What Is GLM? Every Zhipu AI’s Open Model Explained

GLM is the family of large language models built by Zhipu AI, the Chinese lab now trading as Z.ai. If you typed “what is GLM” expecting the statistics term or the Bosch laser measure, this is the other one: GLM is a direct open-weight rival to ChatGPT, Claude and Gemini, and its current flagship GLM-5.2 scores high enough on independent benchmarks to sit in the same conversation as models that cost six times more to run.

The reason GLM matters to you is simple. Every model in the family is open-weight and MIT-licensed, so anyone can download it, run it, or build on it for free, and the paid API costs a fraction of what the big US labs charge. This guide explains what GLM is, who makes it, how the versions differ from GLM-4.5 through GLM-5.2, what it costs, and which one you should actually use. For a benchmark-by-benchmark look at how GLM compares to Claude, GPT, Gemini, Grok and DeepSeek, we have a full head-to-head.

The Key Takeaways

• GLM = General Language Model, a family of open-weight AI models from Zhipu AI (now Z.ai), first introduced in 2021 at Tsinghua University.
• Every GLM model is open-weight and MIT-licensed, free to download on Hugging Face and far cheaper than ChatGPT or Claude via API.
• The current flagship GLM-5.2 (June 2026) runs a 744B-parameter mixture-of-experts design with a 1 million-token context window at roughly one-sixth the price of GPT-5.5.
• GLM-4.7 is the budget coding default at 73.8% on SWE-bench Verified, one of the best scores any open model has posted.
• Quick verdict: pick GLM-4.7 for cheap coding, GLM-5.2 for the frontier flagship, and GLM-4.7 Flash if you want to run a model locally.

What Does GLM Stand For?

GLM stands for General Language Model. It is the name of the model family from Zhipu AI, first introduced in a 2021 research paper by a team at Tsinghua University. The name is not related to the “generalized linear model” used in statistics, nor to the Bosch GLM line of laser measuring tools, even though those share the same acronym.

What made the original GLM distinctive was its training method. Instead of the pure left-to-right approach most chatbots use, GLM was built on an autoregressive blank-infilling objective that blends bidirectional and causal attention in a single model. That technical heritage runs from the early GLM-130B and ChatGLM releases all the way through to today’s frontier models.

Who Makes GLM?

GLM is built by Zhipu AI, a lab spun out of Tsinghua University’s Knowledge Engineering Group. In 2025 the company rebranded its consumer-facing brand to Z.ai, and you can use the models directly at chat.z.ai or download the weights from Zhipu’s open model repository on Hugging Face.

The company became hard to ignore in January 2026, when it completed a Hong Kong IPO that raised roughly $558 million, making it one of the first listed large-language-model companies in the world. Zhipu’s strategy is consistent; it trades a little frontier performance for fully open weights, aggressive pricing, and “sovereign” training on Chinese hardware rather than Nvidia chips. That open approach is why GLM keeps showing up on lists of the best open source AI models.

Every Version Compared

GLM moves fast, with a new release roughly every few months. The table below covers the whole current stack so you can see where each model fits without hunting through release notes.

Model	Released	Params (MoE)	Best for	Status
GLM-4.5 / 4.5 Air	2025	355B / lightweight	Agentic and reasoning baseline; Air runs lean on modest hardware	Superseded
GLM-4.6	2025	355B	Coding workhorse with a 200K context, cheapest per token	Legacy
GLM-4.7	Dec 22 2025	355B	Budget coding default, 73.8% on SWE-bench Verified	Current 4.x
GLM-4.7 Flash	Early 2026	30B dense	Local and consumer-hardware coding, around $0.60 per million tokens	Current lite
GLM-5	Feb 13 2026	744B (44B active)	First open flagship, trained fully on Huawei Ascend chips	Superseded
GLM-5.1	Apr 2026	754B	Long-horizon agentic work, around 94% of Claude Opus on coding	Superseded
GLM-5.2	Jun 13 2026	744B (40B active)	Frontier flagship, 1M context, roughly one-sixth of GPT-5.5’s cost	Current flagship

GLM-4.5 and GLM-4.5 Air (2025)

The GLM-4.5 generation was the family’s first big mixture-of-experts release, a 355B model that unified reasoning, coding and tool use in one system. GLM-4.5 Air is the lightweight sibling, trimmed to run on more modest hardware while keeping most of the agentic skills. Both are superseded now, but they laid the groundwork for everything that followed.

What set GLM-4.5 apart was its hybrid reasoning design, offering a slow thinking mode for hard problems and a fast mode for quick replies inside one model. Because the weights shipped under the Licence MIT, developers could fine-tune and self-host it with no restrictions, and the lean Air variant brought that within reach of a single high-end GPU. This is the release that turned Zhipu from a research name into a serious open-weight contender.

GLM-4.6 (2025)

GLM-4.6 is the coding workhorse that earned the family its budget reputation. It kept the 355B MoE design, stretched the context window to 200K tokens, and undercut almost every rival on price per token. It is now legacy, but still turns up in older coding setups.

That 200K-token window meant GLM-4.6 could hold an entire mid-sized codebase in working memory, which is what won it a following among developers building agents on a budget. It paired the long context with solid tool-calling and a token price that few Western models could match. Anyone still running it gets a capable coder for next to nothing, though GLM-4.7 lifted accuracy enough to make the upgrade worthwhile.

GLM-4.7 (December 2025)

Released on December 22, 2025, GLM-4.7 is the current 4.x flagship and the default model inside Zhipu’s coding plan. It scores 73.8% on SWE-bench Verified, a roughly 6-point jump over GLM-4.6 and one of the strongest results any open-weight model has posted. If you want serious coding on a tight budget, this is the one to reach for.

Beyond the headline number, GLM-4.7 sharpened the multi-step agentic work and front-end generation where earlier GLM models trailed Claude. It is the engine that powers most of Zhipu’s GLM Coding Plan, so a lot of subscribers are running it without realising. For everyday development it lands in the sweet spot of strong output and rock-bottom cost, which is why it stays our pick for budget coding.

GLM-4.7 Flash (early 2026)

GLM-4.7 Flash is the version to run on your own machine. It is a smaller 30B dense model with a 128K context and pricing near $0.60 per million tokens, tuned for fast, cheap, local coding rather than frontier reasoning.

Because it is a dense 30B model rather than a sprawling mixture-of-experts, Flash fits comfortably on a single consumer GPU and replies almost instantly. That makes it the natural choice for offline work, private codebases, or anyone who would rather not send prompts to a server at all. You give up frontier reasoning, but for autocomplete, refactoring and quick scripts it is more than enough.

GLM-5 (February 2026)

GLM-5 was the family’s first true open frontier model. The 744B mixture-of-experts design activates only 44 billion parameters per query, and Zhipu trained it entirely on Huawei Ascend chips without a single Nvidia GPU, a landmark for Chinese AI independence. It still shipped under the permissive MIT license.

Training a model this large end to end on Huawei Ascend silicon was the real story, proving a frontier system could be built with zero Nvidia hardware just as US export controls tightened. Meituan would later take the same no-Nvidia route with its trillion-parameter LongCat-2.0. The sparse design keeps serving costs down by waking only 44 billion of those parameters for any given query. GLM-5 has since been overtaken by 5.1 and 5.2, but it marked the point where China’s open models reached genuine frontier scale.

GLM-5.1 (April 2026)

GLM-5.1 was a fast follow-up focused on long-horizon agentic work, nudging coding quality up to around 94% of Claude Opus while keeping the open weights. It has since been overtaken by 5.2.

The focus this time was stamina rather than raw scores, with 5.1 holding its reasoning together across the long agentic chains where earlier versions drifted. Closing to roughly 94% of Claude Opus on coding put an open-weight model within touching distance of the US frontier for the first time. It was a short-lived flagship, replaced inside two months, but it set up the 1 million-token leap that 5.2 went on to deliver.

GLM-5.2 (June 2026)

GLM-5.2 is the current flagship, released on June 13, 2026. It quadruples the context window to a full 1 million tokens, keeps long-context inference affordable, and ranks ahead of rivals like DeepSeek and Kimi on independent intelligence indexes. We cover its benchmarks and limits in depth in our dedicated GLM 5.2 breakdown. The next release, GLM 5.5, is reportedly expected around August 2026.

The other headline is efficiency. Zhipu trimmed the active parameter count to just 40 billion, which is how it serves that million-token context without the price ballooning. Pair frontier-class reasoning with a cost near one-sixth of GPT-5.5 and you get the value flagship of the open-weight world, the model that anchors this whole family.

Is GLM Any Good Compared to ChatGPT and Claude?

Yes, with honest caveats. GLM-4.7’s 73.8% on SWE-bench Verified is among the best of any open model, and GLM-5.2 outscores DeepSeek and Kimi on broad reasoning tests. For everyday coding, writing and research, you would struggle to tell a GLM response apart from a frontier US model.

The catch shows up on the hardest agentic and long-horizon tasks, where Claude Opus a GPT-5 still hold a measurable lead. The trade is cost: GLM delivers most of that capability while being open-weight and far cheaper to run, which is exactly why it has become the value pick of the open source AI world. If you want a real head-to-head, that is its own deep dive rather than a paragraph here.

How Much Does GLM Cost?

GLM is one of the cheapest serious models to run. You can use it free at chat.z.ai, and on the API GLM-5.2 runs around $1.40 per million input tokens and $4.40 per million output, roughly one-sixth of what GPT-5.5 charges. The older GLM-4.7 is cheaper still. Our full GLM pricing breakdown covers every model, the exact API rates and the Coding Plan tiers.

For heavy coders, Zhipu sells a separate GLM Coding Plan with three tiers, Lite at $18, Pro at $72, and Max at $160 per month, often with an introductory discount. The plans bundle GLM-5.2, GLM-5-Turbo and GLM-4.7 with generous prompt allowances, which is what made the subscription go viral with developers hunting for a cheaper Claude alternative.

Which Model Should You Use?

For most people the answer is short. Pick GLM-4.7 if you want strong coding on the smallest possible budget, choose GLM-5.2 if you want the frontier flagship with a huge context window, and grab GLM-4.7 Flash if you need a model small enough to run on your own hardware.

The faster route is to skip the per-model decision entirely. Inside Fello, GLM-5.2 sits alongside other frontier models like Claude, GPT, Gemini and more for a single $9.99 a month, so you can run the same prompt through several of them and keep whichever answer is best, with no separate z.ai account and no per-token math.

Závěr

GLM is Zhipu AI’s family of open-weight large language models, and it has quietly become the default answer for anyone who wants frontier-class output without frontier prices. From the budget GLM-4.7 to the 1-million-token GLM-5.2 flagship, the whole line is open, cheap and genuinely competitive.

If you want to see how it stacks up against the models you already pay for, the easiest next step is to run GLM and Claude side by side on the same prompt in Fello and judge the answers yourself.

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