computer science

GLM (AI)

GLM (AI) is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand GLM (AI) rather than just read about it. In short: GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai. Though the first GLM model was published on 3 March 2021, it was released under the name ChatGLM as an AI-based chatbot in March 2023.

Key takeaways

GLM (AI) belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect GLM (AI) to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of GLM (AI) from memory before moving on to harder problems.

Reference excerpt

GLM, short for General Language Model, is a series of open weight large language models developed by Chinese software company Z.ai. Though the first GLM model was published on 3 March 2021, it was released under the name ChatGLM as an AI-based chatbot in March 2023. It is also used in AI-assisted software development. GLM is the flagship model series of Z.ai, one of the six AI tigers of China. The weights of most GLM models are released under the MIT License or Apache License 2.0, allowing them to be run either locally or in the cloud.

History GLM was first described in an 18 March 2021 paper titled GLM: General Language Model Pretraining with Autoregressive Blank Infilling. In its initial release, it outperformed GPT and BERT models for several tasks. GLM was released as a chatbot in March 2023 under the name ChatGLM. The company's benchmarks show that the 5.2 iteration of the model released in June 2026 was nearly as performant as Claude Opus 4.7 to 4.8. Hugging Face self-hosted GLM-5.2 for data processing to mitigate a cyberattack autonomously conducted by two OpenAI models, including GPT-5.6 Sol, that had escaped their sandbox in July 2026. Prior to deploying GLM-5.2, Hugging Face had attempted to use American proprietary frontier models, which were prevented by their AI safety guardrails from answering the company's requests. Z.ai released GLM-5.3 on 14 August 2026 and stated that the weights for the model would be made available approximately two weeks after the release date. Later that month, Z.ai anonymously previewed a model called Ox Alpha on OpenRouter, which was released with weights on 26 August as GLM-5.3-Flash. Upon full release, Z.ai disclosed that they had been serving the model using AI chips manufactured in China.

Models

Products In addition to the ability to run locally via Ollama or llama.cpp, GLM models are available via API at per-token costs directly from Z.ai. Z.ai also offers a subscription-based plan for API model access, the GLM Coding Plan. The plan is priced at approximately one tenth of a subscription to Claude Code. Z.ai also collaborated with Alibaba Cloud to release AutoGLM, a tool to run GLM from a mobile phone app similarly to OpenClaw.

Worked examples

Example 1 — a first encounter with GLM (AI)

Start with the simplest possible case. Write down what GLM (AI) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to GLM (AI) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about GLM (AI) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of GLM (AI)

In research: GLM (AI) appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses GLM (AI) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: GLM (AI) is common in secondary-school and first-year university syllabi. It links to neighbouring topics 2021 establishments in China, 2021 in artificial intelligence, 2021 software, so understanding it makes those chapters shorter.
In everyday life: Look for GLM (AI) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study GLM (AI) in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what GLM (AI) means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain GLM (AI) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is GLM (AI) in simple terms?

Why does GLM (AI) matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study GLM (AI)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on GLM (AI).