Use Intelligence as a 'Commodity'! The Game-Changing Impact of GLM-4.7 on Software Development
You know, lately I've been completely sucked into the 'vibe coding' rabbit hole.
I feel like I'm talking to my editor from the moment I wake up until I go to sleep.
I just throw a prompt at the AI like, 'Make me something that works quickly with this kind of vibe,' and the UI is built in an instant.
Once you experience this feeling of 'thoughts turning directly into form,' you can never go back to regular development.✨
But, as you really start using AI agents, there's a 'wall' you inevitably hit.
When you try to get them to implement complex features, have you ever had them say, 'Wait, did you forget the DB design we decided on earlier?' or,
found yourself thinking, 'That fix contradicts the previous step!'?🤔
It's what's known as 'agent drift.' When this happens, all that good vibe goes to waste.
A new star has emerged to solve our frustration by addressing the fundamental 'structure' of the problem.
That is the latest flagship model series from Zhipu AI, GLM-4.7.
If you think this is just another 'cheap and fast model,' you're making a huge mistake.
It's packed with incredible mechanisms designed to treat intelligence as a 'commodity' while keeping it 'smart' to use to its full potential.
Today, I'm going to thoroughly dissect how this GLM-4.7 will rewrite our development workflows from the Miccell perspective!🚀
Saving brain cells? The revolution of 'persistent reasoning' brought about by GLM-4.7
The biggest reason AI agents get lost halfway through is that traditional LLMs 'reset their thinking every turn.'
For a human, that's like being forced to work while having your memory reset every minute.
It's no wonder they lose sight of the goal during long development tasks.💦
🧠 The power of 'Preserved Thinking' that doesn't forget
The most innovative mechanism introduced by GLM-4.7 is 'Preserved Thinking.'
This is a feature that persists the reasoning process—the 'why' behind the model's decisions—within the context throughout multi-turn conversations.
Because of this, even after running dozens of revision loops, the model doesn't forget the original design intent or the fine-grained requirements.
It's the core system that allows agents to maintain their 'intent.' 💡
💡 'Turn-level Thinking': Controlling the Depth of Thought
What's even more interesting is a feature called 'Turn-level Thinking,' which allows the depth of reasoning to be dynamically adjusted based on the complexity of the query.
When you say, 'Think deeper and debug this part!', you can make it reason at full power,
while simple tasks like code formatting are finished quickly with low latency.
Because it can optimize resource allocation like this, your development rhythm never gets disrupted. ✨
✅ Overwhelming Improvement in Instruction Following Ability
With the implementation of this 'thinking mode,' GLM-4.7's ability to follow instructions has improved dramatically compared to the previous generation.
In particular, on 'SWE-bench Verified,' which measures performance as a programming agent, it has achieved a score of 73.8%, the highest level among open-source models.
This puts it on par with cutting-edge closed models like Claude 3.5 Sonnet and GPT-4o.
The Magic of MLA Technology Supporting a Vast 200k Token Field of View
When you're developing, there are times you want to have the model read the entire project's files, right?
But until now, the conventional wisdom was that the wider the context, the more absurdly it would consume server memory (VRAM).
GLM-4.7 broke that conventional wisdom with the magic of 'MLA (Multi-Latent Attention).' 💡
🌊 Overwhelming Compression of KV Cache
Simply put, MLA is a technology that 'dramatically reduces the memory footprint used during inference.'
To process a 200k token context, you would have previously needed a massive 94GB of VRAM,
but with this MLA, GLM-4.7 has compressed that down to about 25GB. 🚀
💻 'Full Loading' in Local Environments Becomes Realistic
Thanks to this compression, it has become realistic to load entire large-scale codebases onto custom PCs equipped with RTX 4090s like the ones we use, or
even on Macs with Apple Silicon.
Even projects that are 'too big to send to the cloud' can now be entrusted to autonomous agents in your local environment while maintaining privacy.
I believe this level of freedom will become an irreplaceable asset for developers.🤝
📊 Acceleration via Mixture of Experts (MoE)
Although the flagship GLM-4.7 model is a massive model with over 350 billion parameters,
it actually adopts a 'Mixture of Experts' (MoE) architecture, where only a portion is active at any given time.
That is why it has a vast amount of knowledge while maintaining lightning-fast response times.
The 'speed of thought' that keeps development moving is right here.✨
A shock starting at $3 per month. The economic efficiency of making intelligence 'disposable'
Honestly, the thing that left me speechless and thinking 'Are you serious...' with this announcement was the pricing structure.
I feel like I've witnessed the moment intelligence shifts from a 'luxury item' to an 'infrastructure' like water or electricity.✨
💳 'GLM Coding Plan' for Developers
The 'GLM Coding Plan' launched by Zhipu AI is at a level where even the term 'price destruction' feels like an understatement.
Lite Plan (from $3/month): Approximately 120 prompt slots every 5 hours.
Pro Plan (from $15/month): Approximately 600 slots every 5 hours. Ideal for complex development.
Max Plan (from $30/month): Approximately 2400 slots every 5 hours. Handle large-scale development with just this.
When you calculate this, it means that with the Max Plan, you can run AI for hundreds of billions of tokens per month.
You can turn a volume that would cost hundreds of thousands of yen with standard API billing into a subscription for just a few thousand yen.
You no longer have to hit the brakes on yourself, thinking, 'I'll stop my research here because the API costs are too high.'🚀
💰 API Pricing That Leaves Competitors Behind
Usage-based billing via API starts at $0.60 per million tokens, which is about one-fourth the cost of GPT-4o.
Furthermore, with 'GLM-4.7-Flash,' API usage can effectively be free.
Development approaches like 'Let's just try 100 different automated debugging attempts'—essentially overpowering problems with sheer volume—are now within reach for us individual developers. 🙆♂️
🌍 Challenging the Global Market
This pricing strategy is clearly aimed at Western giants like OpenAI and Anthropic, isn't it?
As intelligence becomes cheaper, systems that prioritize volume—like 'fully automated CI/CD loops' that were previously abandoned due to cost,
or '24-hour full-scale log monitoring by AI'—are bound to emerge one after another.
I can truly hear the sound of the game's rules changing. 🚀
The New Normal of 'Parallel Development' Pioneered by Ghostty and OpenCode
Let me share some of the latest information here.
As of January 2026, there is a workflow that is being enthusiastically supported among developers.
That is parallel development using Ghostty × OpenCode × GLM-4.7.
🖼️ Multi-pane Orchestration with Ghostty
Ghostty is an ultra-fast terminal that makes full use of GPU acceleration.
What's great about it is that it doesn't slow down at all, even when you line up a large number of panes.
You keep 'GLM-4.7 instances with different roles' running via OpenCode in each of these multiple panes. ✨
Pane 1: A 'Manager' that grasps the overall project structure and assigns tasks
Pane 2: A 'UI Craftsman' that builds React components for the frontend at lightning speed
Pane 3: A 'Logic Specialist' that writes backend logic and database integrations
Pain 4: The 'QA Engineer' that constantly runs test code and keeps proposing fixes
🚀 Blazing-fast feedback at 1,000 tokens per second
When running on Cerebras chips, GLM-4.7 spits out text at speeds ranging from 1,000 to a maximum of 1,700 tokens per second.
There is no need to wait for a human to type anymore. 💡
With four panes, AIs write code simultaneously, and pull requests pile up in an instant.
A single human becomes the conductor (orchestrator), leading multiple AI subordinates—the 'one-person development team' is complete. ✨
🛠️ Leveraging the OpenCode Ecosystem
By using OpenCode or its plugin 'oh-my-opencode',
you can also integrate with external tools and browser operations via MCP (Model Context Protocol).
The way instructions like 'Look up this document online and fix the code to match it' are
passed between multiple agents in a relay format is truly the future of development sites. 🚀
Insight: The value of AI is shifting from 'IQ' to 'Orchestration'
Let me go a bit deeper here and share my own thoughts on the 'essence of change' we are facing in 2026.
What the arrival of GLM-4.7 suggests is that'the commoditization of intelligence'has entered its final phase.
It is no longer a surprise that AI is smart.
The differentiator from here on out lies on our side as humans: 'how we orchestrate that intelligence.' 🤔
🎼 From 'Instrument' to 'Orchestra'
Until now, we competed on 'how to hire one smart person' (which model to use).
But from now on, the game will be about 'designing the system'—how to manage 100 cheap, reasonably smart people.
Instead of relying on one high intelligence, we will run multiple specialized agents in parallel,
Integrate those outputs to build a massive system.
This 'Volume x Command' is likely to become the core competence for engineers from here on out. 💡
🧠 'Trust' as a Mechanism
What GLM-4.7 provides through 'Preserved Thinking' isn't just a high IQ.
It's a structural 'reliability' where agents 'don't give up halfway and don't forget.'
When reliable resources become available at a low cost, what we should be doing is,
not writing 'commands' called prompts, but writing 'scores' called workflows. 🤝
🚀 Beyond Vibe Coding
When combined with this parallel development, 'vibe coding'—creating UIs intuitively—will reach a whole new dimension.
One person conveys the vibe, multiple AIs shape it within their respective areas of expertise,
and an integrated product is launched in minutes.
We are standing in an era where we can manifest our passion to 'create!' with unprecedented purity. ✨🚀
Who Should Adopt It, and 4 Scenarios Where It Shines in Practice
In what specific situations does this 'potent drug' called GLM-4.7 'hit the mark' the best?
I'll introduce four scenarios directly linked to practical work that I recommend. ✅
🌟 End-to-End 'Autonomous Feature Implementation'
Tasks that used to take days, like 'build an API with authentication, DB integration, and an admin dashboard.'
With GLM-4.7, from defining specifications to breaking down tasks, actual coding, and creating test code,
it completes the entire flow at once without 'forgetting' anything.
If you're launching a service on your own, there's no partner more reassuring than this. 💡
🌟 24/7 Non-stop 'Autonomous Debugging & Refactoring'
Keep an agent resident in your development repository,
and have it constantly think, 'Is there a more efficient way to write this?' or 'Are there any hidden vulnerabilities?'
Since API costs are incredibly low, running a server 24/7 costs next to nothing.
You get a magical development experience where 'code polishes itself while you sleep.' ✨
🌟 'Intelligent Research' from Massive Documentation
Fully utilize the 200k token window to ingest thousands of pages of technical specifications or academic papers at once.
On top of that, you can instantly complete research that feels like finding a needle in a sea of information,
such as asking, 'List the three most critical risks when using this new API.' 🚀
🌟 'Rapid Prototyping' with Aesthetic Sense
GLM-4.7 also has an excellent sense for UI design.
Just say, 'Make me a stylish dashboard that looks like Apple and has a sense of transparency,'
and it will instantly convert that vague 'vibe' into sophisticated code using Tailwind CSS or React.
Even teams without a designer can achieve a high-speed, 'Instagrammable' product launch. ✨
Recommended Strategies by Target and 'Considerations' You Should Know
Finally, I'll touch on how you, the reader of this article, should act based on your position,
and also on the 'considerations' regarding the impact this technology brings.
To Individual and Small-Team Developers
The days of worrying about 'Claude's limits...' are over. Use GLM-4.7 for 'primary responses' in high volume, and adopt a 'hybrid strategy' where you only use Claude 4.5 for the most difficult finishing touches. This should allow you to keep monthly costs down while boosting productivity tenfold. ✨
To Enterprise Decision Makers
In an era where privacy and governance are paramount, the open-weight GLM-4.7 is a one-of-a-kind choice.
You can unleash cutting-edge coding agents to every employee while keeping everything contained within your own infrastructure.
Avoid vendor lock-in and inject your company's domain knowledge through fine-tuning,
I want you to push for an 'offensive' digital transformation. 🚀
To those feeling anxious that 'AI will take your job'
Admittedly, simple coding tasks might be replaced by AI.
But as I wrote in this article, the protagonist from here on out is the 'AI-conducting orchestrator'.
If you think of it as having more hands to write code, your creativity is actually being liberated.
This will be an era where those who cherish the uniquely human question of 'what do I want to solve?' will shine the brightest. 🙆♂️
Production Note: The 'Commoditization of Intelligence' I Felt While Using GLM-4.7
Honestly, the thing that shook me most while using GLM-4.7 wasn't the intelligence itself, but the fact that we have been placed in a phase of 'how to conduct intelligence'.
Until now, we were competing over 'how smart the AI is,' but from here on, the battle will be about 'how to incorporate cheap, smart intelligence in parallel and in large quantities into appropriate workflows.'
In 2026, when IQ becomes a commodity (a daily necessity), what will be required of us is the ability to write the best 'score (process)' to play the best 'instrument.' 🤔
It feels a bit like the era when people said, 'Once PCs become widespread, everyone will become a programmer.'
But in reality, everyone started using the 'instrument called a PC' to play their own lives.
I'm sure the same will happen with AI.
I intend to enjoy and use this new 'mechanism' more than anyone else! ✨🚀
Summary: Toward an Era of Orchestration Where You 'Conduct' AI
GLM-4.7 is not just a model update.
It is a powerful engine for us to 'run a team by ourselves'.
A mechanism to 'save thoughts' and the economic efficiency to attack with 'sheer volume'.
When these two come together, the landscape of development should change completely.
The days of skimping on 'smart intelligence' because you were worried about high API costs are now a thing of the past. 🤝
What kind of parallel development would you like to try with this 'AI that never forgets'?
Please tell me your 'ultimate team configuration' that would fill up the Ghostty panes in the comments!
If you have any interesting ideas, I'd love to try them out with you too! 💡🤝
Miccell - Let's set the next vibe with parallel multi-agents.