The short answer
GLM is a family of AI models from Z.ai, the Chinese company formerly known as Zhipu AI. The flagship, GLM-5.3, was released on 18 August 2026 and is built for coding and long agent tasks, with a 1 million token context. You can use GLM in the chat app at chat.z.ai, through the API, inside coding agents with the GLM Coding Plan, or by downloading its open weights from Hugging Face.
| Made by | Z.ai (formerly Zhipu AI), China, with international services run from Singapore |
|---|---|
| Free plan | Chat app at chat.z.ai (limits not published); free older API models; open weights free to download |
| Models | GLM-5.3, GLM-5.3-Flash, GLM-5.2 and older versions |
| Best at | Agentic coding, long-context engineering work, self-hosting |
| Shows sources | Coding Plan includes a Web Search tool; not documented for the chat app |
| Team plans | GLM Coding Plan Team Plan; API for products |
| Your data | Hosted service may use data to improve models, with no documented switch; self-hosted weights keep data with you |
What GLM is in November 2026
GLM is a family of AI models from Z.ai, the Chinese company that used to be known outside China as Zhipu AI. Most people meet GLM in one of four ways: the Z.ai chat app at chat.z.ai, the pay-as-you-go API, the GLM Coding Plan subscription that plugs into coding agents such as Claude Code, or the open weights that anyone can download from Hugging Face and run on their own servers.
The current flagship is GLM-5.3, released on 18 August 2026. It uses the same base model as GLM-5.2 and gets all of its gains from extra training afterwards. Z.ai says it scores 50% higher than GLM-5.2 on its in-house Z.ai Code Bench. It reads text only, accepts up to 1 million tokens of context, writes up to 128,000 tokens in one answer, and always reasons before it replies. You choose how hard it thinks with three effort levels, low, high and max, and it uses max unless you pick another. Switching reasoning off is no longer supported.
Its smaller sibling, GLM-5.3-Flash, arrived on 26 August 2026. It has 320 billion parameters in total with 18 billion active at a time, and unlike GLM-5.3 it can look at screens and images. The chat app at chat.z.ai describes itself as powered by GLM-5.3-Flash, so that is the model you are most likely talking to there.
The pace is worth knowing: GLM-5 came out on 12 February 2026, GLM-5.1 on 7 April, GLM-5.2 on 16 June and GLM-5.3 in August. A new version every two months means any review older than a season is probably describing a different model.
Where GLM is genuinely strong
Coding inside the tools you already use
GLM is built first for software work, and the GLM Coding Plan is the clearest sign. One subscription works inside more than 20 agent tools, including Claude Code, Codex, OpenCode, Cline and Z.ai's own ZCode. If you already like the way Claude Code works but want a different model behind it, the Coding Plan lets you point it at GLM-5.3. Z.ai also describes office uses for the same plan, such as deep research and business data analysis, but coding is where most of its effort goes.
Long, multi-step jobs
The 1 million token context in GLM-5.2 and GLM-5.3 means a large codebase or a long set of documents can fit in one conversation. Z.ai says GLM-5.1 could already work on its own for up to eight hours in a single run, and GLM-5.3 was trained on tasks that Z.ai describes as several days of work for an experienced engineer. Treat those as the maker's claims, but they tell you what the model is designed for: long jobs, not quick chat.
Open weights you can host yourself
This is the reason many companies look at GLM at all. The weights of GLM-5.3 have been on Hugging Face since 25 August 2026 under the GLM-5.3 License, which allows commercial use and modification. The one condition that matters to large firms is that a company running a model-as-a-service business with more than 10 billion US dollars in yearly revenue must pass a security review by Z.ai first. GLM-5.3-Flash and GLM-5.2 are under the plain MIT licence. Running the weights on your own hardware means your prompts never reach Z.ai at all.
Price
On the API you pay per token, GLM-5.3-Flash costs about a tenth as much per token as GLM-5.3, and older models such as GLM-4.7-Flash and GLM-4.5-Flash are free. The Coding Plan also charges half the usual credits outside peak hours, which are 14:00 to 18:00 Singapore time on weekdays. If you work in Europe or the Americas, much of your day is already off-peak. See Z.ai's pricing page for current rates.
Where GLM falls short
- The flagship cannot see. GLM-5.3 accepts text only. For screenshots, charts or interface work you need GLM-5.3-Flash or another tool.
- Most benchmarks come from Z.ai. The model card compares GLM-5.3 with leading Western and Chinese models and it often lands near the top, but these are the maker's own numbers. Run your own test on real tasks before you switch.
- The chat app is thinly documented. Z.ai's official pages explain the API and Coding Plan in detail, but say little about the chat app's limits. If you want a polished everyday assistant with clear plans, ChatGPT or Claude are easier choices.
- Data terms need a careful read. More on that below. Some employers also have rules about Chinese AI providers, so check yours before you paste in company material.
How to get better results from GLM
- Pick the right model for the job. Use GLM-5.3 for hard coding and long reasoning, and GLM-5.3-Flash for quick tasks or anything with images.
- Set the effort level on purpose. In the API the default is max, and Z.ai ran its coding benchmarks at max, but low is far faster for simple rewrites or classification.
- Give it the whole context. With 1 million tokens available, paste the full specification, the relevant files and the error log rather than a summary. Long-context models do better with the real material.
- Ask for a plan before code. For multi-step work, ask GLM to list the steps and the files it will touch, approve the plan, then let it run. It is far easier to correct a plan than a finished change.
- Schedule heavy runs off-peak. On the Coding Plan, the same job costs half the credits outside the weekday peak window.
GLM privacy settings and where your data goes
Z.ai's international services are run by a Singapore company, and its privacy policy says your data is generally processed in Singapore. The same policy lists training and improving its models as one of the ways it uses personal data, and we found no setting in the app that turns this off. The policy describes your right to object or withdraw consent by contacting Z.ai, and API customers are covered by a separate Data Processing Addendum for API Services.
Our advice is simple. Do not put confidential customer, financial or personal data into the hosted chat app. If you want GLM for sensitive work, run the open weights on infrastructure you control, or use them through a cloud provider your company has already approved.
Which way of using GLM suits whom
- Curious individuals: try the chat app at chat.z.ai to see how GLM-5.3-Flash handles your everyday tasks, and keep sensitive material out of it.
- Developers who live in a coding agent: the GLM Coding Plan (Lite, Pro or Max) is the best value way to use GLM-5.3 every day. Start on Lite and move up only if you hit the 5-hour or weekly limits.
- Product teams building features: the API, which speaks the OpenAI and Anthropic formats, so switching an existing app is mostly a change of address and model name.
- Companies with strict data rules: the open weights. It takes engineering time, but it is the only route where nothing leaves your network.
Not sure GLM is the right model for your team? Compare it with the alternatives on our comparison tool, see the best AI for coding, or start with the short lessons at Learn GLM.
Prompts to copy
Plan a change before writing code
You are a senior engineer. Here is our task: [describe the feature]. Here are the relevant files: [paste files]. Before writing any code, list the steps you will take, the files you will change and the risks. Wait for my approval before you start.
You get: A reviewable plan you can correct before any code changes
Build a small script, explained
Write a Python script that reads [orders.csv] and emails me a daily total of [column]. Explain each step in plain English, list the libraries I need to install, and add a test I can run.
You get: Working code plus the explanation to maintain it
Debug from the full log
Here is the complete error log and the code it points to: [paste]. Find the root cause, not just the line that failed. Give me the fix, explain why it works, and tell me how to stop it happening again.
You get: A root-cause fix rather than a patch
Review a pull request for security
Review this diff as a security reviewer: [paste diff]. List any vulnerabilities by severity, explain how each could be exploited, and suggest a fix. Say clearly if you find nothing serious.
You get: A ranked list of security issues with fixes
Summarise and translate
Summarise this article in five bullet points for a busy manager. Then give me the same summary in Chinese, keeping product names in English. [paste article]
You get: A short bilingual summary ready to share
Compare suppliers in a table
From the information below, compare these suppliers of [product] on price, minimum order, delivery time and certifications. Put the result in a table and flag anything missing. [paste supplier details]
You get: A clean comparison table with gaps flagged