[GLM-5 Arrives!] How has that super cost-effective AI evolved? Behind the scenes of its overwhelming performance and cost
Have you all been making full use of Zhipu AI's "GLM-4.7," which shook the AI world with its overwhelming cost-performance?
As someone who runs the brand Miccell, I've been a paying user of GLM-4.7, running it at full capacity every day and relying on it as my partner for vibe coding (coding by feel).
The successor to our reliable partner has finally been unveiled!
Beijing-based Zhipu AI (international brand name: Z.ai) has officially released its 5th generation flagship model, "GLM-5."
It seems this GLM-5 isn't just a "slightly smarter, cost-effective update."
The era seems to have entered a new dimension, moving from the phase of "having AI write parts of code" to "entrusting entire system construction projects to AI," known as Agentic Engineering.
Because I'm Miccell, someone who pays for and plays with GLM-4.7 every day, I'm going to thoroughly analyze the essence of this evolution and the "cost side of things" that everyone is most curious about today.
If you read this, you'll understand the true greatness of GLM-5 and how to wisely interact with this monster AI within the new pricing structure, which could be considered a substantial price hike, so please enjoy it until the end!
The legitimate successor to the super cost-effective AI that shook the industry
Let's first explore the background of how this GLM-5 has evolved further from GLM-4.7, which could be said to have overturned the common sense of AI development.
The "GLM-5" newly announced by Zhipu AI is designed as an existence that completely transcends the framework of conventional "conversational AI" or "code generation AI."
🚀 The previous generation that took the world by storm with overwhelming cost-performance
Let's turn back the clock a little bit.
When the previous generation model, GLM-4.7, appeared, the industry was stunned by its overwhelming cost-performance.
It broke the conventional wisdom of "you get what you pay for," and while the API usage fees were incredibly low, it handled daily programming and text generation at a very high level.
I've also really been helped by GLM-4.7, and I was enjoying days of "Vibe Coding," where I could just give rough instructions like "I want to make an app like this," and it would output the necessary code one after another.
GLM-4.7 was truly like a "magic wand" for individual developers and light users like us.
🏢 Evolution from a laboratory to a giant tech company
So, is Zhipu AI, which makes such amazing AI, just a flash-in-the-pan startup?
Actually, it's completely different.
Their intellectual origins trace back to the highest level of academia in China: the Knowledge Engineering Group (KEG) at Tsinghua University's Department of Computer Science, established in 1996.
For many years, they have been conducting research not just on the statistical processing of vast amounts of data, but on enabling AI to understand it as meaningful, structured 'knowledge'.
This philosophy of being 'knowledge-driven' rather than 'data-driven' flows through the foundation of Zhipu AI.
Then, in 2026, they successfully completed a historic IPO on the Hong Kong Stock Exchange, growing into a massive tech company with a market capitalization exceeding $19 billion.
GLM-5 is the crystallization of 'national representative' class intelligence that they have brought to the world with great anticipation.
An astonishing architecture supporting a 744 billion parameter brain
When talking about the capabilities of GLM-5, what cannot be left out is its overwhelming scale and the sophisticated mechanisms used to run it efficiently.
You might feel a bit dizzy just hearing the numbers, but there are some very interesting mechanisms hidden inside, so let's take a look together.
🧠 Balancing massive knowledge with agile reasoning
The total parameter count of this GLM-5 has been expanded to 744 billion (744B), which is about 2.1 times that of the previous generation.
With such a massive brain, you would normally think, 'Doesn't it cost a huge amount of electricity and computing power just to run it, making it impossible to put into practical use?'
However, GLM-5 adopts an architecture called 'Mixture-of-Experts (MoE)'.
This is a mechanism where, instead of keeping all 744 billion parameters running at full capacity, only the necessary 'expert' parameters are called upon during inference.
As a result, the active parameters actually in operation are kept to just 40 billion (40B).
In other words, it possesses a highly fuel-efficient brain that holds the knowledge of a massive library while only pulling out and reading the books it needs.
⚡ Latest attention technology that holds the key to efficiency
Even more surprisingly, GLM-5 has cleverly adopted a technology called 'DeepSeek Sparse Attention (DSA),' which was pioneered by its rival, DeepSeek.
When reading long texts, normal AI tries to calculate the relationship between every single word, so the amount of computation explodes as the context gets longer.
However, by using this DSA technology, it can focus its attention only on important information, which can dramatically reduce the amount of computation required.
The attitude of flexibly adopting even the excellent technologies of other companies for the sake of practical benefits speaks to the shrewdness of Zhipu AI.
🔄 A reinforcement learning mechanism that enables self-correction
And there is another secret to GLM-5's "intelligence": its unique asynchronous reinforcement learning framework called "slime".
Training a massive AI is extremely difficult, but by using this "slime" mechanism, the AI doesn't just memorize the "correct answer"; it learns to use trial and error to figure out the logical reasoning steps behind "why that answer is correct."
It's as if it has acquired a persistent thought process, like writing out the intermediate steps of a math problem and erasing and rewriting them if it makes a mistake, rather than just copying the final answer from a test.
This "ability to think and correct" is the source of GLM-5's high agentic capabilities.
Agentic engineering: Evolving from coding to delegating entire systems
Now, here is the most exciting part.
GLM-5 introduces a new paradigm called "Agentic Engineering," which might fundamentally change the way we develop software.
🛠️ Graduating from vibe coding
With the "vibe coding" we've used until now, we humans would ask an AI to "write a Python function with these features," then copy and paste the resulting code snippets into our development environments to test them.
In other words, the AI was merely a "talented coder," and it was the human's role to grasp the big picture of the project and assemble it.
But the Agentic Engineering proposed by GLM-5 is different.
Users only need to throw a rough goal at it, such as "build a vending machine management system."
The AI then thinks through the entire system design, creates the necessary folder structure, implements the frontend and backend code, autonomously debugs any errors that arise, and even generates the documentation at the end.
It feels less like hiring an "assistant" and more like hiring an entire, fully autonomous development team.
🤖 Autonomous thinking power capable of running a virtual business
There is an episode that proves the greatness of this agentic capability.
In a simulation test called "Vending-Bench 2," where an AI is tasked with running a virtual vending machine business for a year, GLM-5 achieved the top score among open-source models, generating $4,432.12 in revenue.
This isn't just about the ability to fix program bugs.
It means the AI can make "strategic decisions" from a long-term perspective, such as placing orders when inventory runs low, changing the product lineup when the seasons change, and adjusting prices if it looks like it might go into the red.
It's truly a terrifying evolution that it can not only handle immediate tasks but also act autonomously while considering the overall optimization of a business.
The end of free token giveaways and the truth about changing cost structures
However, with such incredible capabilities, the topic of "money" is inevitably unavoidable.
Zhipu AI was famous for its "generosity," distributing an astronomical free quota of 20 million tokens to new registrants.
💸 Transitioning from a phase of cost-supremacy
In fact, now that they have listed on the Hong Kong Stock Exchange and released GLM-5, those unconditional free campaigns are heading toward reduction and termination.
Having become a publicly traded company, they have entered a phase where they must recover R&D costs and generate solid revenue.
For users like us who have been using the free quota to build and play with prototypes, this might be news that makes us brace ourselves, thinking, "Has the substantial price hike finally arrived?"
🔄 Smart routing strategy using cache utilization
You might be thinking, "So, can we no longer use AI casually?" but there is a rather interesting strategy hidden there.
The API pricing for GLM-5 is set with output costs significantly higher than input costs.
But in exchange, they are taking measures such as making the storage cost for cached inputs free and so on.
Since agents autonomously repeat trial and error many times, they end up having the AI read the same context, such as a codebase, repeatedly.
If you use this "cache" effectively, you can suppress explosive token consumption while drawing out high-level reasoning at a low cost.
Furthermore, the lightweight model "GLM-4.7-Flash" continues to be provided completely free of charge.
In other words, you leave simple tasks like quick translations or summaries to the free Flash model, and offload only heavy work like complex system construction to GLM-5.
I think the ability to design this kind of "model routing" will become the most important skill for AI users from now on.
A management perspective on riding the wave of massive intelligence
We have looked at the evolution and cost structure of GLM-5 so far, but how should we go about accepting this massive intelligence?
I would like to think about the future brought about by this AI shift from a slightly broader perspective.
🌍 New options brought by on-premises operation
First of all, when trying to use AI in a business setting, the issues of "security" and "confidentiality" always become a barrier, don't they?
I think many companies are afraid to send their confidential data to external servers, no matter how convenient the AI is.
In fact, the model weights for GLM-5 are released as open source under the MIT license, which is extremely flexible.
In other words, if the conditions are met, you can install this 744 billion parameter brain within your own company's servers (on-premises environment) and run it in a completely secure state, isolated from the outside.
This can be said to be a decisive strength that major Western AI models, which can only be used via the cloud, do not have.
You can minimize the risk of information leakage to near zero while customizing a cutting-edge agent AI as your own dedicated team.
Especially in corporate environments with strict security requirements like in Japan, I think the significance of having this option available is huge.
🤝 The future form of co-creation between humans and AI
And another thing, when autonomous agents like GLM-5 appear, you always hear anxious voices asking, "Won't this take away programmers' jobs?"
Certainly, the task of "writing code as instructed" that has existed until now may be largely replaced by AI.
But I don't think that means human jobs will disappear at all.
Rather, it should lead to a demand for higher-level, "human-like" creative management skills, such as defining project requirements and drawing the big picture: "What kind of system is needed?" and "Whose problems do we want to solve?"
Also, people who want to easily create prototypes and play around can continue to enjoy Vibe Coding with lightweight models like GLM-4.7 as before.
Those who want to build serious commercial systems can make full use of GLM-5 as a project manager while managing costs.
Because technology has advanced, our options haven't been taken away; we just have a richer set of tools to choose from depending on our goals.
Instead of fearing AI as an "enemy that steals jobs," how do we engage with it as the "ultimate partner that expands our capabilities"?
I think that shift in mindset will be the passport to surviving in the coming era.
Production Notes
Phew, while reading the strategic evaluation report of GLM-5 from cover to cover, I was once again overwhelmed by the speed of AI evolution.
I really felt firsthand that we have moved from an era of cost-performance supremacy—where things were 'cheap and reasonably smart'—to a new phase where we are willing to pay a little more to get a partner that can think completely autonomously.
From now on, it won't be about 'how to give instructions (prompt engineering)' to AI, but rather about management sense—specifically, 'what to delegate to the AI and to what extent.'
I'm also planning to start by handing an entire small-scale tool development project over to GLM-5 and carefully observing its 'thought loop' to see how it acts autonomously.
After all, the best way to go is to enjoy the process of co-creation with AI, including those little silly mistakes, like forgetting to draw the webbed feet when you ask it to draw a pelican!
Summary and Future Actions
What did you think of the world of 'Agentic Engineering' brought to us by GLM-5?
From here on out, it's going to be an era where our productivity is determined not just by simple cost-performance, but by our ability to understand AI's characteristics and our 'power to delegate.'
What kind of work would you all like to delegate to these autonomous agents?
Boring daily administrative tasks? Or perhaps bringing to life that app idea you've been keeping on the back burner for so long?
Please share your exciting AI utilization ideas in the comments section!
Miccell - Maybe next time I'll try handing off the entire tedious end-of-month expense reporting system to it so it can do it all automatically for me (lol).