A+H share structure.[37][38] Of the proceeds, RMB 12 billion was earmarked for foundation model research and development, RMB 2 billion for its Model-as-a-Service platform, and RMB 1 billion to supplement working capital.[37][38]

On June 13, 2026, Zhipu announced GLM-5.2, an upgrade to the GLM-5 series that quadrupled the context window to 1 million tokens while retaining the 744-billion-parameter MoE architecture with roughly 40 billion active parameters per token, and released the weights under the MIT license on Hugging Face and ModelScope.[41][42] In independent press coverage of GLM-5.2's benchmarks, the model reported 62.1 on SWE-bench Pro, ahead of OpenAI's GPT-5.5 (58.6) and the predecessor GLM-5.1 (58.4), 81.0 on Terminal-Bench 2.1, and 74.4% on FrontierSWE, within about one point of Anthropic's Claude Opus 4.8, while undercutting comparable proprietary APIs on price.[42][43] News of the launch sent the shares up as much as 48% intraday on June 15, 2026, before they closed up 32.8% at HK$1,457.[40]

#### Is GLM-5.2 open source?

Yes. Like every Zhipu flagship since the GLM-4-0414 series in April 2025, GLM-5.2 was released under the permissive MIT license, allowing unrestricted commercial use, fine-tuning, and local deployment of the weights.[8][41] Zhipu published the model on Hugging Face and ModelScope and made it accessible through its Z.ai API and third-party coding agents.[42] On the reliability of its headline context window, the company cautioned in its release notes that "a 1M context is easy to claim, but much harder to keep reliable under real engineering pressure."[42]

## Leadership

| Role | Name | Background |
|---|---|---|
| Chief Scientist and Co-founder | Tang Jie (唐杰) | Professor at [Tsinghua University](/wiki/tsinghua_university), ACM/IEEE/AAAI Fellow, Director of the Foundation Model Research Center at Tsinghua's AI Institute, vice director of BAAI, architect of the GLM model design |
| Co-founder | Li Juanzi (李涓子) | Professor at Tsinghua University's Department of Computer Science, co-lead of the Knowledge Engineering Group |
| Chairman and Co-founder | Liu Debing (刘德兵) | Former Technicolor (China) executive, oversees corporate governance and state-level alignment |
| CEO | [Zhang Peng](/wiki/zhang_peng) (张鹏) | Tsinghua University Computer Science alumnus, leads commercial strategy |
| President | Wang Shaolan (王绍兰) | Holds a doctorate, recognized as a Tsinghua Innovation Leader |

Tang Jie and Liu Debing serve as the effective controlling shareholders. Together with Li Juanzi, Zhang Peng, and other aligned stakeholders, the founding group controls approximately 36.97% of voting rights. Tang Jie directly owns 7.41% of the company.

## GLM Model Family

The GLM (General Language Model) series is Zhipu's core technology. Unlike the purely autoregressive approach used by [GPT](/wiki/gpt-4) models or the masked language modeling of [BERT](/wiki/bert), GLM uses autoregressive blank infilling: the model learns to fill in randomly blanked-out spans of text in an autoregressive manner.[1] This hybrid approach enables GLM to handle both natural language understanding and generation tasks within a single framework, providing versatility across downstream applications.[1]

### Model Timeline

| Model | Release Date | Parameters | Key Features |
|---|---|---|---|
| GLM-130B | August 2022 | 130B | Bilingual (English/Chinese), open-weight, ICLR 2023 paper |
| ChatGLM-6B | March 2023 | 6.2B | Open-source, consumer-GPU friendly (6 GB INT4), optimized for Chinese dialogue |
| ChatGLM2-6B | June 2023 | 6.2B | Extended context (32K), 42% faster inference |
| ChatGLM3-6B | October 2023 | 6.2B | Native function calling, code interpreter, agent workflows |
| GLM-4 | January 2024 | Not disclosed (proprietary) | 128K context, 60% improvement over GLM-3, multilingual (26 languages) |
| GLM-4-9B | June 2024 | 9B | Open-source, web browsing, code execution, 128K context |
| GLM-4V-9B | June 2024 | 9B | Open-source [multimodal](/wiki/multimodal_ai) vision-language model |
| GLM-4-Plus | August 2024 | Not disclosed | Proprietary, GPT-4o-comparable performance |
| GLM-4-32B-0414 | April 2025 | 32B | Open-source (MIT), strong reasoning, trained on 15 TB of data |
| GLM-Z1-32B-0414 | April 2025 | 32B | Reasoning model, comparable to DeepSeek-R1 on select tasks |
| GLM-4.5 | July 2025 | 355B (MoE, 32B active) | Open-source (MIT) flagship, drove 10x usage growth |
| GLM-4.5V | August 2025 | 106B (MoE, 12B active) | Open-source (MIT) vision-language model based on GLM-4.5 Air[22] |
| GLM-4.6 | September 2025 | 355B (MoE, 32B active) | 200K context, improved agentic coding, open weights[23] |
| GLM-4.6V | December 2025 | 106B / 9B (Flash) | Vision-language, native function calling, 128K context |
| GLM-4.7 | December 2025 | Not disclosed | Open-source, optimized for coding and development workflows |
| GLM-5 | February 2026 | 744B (MoE, 40B active) | 200K context, trained on Huawei Ascend chips, MIT license |
| GLM-5.1 | April 2026 | 744B (MoE, 40B active) | Open-source upgrade of GLM-5, long-horizon agentic tasks[34] |
| GLM-5.2 | June 2026 | 744B (MoE, 40B active) | 1M-token context, MIT license, top open-source coding scores[41][42] |

### Open-Source Strategy

Zhipu has maintained a strong commitment to open-source releases throughout its history. The ChatGLM-6B series was among the most popular open-source Chinese language models in 2023 and helped establish a large ecosystem of fine-tuned derivatives.[14] Beginning with the GLM-4-0414 series in April 2025, Zhipu adopted the MIT license for its open-weight releases, allowing unrestricted commercial use and redistribution.[8] This was a deliberate contrast to more restrictive licenses used by some competitors. Models are hosted on [Hugging Face](/wiki/hugging_face) under the THUDM (Tsinghua University Data Mining Group) and later zai-org organization pages, and are also available through GitHub repositories. Every subsequent flagship release, from GLM-4.5 in July 2025 through GLM-5.2 in June 2026, has shipped open weights under the MIT license.[21][31][34][41]

## Multimodal Models

### CogView (Image Generation)

The CogView series represents Zhipu's text-to-image generation technology, developed in collaboration with Tsinghua University's KEG and BAAI.[3]

| Model | Year | Details |
|---|---|---|
| CogView | 2021 | 4B-parameter Transformer with VQ-VAE tokenizer, NeurIPS 2021 paper, outperformed prior GAN-based models on MS COCO |
| CogView2 | 2022 | Hierarchical Transformer, 10x faster than CogView, competitive with [DALL-E 2](/wiki/dall-e) |
| CogView3 | 2024 | Cascaded [diffusion model](/wiki/diffusion_models) framework for higher quality generation |
| CogView3-Plus | 2024 | Based on Diffusion Transformer (DiT) architecture |
| CogView4 | 2025 | 6B parameters, native Chinese text input and Chinese text-to-image support |

In March 2025, Zhipu released CogKit, an open-source fine-tuning and inference framework that supports both the CogView4 and CogVideoX model families, allowing developers to customize these models for specific use cases.[12]

### CogVideo (Video Generation)

CogVideo, and its successor CogVideoX, power Zhipu's video generation capabilities.[11] The consumer-facing product derived from this technology is called Qingying (清影), which translates roughly to "clear reflection." Qingying was launched in July 2024 as a text-to-video generator available to the public.

| Model | Year | Details |
|---|---|---|
| CogVideo | 2022 | Early text-to-video Transformer model, ICLR 2023 paper |
| CogVideoX-2B | 2024 | 2B parameters, open-source text-to-video, same lineage as Qingying |
| CogVideoX-5B | 2024 | 5B parameters, enhanced video duration, spatial resolution, and semantic consistency |
| CogVideoX-5B-I2V | September 2024 | Image-to-video generation, open-source |
| CogVideoX v1.5 | Late 2024 | 768P resolution, 5-second and 10-second video lengths, 16 frames per second |

### CogVLM (Vision-Language)

CogVLM is a family of vision-language models developed by Zhipu and Tsinghua. CogVLM-17B was open-sourced in October 2023 alongside ChatGLM3 and demonstrated strong performance on visual question answering and image understanding tasks. The CogVLM line later evolved into the GLM-4V series of integrated multimodal models. Subsequent open-weight vision models continued this lineage: GLM-4.5V (August 2025) applied the GLM-4.5 Air architecture to multimodal reasoning,[22] and the GLM-4.6V series followed in December 2025 with native tool calling.[27]

## Products and Platform

### Zhipu Qingyan (智谱清言)

Zhipu Qingyan is the company's consumer-facing chatbot application, comparable to [ChatGPT](/wiki/chatgpt) in function. The app features text conversation, code interpretation, video call interaction, and multimodal input. It provides access to the latest GLM models along with specialized tools for image generation, web browsing, and data analysis. By late 2025, Zhipu Qingyan had accumulated over 25 million registered users, making it one of the most widely used AI assistant apps in China.

### Zhipu Open Platform (bigmodel.cn)

The Zhipu Open Platform, accessible at bigmodel.cn (and internationally at open.bigmodel.cn), provides a Model-as-a-Service (MaaS) API for developers and enterprises.[13] The platform offers access to the full GLM model family, including language models, vision models, image generation (CogView), and video generation (CogVideoX).[13] It supports model [fine-tuning](/wiki/fine_tuning), evaluation tools, intelligent agent creation, and a one-stop modular toolkit for building applications with large models. Fine-tuning can be completed in as little as ten minutes for lightweight tasks. Pricing follows a pay-as-you-go model; as of mid-2025, GLM-4.x API access cost approximately $0.11 per million input tokens. A Batch API option is also available at 50% of standard cost for large-scale data processing. In June 2024, Zhipu slashed GLM-4 API prices by over 50%, joining a broader price war among Chinese AI providers.[4] By March 2026, annualized recurring revenue from the open platform and API business had reached approximately RMB 1.7 billion, roughly 60 times the year-earlier level, as Zhipu raised prices twice during early 2026 amid demand that exceeded supply.[33]

### AutoGLM

AutoGLM is Zhipu's AI agent platform designed for autonomous device operation. The system interprets on-screen content and performs human-like interactions (taps, swipes, text entry) to complete multi-step tasks such as food delivery orders, flight bookings, and cross-app workflows. First introduced in November 2024, AutoGLM was later open-sourced in December 2025, allowing developers to build on its device-operation capabilities.[26] The open-source release, branded Open-AutoGLM, included the AutoGLM-Phone-9B model weights, a phone-use framework with Android adaptation layers, and support for core workflows across more than 50 widely used Chinese apps such as WeChat, Taobao, Douyin, and Meituan.[26]

### GLM Coding Plan

Zhipu sells a low-cost coding subscription, marketed internationally through Z.ai, that gives developers access to its GLM coding models from inside agentic coding tools. At the GLM-4.7 launch in December 2025, the entry tier cost $3 per month, a price widely contrasted in press coverage with U.S. coding subscriptions costing as much as $200 per month.[29] As demand grew, Zhipu raised prices twice in early 2026, including the average increase of roughly 10% in cloud API prices that accompanied the GLM-5.1 release in April 2026.[34]

### Enterprise Solutions

Zhipu provides localized deployment solutions for enterprise customers, which accounted for 84.8% of the company's first-half 2025 revenue. Cloud-based deployment made up the remaining 15.2%. Strategic partnerships include a collaboration with Huawei on "AI-in-a-box" solutions for on-premises data processing and integration work with Qualcomm for on-device AI deployment in mobile and automotive applications.[4] The company also offers government-focused AI solutions, serving both domestic Chinese government customers and exploring international government partnerships.

## Funding and Financials

### Funding History

Zhipu has raised approximately $1.5 billion in total funding across 12 rounds since its founding, including 6 early-stage and 6 late-stage rounds.[7]

| Period | Amount | Notable Investors | Valuation |
|---|---|---|---|
| Early rounds (2019-2022) | Undisclosed | Various Chinese VCs | N/A |
| October 2023 | ~$342M | [Alibaba](/wiki/alibaba_cloud), Tencent, Meituan, Xiaomi, HongShan | ~$2.8B |
| May 2024 | ~$400M | Prosperity7 Ventures (Saudi Aramco), Ant Group | ~$3.0B |
| December 2024 (Series D) | ~$420M | Multiple investors | ~$4.0B |
| March 2025 | ~$257M | Chengdu, Zhuhai, and Hangzhou government-backed funds | N/A |
| July 2025 | ~$140M | Shanghai state funds | N/A |
| January 2026 (IPO) | ~$558M | Public market | ~$6.5B |

The presence of government-backed funds in the 2025 rounds reflects broader Chinese state support for domestic AI development and self-sufficiency.

### Revenue and Losses

| Year | Revenue (RMB) | Net Loss (RMB) |
|---|---|---|
| 2022 | 57 million | 144 million |
| 2023 | 125 million | 788 million |
| 2024 | 312 million | 2.96 billion |
| H1 2025 | 191 million | 2.36 billion |
| 2025 (full year) | 724 million | 4.72 billion[33] |

Revenue grew at a compound annual growth rate of approximately 130% from 2022 to 2024.[25] However, losses grew substantially alongside revenue, driven primarily by R&D spending on model training and infrastructure. R&D expenses increased from RMB 84 million in 2022 to approximately RMB 529 million in 2023.[24] R&D spending rose further to approximately RMB 2.2 billion in 2024 and RMB 3.2 billion in 2025.[24][33] The majority of revenue comes from localized (on-premises) deployment services for enterprise customers, with cloud-based API services making up a smaller share. This pattern is typical of Chinese enterprise AI companies, where large clients often require private deployment for data security and compliance reasons.

Zhipu's IPO prospectus and first annual report give a fuller picture of the economics of frontier model development. Computing-service fees alone exceeded RMB 1.1 billion in the first half of 2025, the company held about RMB 2.55 billion in cash and cash equivalents as of June 30, 2025, and its top five customers accounted for over 40% of revenue.[25] For full-year 2025, the revenue mix shifted toward recurring services: RMB 366 million came from enterprise general-purpose model services, RMB 190 million from the open platform and API business (which nearly quadrupled), and RMB 166 million from enterprise AI agents (up 249%), while gross margin fell to 41% from 56% a year earlier.[33] Annualized recurring revenue from the API platform reached RMB 1.7 billion by March 2026, roughly 60 times the year-earlier level.[33][36] After the annual results, CEO Zhang Peng said Zhipu aspires to become "China's Anthropic," with API revenue as its foundation.[33]

## China's "Six Little Tigers"

Zhipu is recognized as one of China's "Six Little Tigers" (六小虎) of AI, a term coined by Chinese media outlet Caixin in late 2024 to describe the country's most prominent generative AI startups.[10] The six companies are:

| Company | Focus | Founded |
|---|---|---|
| Zhipu AI (Z.ai) | General-purpose LLMs, multimodal AI, AI agents | 2019 |
| [Baichuan Intelligence](/wiki/baichuan) | Large language models | 2023 |
| [MiniMax](/wiki/minimax) | Multimodal AI, consumer applications | 2021 |
| [Moonshot AI](/wiki/moonshot_ai) (Dark Side of the Moon) | Long-context language models (Kimi) | 2023 |
| [01.AI](/wiki/01_ai) (Zero One Everything) | Open-source LLMs (Yi series) | 2023 |
| [StepFun](/wiki/stepfun) (Leap Star) | Multimodal foundation models | 2023 |

Among these companies, Zhipu stands out as the earliest founded, the largest by headcount (approximately 800 employees as of late 2024, with 60-70% in R&D roles), and the first to pursue a public offering.[4][10] Each of the Six Little Tigers is valued at several billion dollars. The company became the first of the group to complete an IPO when it listed in Hong Kong in January 2026, followed shortly by MiniMax.[7]

## Competition and Market Position

In the Chinese AI market, Zhipu competes with both the Six Little Tigers and the AI divisions of major technology companies such as [Baidu](/wiki/baidu_ai) (ERNIE), Alibaba ([Qwen](/wiki/qwen)), ByteDance (Doubao), and Tencent (Hunyuan). The company also faces competition from [DeepSeek](/wiki/deepseek), which gained international attention in early 2025 with its cost-efficient reasoning models and accelerated AI investment across China.

Internationally, Zhipu positions itself as a competitor to [OpenAI](/wiki/openai), [Anthropic](/wiki/anthropic), and [Google DeepMind](/wiki/google_deepmind). The company's stated mission is to pursue artificial general intelligence (AGI) with "super cognitive intelligence beyond human level."[9]

In June 2025, OpenAI said its analysts had seen "notable progress" by Zhipu in securing contracts with governments and state-owned enterprises across Southeast Asia, the Middle East, and Africa, describing the company as offering sovereign large language model infrastructure, often paired with Huawei hardware, in markets including Malaysia, Singapore, the United Arab Emirates, Saudi Arabia, and Kenya; the push competes directly with OpenAI's own "OpenAI for Countries" initiative.[18]

Zhipu has differentiated itself through several strategic choices: a strong open-source commitment with permissive MIT licensing, dual focus on both consumer (Zhipu Qingyan) and enterprise (MaaS platform) markets, deep academic roots at Tsinghua University providing a steady pipeline of research talent, and a willingness to train frontier models on domestically produced hardware (Huawei Ascend chips) rather than relying solely on restricted [NVIDIA](/wiki/nvidia) GPUs. This last factor has become increasingly important given ongoing U.S. export controls that limit Chinese companies' access to advanced NVIDIA chips like the A100, H100, and H200.

## Technical Contributions

Beyond its commercial products, Zhipu and its affiliated researchers have made notable academic contributions:

- **GLM [Pre-training](/wiki/pre-training) Framework**: The General Language Model approach to combining autoregressive and bidirectional objectives, which influenced subsequent Chinese LLM research and provided an alternative to the dominant GPT and BERT paradigms.[1]
- **CogView / CogVideo**: Early open-source contributions to text-to-image and text-to-video generation from the Chinese research community, published at top venues including NeurIPS and ICLR.[3]
- **Efficient [Inference](/wiki/inference)**: Techniques for running large models on consumer hardware, demonstrated by ChatGLM-6B's ability to run on 6 GB GPUs, which helped democratize access to large language model technology.[2]
- **Long-Context Scaling**: Progressive expansion of context windows from 2K (ChatGLM) to 32K (ChatGLM2) to 128K (GLM-4) to 200K (GLM-5) to 1M (GLM-5.2) tokens.[41]
- **[Agent](/wiki/agent) Architectures**: AutoGLM's approach to autonomous device operation through visual understanding and action simulation, representing an early implementation of practical [AI agents](/wiki/ai_agents).
- **Frontier Training on Domestic Hardware**: GLM-5's full training run on Huawei Ascend accelerators with the [MindSpore](/wiki/mindspore) framework demonstrated that a frontier-scale model could be trained outside the NVIDIA hardware and software ecosystem.[31][32]

Key papers from the team include "GLM-130B: An Open Bilingual Pre-trained Model" (ICLR 2023), "CogView: Mastering Text-to-Image Generation via Transformers" (NeurIPS 2021), "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers" (ICLR 2023), and "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools" (2024).[2] Later technical reports include "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models" (2025) and "GLM-5: from Vibe Coding to Agentic Engineering" (2026).[20][30]

## References

1. Zeng, A., Liu, X., Du, Z., et al. "GLM-130B: An Open Bilingual Pre-trained Model." *Proceedings of ICLR 2023*. [https://arxiv.org/abs/2210.02414](https://arxiv.org/abs/2210.02414)
2. GLM Team. "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools." *arXiv preprint arXiv:2406.12793*, 2024. [https://arxiv.org/abs/2406.12793](https://arxiv.org/abs/2406.12793)
3. Ding, M., Yang, Z., Hong, W., et al. "CogView: Mastering Text-to-Image Generation via Transformers." *Proceedings of NeurIPS 2021*. [https://proceedings.neurips.cc/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Paper.pdf](https://proceedings.neurips.cc/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Paper.pdf)
4. "Zhipu AI: China's Generative Trailblazer Grappling with Rising Competition." *Center for Data Innovation*, December 2024. [https://datainnovation.org/2024/12/zhipu-ai-chinas-generative-trailblazer-grappling-with-rising-competition/](https://datainnovation.org/2024/12/zhipu-ai-chinas-generative-trailblazer-grappling-with-rising-competition/)
5. "Alibaba, Tencent and other major Chinese backers invest US$342 million in start-up Zhipu AI." *South China Morning Post*, October 2023. [https://www.scmp.com/tech/big-tech/article/3238698](https://www.scmp.com/tech/big-tech/article/3238698)
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12. CogView4 GitHub repository. [https://github.com/zai-org/CogView4](https://github.com/zai-org/CogView4)
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19. "China's Zhipu AI Secures $140 Million Investment From Shanghai State Funds Amid IPO Push." *Caixin Global*, July 3, 2025. [https://www.caixinglobal.com/2025-07-03/chinas-zhipu-ai-secures-140-million-investment-from-shanghai-state-funds-amid-ipo-push-102337464.html](https://www.caixinglobal.com/2025-07-03/chinas-zhipu-ai-secures-140-million-investment-from-shanghai-state-funds-amid-ipo-push-102337464.html)
20. GLM-4.5 Team. "GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models." *arXiv preprint arXiv:2508.06471*, August 2025. [https://arxiv.org/abs/2508.06471](https://arxiv.org/abs/2508.06471)
21. "Zhipu AI Launches GLM-4.5, an Open-Source 355B AI Model Aimed at AI Agents." *Pandaily*, July 2025. [https://pandaily.com/zhipu-ai-launches-glm-4-5-an-open-source-355-b-ai-model-aimed-at-ai-agents](https://pandaily.com/zhipu-ai-launches-glm-4-5-an-open-source-355-b-ai-model-aimed-at-ai-agents)
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23. "Zhipu AI Releases GLM-4.6: Achieving Enhancements in Real-World Coding, Long-Context Processing, Reasoning, Searching and Agentic AI." *MarkTechPost*, September 30, 2025. [https://www.marktechpost.com/2025/09/30/zhipu-ai-releases-glm-4-6-achieving-enhancements-in-real-world-coding-long-context-processing-reasoning-searching-and-agentic-ai/](https://www.marktechpost.com/2025/09/30/zhipu-ai-releases-glm-4-6-achieving-enhancements-in-real-world-coding-long-context-processing-reasoning-searching-and-agentic-ai/)
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