Agentic AI

GLM 4.5 vs GPT-4: China’s Open-Source Agentic AI Model You Need to Know About

Discover what GLM 4.5 is and how this China agentic AI model compares to GPT-4. Explore its open-source power, features, and global impact.

By Ananya Rajeev 45 min read
Colorful digital illustration comparing Chinas GLM 45 agentic AI chip and GPT 4 with neural circuits and Chinese flag in the background

Introduction

GLM 4.5 is a cutting-edge large language model from China that’s making waves in the AI world. Developed by Zhipu AI (now rebranded as Z.ai), this powerful China agentic AI model is open-source, multilingual, and built to think and act autonomously — not just chat. In this post, we’ll break down what GLM 4.5 is, how it compares to GPT-4, and why it matters for developers, businesses, and beginners alike.

In this post, we’ll break down what GLM 4.5 is, where it came from, and why it matters. We’ll explain the concept of agentic AI in simple terms and explore GLM 4.5’s key technical features like its massive size, multilingual abilities, and unique “thinking” mode. We’ll also compare GLM 4.5 with Western models like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini, looking at their performance, openness, and applications. Finally, we’ll dive into real-world use cases across education, government, healthcare, and enterprise software, and discuss the global implications of China’s AI ambitions. Whether you’re a curious beginner or a tech enthusiast, this comprehensive guide will help you understand GLM 4.5 – China’s agentic AI model – in a clear and beginner-friendly way.

What is GLM 4.5?

GLM 4.5 (which stands for General Language Model version 4.5) is the latest advanced AI model in Zhipu AI’s GLM series. It’s essentially a large language model – similar to GPT-4 or Claude – but developed in China and released as open source. Zhipu AI, the company behind GLM 4.5, was founded in 2019 as a spin-off from Tsinghua University. Backed by Chinese tech giants (like Alibaba and Tencent) and local governments, Zhipu is often called one of China’s “AI tigers”. The GLM series has included models like GLM-130B and ChatGLM before, but GLM 4.5 is their new flagship, introduced in July 2025.

So, what makes GLM 4.5 special? In short, it’s China’s most advanced open-source AI model to date. It’s designed as a foundation model for building intelligent agents – AI systems that can reason, use tools, and perform tasks autonomously. GLM 4.5 is extremely powerful under the hood: it uses a Mixture-of-Experts (MoE) architecture with a whopping 355 billion parameters (making it one of the largest models in the world). Thanks to the MoE design, only about 32 billion of those parameters are “active” for any given query, which makes the model more efficient by dividing knowledge among expert sub-models. Zhipu AI actually released two versions of the model: the full GLM-4.5 (355B parameters) and a lighter GLM-4.5-Air version with 106B parameters (12B active) for easier deployment. Both versions are freely available under an MIT open-source license, meaning anyone can download the model weights, run them locally, fine-tune them, or integrate them into their own applications. This openness is a big deal – it contrasts with Western models like GPT-4 which are proprietary and closed-source. In fact, Zhipu’s GLM 4.5 has been heralded as a “truly open source alternative in an industry increasingly defined by closed, proprietary systems”.

GLM 4.5 isn’t just large; it’s smart. It was trained on a massive dataset (reportedly over 15 trillion tokens of text, including general knowledge and code). It supports multiple languages, with a strong focus on both Chinese and English, making it a bilingual model suitable for a global audience. In fact, the broader GLM family supports dozens of languages and even specialized models for vision and voice, reflecting China’s drive for AI that isn’t limited to English-only tasks. GLM 4.5 is also built to handle very long inputs – it has a context window of 128,000 tokens, which means it can read and reason over long documents (hundreds of pages) or maintain extremely long conversations without losing track. This is significantly more than the 8k-32k token context of many Western models, enabling GLM 4.5 to be used in applications like lengthy reports analysis or multi-turn dialogues in enterprise settings.

In essence, GLM 4.5 is China’s answer to GPT-4, with an emphasis on openness and “agentic” capabilities. It’s built to unify various skills (natural language understanding, complex reasoning, coding, tool use) into one general-purpose AI model. Zhipu’s goal with GLM 4.5 is to push towards more general AI – a model that can handle a wide range of tasks well, rather than being narrowly specialized. Early evaluations show GLM 4.5 is indeed a top-tier model: it achieved third place globally on a suite of 12 standard AI benchmarks, ranking behind only two of the best proprietary models (and #1 among all open-source models). In fact, Zhipu claims GLM 4.5 reaches GPT-4-level performance on many academic benchmarks (like MMLU for knowledge, GSM8K for math, and HumanEval for coding) and outperforms rivals in some categories, especially on Chinese-language tasks. Simply put, GLM 4.5 is one of the most powerful AI models available and represents a major milestone for the Chinese AI community.

Understanding Agentic AI (AI Agents Made Simple)

Before diving deeper into GLM 4.5, let’s clarify what agentic AI means. The term “agentic AI” refers to AI systems (often called AI agents) that can autonomously perceive, reason, and act to achieve goals with minimal human intervention. Unlike a typical chatbot that only responds to individual prompts, an agentic AI can take initiative: it can break down a complex task into steps, use tools or external information, and carry out multi-step plans on its own. In other words, agentic AI combines the creative language abilities of models like GPT with the decision-making and action capabilities of software agents.

Think of an AI agent as a digital assistant that doesn’t need to be told every little step. For example, if you ask an agentic AI, “Plan my weekend trip,” it could autonomously figure out the steps: check the weather, search for flights and hotels, compare options, maybe even book reservations – all on its own if given the right permissions. It’s goal-driven AI. This is possible by connecting a language model to tools and data sources (like search engines, APIs, or databases) so it can gather information and take actions, not just chat. The AI “agent” will perceive the task, reason about how to solve it, then act (and even learn from the results).

Conceptual workflow of an AI agent in an agentic AI system. The agent uses an LLM as its brain to perceive information, reason and plan, then execute tasks via tools, and learn from feedback.

For beginners, an easy way to understand agentic AI is: it’s like giving the AI a bit of agency. Instead of just answering questions, an agentic AI can figure out what actions to take next. This field has become the “next big thing” in AI research – frameworks like LangChain or AutoGPT have shown how a GPT-like model can be turned into an autonomous agent. Companies are excited because agentic AI could handle complex workflows – imagine customer service bots that not only answer queries but also check your account balance and process a refund, all in one go. Or consider an AI coding assistant that not only writes code but also runs tests and debugs errors iteratively. All of that requires the AI to plan, take actions, and adapt on the fly – which is exactly what agentic AI is about.

Now, how does GLM 4.5 fit in? GLM 4.5 is explicitly designed for agentic AI scenarios. In fact, Zhipu calls it their first “agent-native” model. This means GLM 4.5 has certain features baked into its core that help in building AI agents. For one, it has an extended context (128k tokens) to handle the multi-step reasoning and memory that agents need. It also has a native function-calling ability – similar to how OpenAI’s GPT-4 can call external functions when given a specification, GLM 4.5 can directly interface with tools or APIs. Moreover, GLM 4.5 introduced a special “thinking mode” (also called Deep Thinking Mode) that the model can enter when facing a complex problem or when tool use is required. In this mode, the model takes more time, produces step-by-step reasoning or code (like scratch paper calculations), and can decide to use tools. Conversely, it has a normal or “non-thinking” mode for straightforward tasks where it just needs to answer quickly. You can literally switch this mode on or off via a parameter when using GLM 4.5, giving developers fine-grained control over the AI’s behavior. This dual-mode approach is pretty beginner-friendly to grasp: it’s like the model has two gears – a slow, analytical gear for hard problems, and a fast, chatty gear for easy questions.

Thanks to these features, GLM 4.5 can autonomously plan multi-step tasks, use tools like web browsers or code interpreters, and manage end-to-end workflows more effectively. In tests, GLM 4.5 has demonstrated excellent “agentic” abilities. For instance, on a web-browsing challenge (where the AI must browse the web to find answers), GLM 4.5 could answer 26.4% of the questions correctly with tool use – outperforming Anthropic’s Claude-4 and coming close to top proprietary models. It also achieved a high success rate (around 90.6%) in properly calling tools/functions during tasks, beating models like Claude and other open-source agents. All this shows that GLM 4.5 truly lives up to the “agentic AI” label: it’s not just a static language model, but more like a flexible AI agent that can reason and act.

Key Features and Technical Highlights of GLM 4.5

GLM 4.5 packs a lot of technical innovations. Let’s break down its most important features in simple terms:

  • Massive Scale with Mixture-of-Experts: GLM 4.5 is huge – 355 billion parameters – yet it uses a special Mixture-of-Experts (MoE) architecture to stay efficient. In an MoE model, the “knowledge” is split among many expert subnetworks, and only a few experts are active for each input. Think of it like consulting a few specialists out of a panel of experts, rather than one generalist. This means GLM 4.5 can be as smart as a much larger model while only using a fraction of its parameters at a time (32B active). The design, according to insiders, involves deeper layers but slimmer experts, which improves reasoning stability over long contexts. For users, the benefit is better performance without an impractical jump in computing cost. GLM-4.5’s smaller sibling, GLM-4.5-Air, uses the same approach at 106B total (12B active) to offer a more accessible model if you don’t need the absolute top performance.
  • Hybrid “Thinking” Modes: As mentioned, GLM 4.5 can operate in two modes – thinking mode for complex reasoning and non-thinking mode for quick responses. This is a novel feature not seen in most Western models. In thinking mode, the model will produce step-by-step solutions (like showing its work for a math problem or drafting a plan before executing) which can improve accuracy on tough tasks. In non-thinking mode, it skips the chain-of-thought and answers directly (faster, but less exhaustive reasoning). The ability to toggle this gives developers flexibility: you could enable deep thinking when building an agent that needs to use tools or solve a puzzle, and disable it for a simple chatbot response. This hybrid reasoning approach is part of why GLM 4.5 performs so well across diverse benchmarks – it can adjust its strategy to the task at hand.
  • Extremely Long Context Window: GLM 4.5 supports a context length of 128k tokens (approximately 100k+ words). This means it can consider very long inputs or conversations. For perspective, GPT-4’s standard version handles 8k tokens, and even its enhanced version maxes out at 32k tokens; Anthropic’s Claude can handle about 100k tokens in Claude 2. GLM 4.5 slightly exceeds that. In practical terms, you could feed GLM 4.5 an entire book or multiple documents (like ~500 pages of text) and ask questions that require cross-referencing across the whole text. This is huge for tasks like analyzing lengthy reports, legal documents, or academic research. The model uses advanced techniques (like grouped-query attention and Rotary Position Embeddings extensions) under the hood to make this possible without losing coherence. For beginners, the takeaway is that GLM 4.5 has a great memory – it can remember and process a lot more information in one go than most models.
  • Strong Multilingual and Coding Capabilities: GLM 4.5 was trained on an enormous dataset of around 22 trillion tokens of text, drawing from general web data, literature, and a substantial portion (7T tokens) of code and technical data. As a result, it is knowledgeable in many domains and programming languages. Importantly, it’s a bilingual/multilingual model – with training in both Chinese and English at scale, plus some exposure to other languages (the GLM team mentions coverage of 20+ languages for certain tasks like translation). This means GLM 4.5 can switch between languages or translate with ease. It actually excels at Chinese language tasks (not surprising, given its origins), often outperforming Western models on Chinese benchmarks. But it’s also quite proficient in English and can handle tasks in other languages, making it a versatile choice for global users. On coding, GLM 4.5 shines as well – it can generate code, debug, and even build small projects from scratch. In evaluations, it beat or matched GPT-4 on some coding benchmarks (like a software engineering benchmark) and was only narrowly behind Anthropic’s best on others. Essentially, it’s like having a multilingual expert and a coding assistant in one model.
  • High Performance on Reasoning: Reasoning through complex problems (math, logic puzzles, etc.) is a challenge for AI, but GLM 4.5 has made it a core strength. Thanks to its “thinking mode” and specialized training, it achieved near top-of-class results on tough reasoning tests. For example, it scored 98.2% on a math word problem benchmark (MATH 500) and 91.0% on a challenging logical reasoning test (AIM-E). It’s consistently in the top tier for diverse reasoning tasks, often within a few points of the best model (GPT-4 or Claude) on each. What’s impressive is that it does this across the board – many models are great at one thing but not others, whereas GLM 4.5 aims for all-round excellence. This indicates a step toward more general intelligence. For a beginner user, this simply means GLM 4.5 is more likely to handle whatever tricky question you throw at it – be it solving a riddle, analyzing a dataset, or debugging code – without giving up.
  • Tool Use and Agent Abilities: A standout feature of GLM 4.5 is its ability to use external tools effectively. The model was trained and fine-tuned with scenarios involving tool use – for instance, it practiced using a web browser to look up information and using code execution to solve problems. Zhipu even developed a reinforcement learning system (“slime” as they nicknamed it) to train GLM 4.5 on agentic tasks – meaning they let the model actually attempt multi-step tasks (like find information online or interact with a virtual environment) and learn from that experience. As a result, GLM 4.5 has an impressive tool-use success rate (~90% as noted before). For example, if you ask a question that requires data, GLM 4.5 can internally decide to call a search API or a calculator function, get the result, and incorporate it into its answer. This is similar to how GPT-4 with plugins or Bing Chat with web access works – but GLM 4.5’s ability is natively integrated and open for anyone to use. For developers, this lowers the barrier to building powerful AI agents: GLM 4.5 can be the “brain” of an agent that interacts with your choice of tools (APIs, databases, etc.), making it ideal for building autonomous assistants and complex workflows.
  • Open Source, Customizable, and Affordable: Unlike GPT-4 or other closed models, GLM 4.5 is fully open source. The model weights are downloadable (though be aware that the full 355B model is huge in size) and can be run on your own hardware or cloud. It’s released under a permissive MIT license, which means you can use it in commercial products, fine-tune it on your domain data, or modify it – no strings attached. This openness gives enterprises and researchers much more control and transparency. For example, a company can deploy GLM 4.5 on-premises to keep all data in-house, which is important for privacy. Zhipu also emphasizes affordability: they offer API access to GLM 4.5 at a cost as low as $0.11 per million input tokens (and $0.28 per million output tokens). This is orders of magnitude cheaper than GPT-4’s pricing (for comparison, GPT-4 8k context costs about $20 per million tokens at the time of writing). GLM 4.5 also boasts high efficiency – a high-speed version can generate over 100 tokens per second, and it’s optimized to run on common GPUs (Zhipu even demonstrated it on “consumer-grade” GPU setups). All these factors make GLM 4.5 more accessible to developers and organizations who want advanced AI capabilities without breaking the bank. As Zhipu’s CEO puts it, GLM 4.5 shows that “cutting-edge performance can be open, efficient, and affordable”.
  • Continuous Improvement and Ecosystem: GLM 4.5 is not a one-off model; it’s part of a larger ecosystem of AI solutions by Zhipu. The company has also developed AutoGLM, one of China’s earliest agent frameworks, and a suite of smaller “Flash” models that can run faster on limited hardware. They provide an online platform (Zhipu’s Open Platform on bigmodel.cn and the global site chat.z.ai) where you can try GLM 4.5 and other models with built-in tools. This ecosystem approach means users can pick a model size or variant that fits their needs and hardware. The open community around GLM is growing too – by 2025, Zhipu’s models had been downloaded over 40 million times globally, and they have over 700k developers in their user base leveraging these models. For a beginner, this means you’re not alone – there are many resources, community projects (even on Hugging Face or GitHub), and possibly tutorials to help you get started with GLM 4.5. The momentum behind it suggests it will receive updates and improvements over time, possibly including more multimodal capabilities (like combining it with their image model CogView for vision).

In summary, GLM 4.5’s key features – huge scale, dual reasoning modes, long context, multilingual training, tool use, and open accessibility – combine to make it a formidable AI foundation. It’s an ambitious attempt to bring general, agent-capable AI to everyone, spearheaded by Chinese research. Now, let’s see how it compares with some well-known AI models from the West.

GLM 4.5 vs. GPT-4, Claude, and Gemini – How Do They Compare?

Colorful digital illustration comparing Chinas GLM 45 agentic AI chip and GPT 4 with neural circuits and Chinese flag in the background
Colorful digital illustration comparing China’s GLM 4.5 agentic AI chip and GPT-4 with neural circuits and Chinese flag in the background

GLM 4.5 enters an arena alongside several heavyweight AI models developed by Western companies. The most notable ones are OpenAI’s GPT-4, Anthropic’s Claude (we’ll consider the latest version, e.g. Claude 2 or beyond), and Google DeepMind’s Gemini (Google’s next-gen model). Each of these models has its own strengths and focus areas. Below is a side-by-side comparison of GLM 4.5 vs. GPT-4 vs. Claude vs. Gemini on key aspects:

FeatureGLM 4.5 (Zhipu AI)GPT-4 (OpenAI)Claude (Anthropic)Gemini (Google DeepMind)
OriginChina (Zhipu AI, a Tsinghua University spin-off). Open-sourced as Zhipu’s flagship model in 2025.USA (OpenAI, backed by Microsoft). Proprietary model launched in 2023.USA (Anthropic, AI safety startup). Proprietary model, Claude 2 released 2023.USA (Google DeepMind). Announced in 2023–24 as a next-gen model by Google.
Scale & Architecture355B parameters (MoE architecture with 32B active). Hybrid reasoning (thinking & non-thinking modes).Size not publicly disclosed (estimated hundreds of billions of params). Dense Transformer architecture with Reinforcement Learning from Human Feedback (RLHF).~70–100B+ parameters (exact number not public). Dense Transformer with focus on alignment (“Constitutional AI”).Not fully disclosed, but expected to be very large. Uses advanced Transformer architecture; incorporates DeepMind techniques (e.g. AlphaGo-like planning).
OpennessOpen Source – Weights available under MIT license. Can be self-hosted, fine-tuned, and audited freely.Closed Source – Only accessible via OpenAI API (paid). No public model weights.Closed Source – Available via limited API and partners (e.g. Slack). Not open for self-hosting.Closed Source – Used internally by Google (e.g. powering Bard and other services). May be offered via Google Cloud API, but not open weights.
Multilingual SupportBilingual (Chinese & English) by design; good multilingual capability (trained on 2+ languages). Excels in Chinese tasks and handles translation of 26+ languages.Strong English; high multilingual proficiency (GPT-4 is known to perform well in many languages, though slightly weaker in Chinese than GLM-4.5 in some tests).Strong English; decent multilingual (Claude can handle many languages, but primary focus has been English tasks and large English text inputs).Very strong multilingual (expected, as Google has huge multilingual training data). Likely excels in many languages due to Google’s translation expertise.
Context Length128k tokens (extremely long context) for input/output – suitable for very long documents.8k tokens (standard), 32k tokens (enhanced GPT-4 32K version). Possibly extended in future but 32k is common limit.100k tokens (Claude 2) – known for very long context chats. Claude pioneered large context sizes among models.Not officially stated; likely to support long contexts (Google hasn’t detailed, but possibly tens of thousands of tokens given its enterprise focus).
Multimodal CapabilitiesPrimarily text-based, with agentic tool use enabling some multimodal functionality (e.g., it can call an image analysis tool if connected). Zhipu has separate models for vision (GLM-4V for images) and plans to integrate perception.Multimodal (text + image inputs) – GPT-4 can analyze images in addition to text. (No audio output, but can describe images; image input is limited to certain partners.)Text-only for now – Claude does not natively accept images/audio. Focus is on conversational text (though Anthropic might be researching multimodal, it’s not a feature as of 2025).Multimodal by design – Gemini is built to handle text, images, audio, video, and code in a unified model. This is a key differentiator; for example, it can potentially analyze a video or generate image content, unlike others.
Notable Strengths– Agentic Tasks & Tools: Native support for function calls and autonomous task planning. High tool-use success rate (~90%). – Open & Customizable: Can be fine-tuned and deployed on-premises, giving control to users. – Chinese Language & Coding: Top-tier performance in Chinese and strong coding abilities. – Cost Efficiency: Low API cost ($0.11 per 1M tokens) and faster inference (100+ tokens/s).– Reasoning & Knowledge: Excels at complex reasoning, creativity, and factual knowledge (a gold standard on many benchmarks). – Widely Integrated: Powers popular applications (e.g. ChatGPT, Bing Chat) and plugins, with a large user community. – Safety & Alignment: Trained with RLHF to follow instructions and avoid harmful outputs; OpenAI continuously refines it for reliability. – Multimodal Vision: Can interpret images (e.g. describe a picture or diagram), which most others (except Gemini) can’t do yet.– Extremely Long Memory: Handles lengthy documents and conversations up to 100k tokens, great for analyzing long texts or keeping context in customer service chats. – Friendly & Safe Responses: Designed to be helpful and harmless; uses a “constitution” of guidelines to reduce bias or toxic outputs. – Efficient QA and Summaries: Known for concise, clear answers and ability to summarize or explain at length due to its training focus. – High Coding & Math Skills: Claude often performs well in coding tasks and can handle tricky math word problems, rivaling GPT-4 in some cases.– Multi-Modal Mastery: Can combine vision, language, and possibly other modalities (audio/video) seamlessly, enabling richer applications (e.g. analyzing a video and answering questions about it). – Integration with Google’s Ecosystem: Likely powers or will power Google products (Search, YouTube, Workspace), meaning it’s optimized for real-world tasks at massive scale. – Creative Content Generation: Expected to excel in generating not just text but also images or even music, given Google’s AI research (Imagen, MusicLM, etc. integrated into Gemini). – Unknown Potential: Being a next-gen model, it may introduce new techniques (rumored to incorporate AlphaGo-like strategic planning) that could push the performance beyond GPT-4 in certain areas.
Current Limitations– Requires significant computing resources to run locally (the 355B model is resource-heavy, though the 106B “Air” model is easier). – As a newer model, it might have fewer third-party integrations or community plugins than GPT-4. – Primarily text-based output (no built-in image generation from text; needs separate models/tools). – Benchmarking shows it’s slightly behind the absolute top models (GPT-4 or Gemini) on some tasks, but closing the gap.– Proprietary and expensive: users rely on API access and usage can be costly for large volumes. No ability to self-host or inspect the model directly. – Fixed context (32k max) may be limiting for tasks needing more memory (though often sufficient). – Multimodal image understanding is available, but image generation is not (GPT-4 can’t create images, only describe them). – Still can produce incorrect or biased answers at times; not fully transparent how it makes decisions due to closed model nature.– Not openly available to self-host, which can be a barrier for customization. – Fewer public uses than ChatGPT/GPT-4, so community knowledge is less. – Tends to be overly verbose or overly deferential in answers (due to its safety training). – While very capable, it slightly trails GPT-4 in certain benchmark categories (OpenAI reports Claude as less performant in some areas like coding, as of 2023).– Not publicly released at the time of writing, so real performance is partly speculative. – Will likely be proprietary (Google hasn’t indicated any open-source plan), so similar issues of trust and transparency as other closed models. – Integrating such a multimodal model outside Google’s ecosystem might be difficult for developers (if not provided via easy API). – As with any complex model, ensuring factual accuracy and avoiding errors in all these modalities is a challenge that remains to be seen.

Table: Feature comparison of GLM 4.5 with OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini. GLM 4.5 stands out as the only open-source model in this group, with a focus on agentic capabilities and Chinese language proficiency, whereas GPT-4 and Claude are proprietary leaders in general AI tasks, and Gemini is poised as a multimodal powerhouse integrated into Google’s products.

As we see from the table, GLM 4.5 is highly competitive with the best Western models. In evaluations, GLM 4.5’s overall performance is on par with top models like GPT-4 and Claude-4 on many benchmarks. It actually ranked third among all models globally (including both open and closed) in a comprehensive test. The only models ahead of it were proprietary ones (likely GPT-4 and possibly Google’s latest). This is remarkable considering GLM 4.5 is open. For example, on an “agent” benchmark measuring how well an AI can use tools, GLM 4.5 matched Claude 4 in performance, and on coding challenges it beat GPT-4’s public version in some cases.

That said, each model has its niche. GPT-4 is currently the most widely used and is known for its reliable reasoning and broad knowledge – it’s the model behind ChatGPT’s most advanced version and is heavily optimized through human feedback. However, it’s closed-source and not accessible without paying OpenAI, which makes some organizations (and countries) wary of relying on it. Claude by Anthropic differentiates itself with a huge context window and a safety-first approach; it’s great for tasks like digesting long documents or having extended conversations. Claude is also proprietary, but Anthropic has positioned it as a friendly assistant with fewer usage restrictions (useful for businesses that need large-context AI). Gemini is Google’s ambitious project to leapfrog GPT-4 – by mid-2025, reports suggest Gemini rivals or surpasses GPT-4 in certain areas, especially because it’s multimodal (handling text, images, audio all together). For instance, Gemini could take a complex input like, “Watch this video and summarize the main points in French,” and handle it end-to-end – something beyond GPT-4 or GLM 4.5 as of now. However, Gemini is still within Google’s ecosystem, mostly behind the scenes powering things like Search or YouTube recommendations.

One key difference is openness. GLM 4.5 is the only one in this lineup that is open-source and can be deployed by anyone. This means if a company or developer needs full control (for privacy, customization, or cost reasons), GLM 4.5 is very appealing. You could fine-tune GLM 4.5 on your proprietary data to get a custom model, which you just cannot do with GPT-4 or Claude. Also, cost is a factor: because GLM 4.5 is open, you can run it on your own hardware or use Zhipu’s low-cost API. Meanwhile, using GPT-4 at scale might incur significant API costs.

In terms of applications, GPT-4 and Claude are generalist models used in a variety of apps (from chatbots to coding assistants to education tools). GLM 4.5 can serve similar purposes but might particularly shine in applications needing autonomy (agents) or Chinese language support. Gemini, once fully available, will likely be the go-to for applications that need to process multiple types of media or that are tightly integrated with Google’s platform (imagine AI in Google Docs or an AI that can analyze your Google Photos).

To put it simply: GLM 4.5 is to China what GPT-4 is to the US – a flagship model demonstrating national AI prowess. Each model has its pros and cons, but GLM 4.5’s emergence is great news for the AI community because it provides a powerful open alternative. This fosters competition and innovation, ensuring no single company monopolizes advanced AI. And as GLM 4.5 shows, the gap between Chinese AI models and Western models is closing rapidly, with GLM 4.5 even surpassing some Western models in specific metrics (like tool use success and Chinese language tasks). Next, let’s explore how GLM 4.5 can be used in real-world scenarios across different industries.

Use Cases of GLM 4.5 Across Industries

GLM 4.5 is a general-purpose model, which means it can be applied in many areas. Thanks to its agentic abilities, long context, and coding skills, it’s like a multi-talented assistant that can be adapted to various sectors. Here are some notable use cases and industry applications for GLM 4.5:

  • Education and E-Learning: GLM 4.5 can power intelligent tutoring systems and language learning apps. For example, it could serve as a personal tutor that converses with students, explains concepts, and even generates practice questions. Its large knowledge base and reasoning ability allow it to answer questions across subjects. It can also create educational content – like summarizing chapters of a textbook or generating quiz questions. In fact, models like GPT-4 are already used in tools like Duolingo for language learning and Khan Academy for tutoring, and GLM 4.5 can play a similar role. The advantage of GLM 4.5 is its multilingualism; a student could ask a question in Spanish and get an answer in Spanish, or request an explanation in simpler Chinese, etc. Additionally, educational institutions can fine-tune GLM 4.5 on specific curricula or use it to automate grading of essays with feedback, given its ability to analyze long texts. Zhipu’s platform explicitly mentions applying AI in education to “enhance teaching effectiveness and learning experiences.” Imagine an AI teaching assistant that is available 24/7 to help with homework – that’s the kind of application GLM 4.5 enables.
  • Government and Public Services: Government agencies can leverage GLM 4.5 for a variety of applications. One is language translation and document analysis – GLM 4.5’s strong translation capability (covering formal and long documents) can help in translating policy documents or legal texts accurately while preserving tone. It’s also useful for summarizing public comments or large reports for busy officials. Another use case is intelligent chatbots for citizen services: for instance, a government could deploy a chatbot to answer questions about public services, procedures, or laws in multiple languages. GLM 4.5, with its 128k context, could maintain context over a long conversation with a citizen and provide detailed answers or even fill out forms interactively. Notably, Zhipu AI has secured several contracts with local governments in China, indicating these models are being used in smart city initiatives, e-government platforms, and other civic tech. We might see GLM 4.5 used for analyzing surveillance data or social media trends as well (since it can handle big data inputs). There’s also potential use in drafting and evaluating policies: an agentic AI could help simulate the outcomes of a policy by gathering data and providing a report. Given that GLM 4.5 can be hosted internally, governments may prefer it for sensitive data processing over sending data to foreign APIs. Overall, GLM 4.5 can make government operations more efficient by automating routine cognitive tasks and assisting officials in decision-making with quick insights.
  • Healthcare and Medicine: In healthcare, large language models must be used carefully, but they have huge potential. GLM 4.5 could act as a medical assistant AI that helps doctors and patients. For example, it can digest and summarize large medical documents or research papers – useful for doctors keeping up with new studies. It can also extract key information from a patient’s electronic health records (EHR) and present it in an easy summary. With its reasoning ability, it might assist doctors in differential diagnosis by comparing patient symptoms against its knowledge (though any recommendations would need human verification). Another application is patient Q&A and triage: an AI chatbot could answer patients’ questions about medications or symptoms, and advise if they should seek care. NVIDIA has noted that AI agents can help doctors by “distilling critical information” to help make better decisions. GLM 4.5 could, for instance, parse a patient’s lab reports and highlight abnormalities. Additionally, healthcare involves a lot of administrative paperwork – GLM 4.5 could generate first drafts of medical reports, handle insurance claim forms, or translate patient materials into different languages. Zhipu’s materials mention using AI models to “enhance the efficiency of medical services” and build new patient service ecosystems. Imagine a hospital chatbot that schedules appointments, answers common questions, and follows up with patients about post-care instructions in their native language – that’s in scope for GLM 4.5-based solutions (with proper safety guardrails and domain fine-tuning). Of course, in healthcare, verification by professionals is crucial, but an AI like GLM 4.5 can significantly reduce the burden by automating many tasks and providing quick information retrieval.
  • Enterprise Software and Development: GLM 4.5 is a boon for the enterprise software domain. Its strong coding ability means it can function as an AI pair-programmer or code assistant (similar to GitHub’s Copilot but potentially even more capable in multi-step tasks). Developers can use GLM 4.5 to generate code snippets, document code, or even build small applications from a description. In fact, GLM 4.5 has been demonstrated creating entire simple games and web apps autonomously. Enterprises can integrate GLM 4.5 into their development workflows for tasks like automated code reviews, writing unit tests, or migrating code from one language to another. Zhipu’s own evaluation showed that incorporating GLM 4.5 in a coding toolchain sped up delivery times by 40% in multi-module projects. Beyond coding, enterprise software often means internal tools – GLM 4.5 can serve as a conversational interface to company databases (answering questions like “How many sales did we make last quarter?” if connected to data), assist in project management (drafting emails, generating reports), and help in customer support. For customer service, an agentic GLM 4.5 could handle entire support tickets: reading a customer’s issue, looking up their account info (via API calls), and providing a resolution or escalating appropriately. Some companies are building their own AI copilots for employees – imagine every worker has an AI assistant that knows the company’s knowledge base and can help with daily tasks. Because GLM 4.5 can be fine-tuned on proprietary data and run on-prem, companies can safely build such assistants without sending data to external services. Another interesting use is content creation in marketing: GLM 4.5 can draft marketing copy, generate slides (there’s even a mention of using it for PPT generation with logical structure), and personalize content for different audiences. In summary, GLM 4.5 can streamline enterprise workflows by automating programming tasks, acting as an intelligent analyst, and serving as a powerful natural language interface to software systems.
  • Content Creation and Media: Creative industries can also benefit from GLM 4.5. Its ability to produce coherent, contextually rich text makes it suitable for writing articles, stories, or social media content. It can generate content in different styles or tones – for instance, writing a press release in formal language or a blog post in a casual tone. It’s also capable of script writing; one example use case is generating dialogue for a game or a storyboard for a short video. Zhipu notes that GLM 4.5 can handle tasks like creating emotionally engaging copy and even scripting for platforms like Douyin (TikTok). For media companies, an AI like this can help draft news summaries, produce quick reports on data (e.g., financial earnings summaries from raw data), or localize content by translating and adapting it to different cultures. Since GLM 4.5 supports creative writing and has knowledge of literature, it could even be used by authors for inspiration or by publishers to auto-generate first drafts of content. Moreover, with its agentic abilities, GLM 4.5 could coordinate multi-step content creation: for example, create an outline, then flesh out each section, then perhaps call an image generation tool (like Zhipu’s CogView or others) to add illustrations – effectively acting as a mini-producer assembling a piece of content. This could significantly speed up content pipelines in marketing and publishing.

These are just a few industry examples. GLM 4.5’s versatility means it can also be applied in finance (analyzing market reports or assisting with customer inquiries in banking), law (summarizing case files or suggesting contract language), automotive (maybe as an in-car voice assistant with deep knowledge), gaming (NPC dialogue generation or game code debugging), and more. In China, Zhipu’s GLM models are being plugged into everything from smart office platforms (making presentations, drafting documents) to virtual characters that can engage users in apps. A virtual customer service agent or a “digital human” avatar that chats naturally with customers could be powered by GLM 4.5’s dialogue skills. The key is that because GLM 4.5 can plan and use tools, it’s not limited to one-off Q&A – it can handle interactive, multi-turn scenarios that are common in real business processes.

China’s AI Ambitions and Global Implications

The rise of GLM 4.5 is not just a technical story – it’s also a strategic one. China has been pouring resources into AI research and sees it as a critical area of competition on the global stage. GLM 4.5’s development and open-source release highlight a few important implications and ambitions:

  • China’s Push for AI Leadership: China is determined to be a world leader in AI, and one approach has been to encourage domestic alternatives to Western models like ChatGPT. By mid-2025, Chinese companies and institutions had released over 1,500 large language models, more than any other country. GLM 4.5 sits at the top of this pyramid as one of the most advanced. The Chinese government has supported a cohort of AI firms (sometimes dubbed the “four AI Tigers”), including Zhipu, Baidu, Tencent, and others, to spur innovation. Zhipu AI itself has backing from local governments and major investors. The fact that OpenAI singled out Zhipu as a notable emerging competitor shows that Chinese AI is catching up. By achieving near state-of-the-art performance, GLM 4.5 signals that top-tier AI tech is no longer exclusive to Silicon Valley. This contributes to national pride and technological independence for China.
  • Open-Source as a Strategy: Interestingly, China is embracing open-source AI in a big way. While Western leaders like OpenAI and Google keep their best models closed, many Chinese companies (including Zhipu) are open-sourcing very powerful models. GLM 4.5 being MIT-licensed is a prime example. Other Chinese models like Baidu’s ERNIE or smaller ones from startups are also being open-sourced or made widely available. This serves multiple purposes: it rallies the global developer community to use and improve Chinese models, potentially setting global standards or ecosystems around them. It also counters U.S. dominance – if everyone can use a Chinese model for free, it might reduce reliance on American companies. Open-sourcing is somewhat surprising (given companies give away their “secret sauce”), but it has given China a lot of influence in open AI circles. For instance, Meta’s open-source LLaMA model gained traction worldwide; China likely sees a similar benefit by open-sourcing GLM. Indeed, Zhipu’s open approach has gained them over 700,000 developers and companies using their platform, and tens of millions of downloads of their models. There’s a geopolitical angle too: open models can’t be easily sanctioned or restricted – they’re out in the wild for anyone to use, including countries that might be barred from using U.S. APIs. This democratization could lead to a wider adoption of Chinese AI technology globally.
  • Building an Ecosystem and Standards: China’s AI firms aren’t working in isolation; there’s a trend of forming alliances and sharing research to create a domestic AI ecosystem. By releasing models like GLM 4.5, Chinese researchers contribute to open benchmarks and participate in the global AI conversation (for example, GLM 4.5 being listed in the Stanford AI Index Report as a notable model). We also see Chinese companies focusing on specific strengths: e.g., GLM’s agentic focus, Alibaba’s Qwen models on business tasks, others on multimodal. This specialization and collaboration could lead to a suite of models that together cover all fronts (language, vision, reasoning, etc.) at world-class level, all under Chinese stewardship. Globally, this means more competition which is healthy – users and developers will have choices beyond the Western tech giants. It could also drive AI prices down (as seen with Zhipu’s low pricing) and accelerate innovation (each side pushing the other). However, it might also lead to standards divergence: for instance, Chinese models might follow local norms or censorship rules for their public deployments, which differ from Western values. The global AI community will need to navigate multiple “ecosystems” of models.
  • Ethical and Safety Considerations: With great power comes great responsibility. A concern for any advanced model is misuse or unintended consequences. Zhipu, to its credit, has been proactive about AI safety – it was the first Chinese company to sign the Frontier AI Safety commitments (a set of voluntary commitments also signed by OpenAI, Google, etc.). This indicates China’s top labs are engaging in the conversation about safe AI development. But different cultural and political contexts mean their approach might differ. For example, Chinese models like GLM 4.5 will be tuned to comply with Chinese regulations on AI content (ensuring they don’t produce politically sensitive or harmful content as defined by authorities). Global users might find these models have certain filters or avoid certain topics. This raises interesting discussions on AI alignment – whose values should a model align with when it’s used globally? On the flip side, open models allow any community to fine-tune them to their own norms if needed, which proprietary models don’t allow. So an open model like GLM 4.5 could even be adapted by communities that want less restrictive or more culturally specific behavior, thereby “localizing” AI in a way one-size-fits-all models cannot.
  • Economic and Competitive Impact: The competition between GLM 4.5 and models like GPT-4/Gemini is essentially an AI arms race. We’re likely to see even faster progress as each tries to outdo the other. Google’s Gemini was partly a response to GPT-4; now GLM 4.5 challenges both, which might spur OpenAI to accelerate GPT-5 or increase openness. For businesses and consumers, this is good news – better models, potentially lower costs. Countries that are not AI superpowers will also benefit by having open models to build on (for example, a startup in Europe or India could take GLM 4.5 and fine-tune it for their language or domain, rather than needing to license GPT-4). There’s a localization aspect too: GLM 4.5’s strong Chinese capability ensures that Chinese tech companies can create products tailored for the huge Chinese market (which has linguistic and cultural nuances that English-trained models handle less gracefully). This means we’ll see more AI products from China that feel naturally suited to Chinese users – from chat assistants to enterprise tools – thereby boosting the local AI economy and potentially creating exportable products to other non-English-speaking markets. China’s ambition is not just to catch up but to lead; if models like GLM 4.5 continue to improve, we might soon see a Chinese model at the very top of the AI rankings. That could shift the balance in AI research influence and even talent attraction (AI scientists may flock to where the best models are being built, be it Silicon Valley or Beijing).

In conclusion, GLM 4.5 is more than just a new model – it’s a signal of China’s coming-of-age in AI research and a harbinger of a more multipolar AI landscape globally. For users worldwide, it means more choice and perhaps a more open AI ecosystem (since China’s top model is open source). It also means that the race for AI supremacy is intensifying, which will likely yield incredible AI advancements in the near future. Of course, collaboration on setting global norms for AI will be essential to ensure these powerful models are used for beneficial purposes across borders.

Getting Started with GLM 4.5 (For Developers)

If you’re a developer or an enthusiast wanting to try GLM 4.5 yourself, you’re in luck – because it’s open source, you can start experimenting with it right away. Zhipu AI provides model files on platforms like Hugging Face, and they also offer an API. Here’s a quick guide to getting started:

Using the GLM 4.5 API: Zhipu (Z.ai) offers a cloud API service for GLM 4.5 globally. You would need to sign up on their platform (either the global site or the Chinese open platform) and get an API key. The API allows you to send prompts and receive model completions, similar to OpenAI’s API. It supports parameters to enable/disable “thinking mode” and other settings. Documentation is available on their site. This is the easiest way to use GLM 4.5 without heavy compute requirements – Zhipu runs the model on their servers and you just pay for usage (with the mentioned low rates).

Running GLM 4.5 Locally: For those who have the hardware, you can download the model weights and run it on your own machine. Keep in mind the full GLM-4.5 model is 355B parameters (which likely requires at least 8 A100 GPUs or equivalent), so it’s not trivial. However, the GLM-4.5-Air model (106B) or smaller versions (Zhipu has GLM models down to 6B) are more feasible to run on a multi-GPU setup. You’ll need to use a deep learning framework like PyTorch and potentially special optimization (like DeepSpeed or vLLM) to handle the model. The Hugging Face model card indicates that GLM-4.5 is integrated with Transformers and vLLM libraries. Zhipu has also released FP8 (8-bit) versions for efficiency.

Here’s a sample Python code snippet using Hugging Face Transformers to load GLM 4.5 and generate a response:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the GLM-4.5 model and tokenizer 
model_name = "zai-org/GLM-4.5"  # Hugging Face repository name for GLM-4.5
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    device_map="auto",       # automatically use available GPUs/TPUs
    torch_dtype="auto",      # use mixed precision if available
    trust_remote_code=True   # allow custom modeling code from Zhipu
)

# Prepare a prompt
prompt = "Question: Who developed the GLM 4.5 AI model?\nAnswer:"

# Tokenize input and generate output
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=False)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Code explanation: We use AutoTokenizer and AutoModelForCausalLM to load the tokenizer and model. trust_remote_code=True is required because Zhipu provided some custom model implementation details. The model will automatically load across GPUs (if you have multiple) due to the device_map="auto". We then prompt the model with a question and ask it to generate an answer. In a real scenario, you might want to enable the thinking mode or adjust generation parameters for creativity. The output in this case might be something like: “GLM 4.5 was developed by Zhipu AI, a Chinese research company, originally a spin-off from Tsinghua University.” (The model might not cite sources in its answer by default, but it will know the factual info from its training data).

If you don’t have the GPU resources for GLM-4.5, you can try smaller GLM models or use the inference API on Hugging Face which might have a demo. Also, Zhipu’s web interface (chat.z.ai) allows you to chat with GLM 4.5 online with a generous free token quota.

Because GLM 4.5 is open, you can also fine-tune it on your own data using frameworks like PyTorch Lightning or Hugging Face Trainer, but note that fine-tuning such a large model will require serious hardware or using techniques like Low-Rank Adaptation (LoRA) to adjust only some weights. Zhipu often provides fine-tuning tools or offers fine-tuning as a service (they even advertised free tokens for fine-tuning on their platform).

In summary, GLM 4.5 is accessible to developers: you can use it via API or run it yourself. This democratization means you can experiment with an AI model that’s on par with the best out there, without needing to be at a big tech company. It’s a great opportunity to build innovative applications or just learn more about how these advanced AI systems work.

Conclusion

GLM 4.5 represents a significant leap in the AI landscape – it’s China’s most advanced contribution to the era of large language models, and it arrives with a distinctive philosophy of openness and agentic design. We’ve learned that GLM 4.5, developed by Zhipu AI (Z.ai), is not just another chatbot model; it’s a sprawling 355B-parameter powerhouse that can reason, code, and act as an autonomous agent. It bridges the gap between Eastern and Western AI efforts, matching models like GPT-4 and Claude in many aspects while offering features like a 128k context and dual thinking modes that set it apart. For beginners and global users, GLM 4.5 is a promising development – it’s easier to access (being open-source) and can cater to non-English needs more naturally, all while maintaining top-notch performance.

We discussed how agentic AI is shaping the future, and GLM 4.5 is built for that future. It can plan tasks and use tools on its own, making it a foundation for the next generation of AI applications that go beyond simple Q&A. From helping students learn, to assisting doctors in sorting medical data, to writing code or content in enterprise settings, GLM 4.5’s versatility opens countless possibilities. Its emergence also underscores China’s strategic push in AI – showcasing that innovation is not confined to one country. The global AI community stands to gain from this competition: as models like GLM 4.5 vie with GPT-4, Claude, and Gemini, we can expect faster advancements, more openness, and hopefully, more affordable AI services for everyone.

For a beginner looking at this space, the key takeaway is that AI is becoming more powerful and more accessible at an astonishing rate. GLM 4.5 is a testament to that – a year or two ago, having a model of this caliber openly available would have sounded like science fiction. Now you can literally download it or call an API to harness its capabilities. It’s an exciting time to be involved with AI, whether you’re a developer building the next big app or a student curious to learn from these models.

Going forward, keep an eye on GLM 4.5 and the GLM series. Zhipu is likely to continue refining their models (perhaps GLM 5.0 is on the horizon) and pushing the boundaries, maybe adding true multimodal inputs or further increasing efficiency. The competition will also heat up – OpenAI, Google, Anthropic and others will try to maintain their lead. For end users, this means better AI tools in daily life: more natural language assistants, smarter enterprise software, and AI that can genuinely assist in creative and complex tasks. The hope is that with models like GLM 4.5 being open, the benefits of AI will be spread widely and not just controlled by a few companies.

In closing, GLM 4.5 is a landmark AI model that you should know about. It symbolizes a shift towards a more open and globally inclusive AI ecosystem. Beginners should feel encouraged that understanding and even utilizing such advanced AI is within reach – you don’t need to be an expert to grasp its concepts or a CEO to use it in your product. As we demystified in this article, terms like “agentic AI” simply boil down to AI doing multi-step tasks autonomously, and GLM 4.5 is basically a very smart, multilingual AI brain that anyone can tap into. We’re witnessing AI history in the making, with East and West innovating in parallel. Whether you want to build with GLM 4.5 or just learn from it, it’s clear that models like this are driving us into the next era of AI – one where intelligent agents might become as common as smartphones, helping us in every domain of life.

Thank you for reading! Feel free to share your thoughts or questions about GLM 4.5 and agentic AI. If you found this guide helpful, stay tuned for more insights into the ever-evolving world of AI. Together, let’s keep exploring these technologies responsibly and creatively, making AI work for everyone.

Sources: The information in this article was gathered from Zhipu AI’s official releases and technical blogs, news reports, and expert analyses to ensure accuracy. Key references include Zhipu’s GLM-4.5 technical blog, a Reuters news report on the model’s release, a detailed TechStartups article, and Zhipu’s press release on GLM-4.5, among others. These sources provide insight into GLM 4.5’s development, features, and performance relative to models like GPT-4, Claude, and Gemini.

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Ananya Rajeev

Ananya Rajeev is a Kerala-born data scientist and AI enthusiast who simplifies generative and agentic AI for curious minds. B.Tech grad, code lover, and storyteller at heart.