If 2024 was the year of the generic chatbot, 2025 has effectively become the year of the “Thinking” model. While OpenAI and Anthropic have kept their reasoning chains hidden behind API curtains, Zhipu AI has just kicked the door open. GLM 4.7 isn’t just an upgrade; it’s a philosophical shift.
By exposing Interleaved Thinking and Preserved Reasoning states, it forces us to ask: What happens when an open-model can “pause and reflect” just as effectively as the best closed models?
Beyond Coding: True Agentic “Thinking”
The headline feature here is Agentic Task Completion. Most models treat coding as a “fill in the blank” exercise. GLM 4.7 treats it as a multi-step logic puzzle.
The Three Modes of Thought
1. Interleaved Thinking: The model reasons before every single action. Before it runs a terminal command, it writes a short paragraph explaining why. This sounds minor, but in practice, it reduces “lazy dev” errors by forcing the model to validate its own assumptions.
2. Preserved Thinking: This is the game-changer for long sessions. Instead of recalculating its “plan” from scratch every turn (the “goldfish memory” problem), GLM 4.7 retains its reasoning artifacts. It remembers why it decided to refactor that database schema five turns ago.
3. Turn-Level Thinking: You can toggle reasoning on/off per turn. Need a quick regex? Turn it off for speed. Need a system architecture review? Turn it on for depth.
The Benchmarks: Less Hallucination, More Action
We’ve all been there: an agent gets stuck in a loop, endlessly rewriting the same broken code. GLM 4.7 explicitly attacks this with its hallucination-reduction architecture.
On SWE-bench Verified, it is showing stability scores that rival GPT-5.1 Preview. But the real test is in tool use. In BrowseComp and Terminal Bench 2.0, it demonstrates a scary ability to recover from errors. If a command fails, it reads the generic error message, thinks about the probable cause (e.g., “Oh, the dependency version is mismatched”), and self-corrects without user input.
(Honestly, watching it debug its own compile errors feels a bit like watching a junior dev grow up in real-time.)
The Catch: Heavy Metal Required
Here is the thing nobody puts in the marketing materials: This model is heavy.
If you are planning to run the full FP16 parameter version locally, hope you have a datacenter in your basement. It clocks in at 716GB. Even the quantized Q4 version will eat about 192GB of VRAM. This is not a “run on your MacBook Pro” model. It is designed for serious workstations or cluster deployments.
Also, be patient. That “Thinking” process takes time. Code generation can feel sluggish compared to the lightning-fast (but often wrong) responses of lighter models. It’s the classic “fast vs. right” trade-off.
Community Verdict
Complexity is the enemy of adoption, and GLM 4.7 is finding its niche among the “Power Users.”
* The UI Designers: They aren’t impressed. The model struggles with aesthetic nuance (CSS gradients, whitespace management) compared to Claude.
* The Backend Engineers: They love it. The ability to handle complex, multi-file refactors without losing the plot is a massive win.
* The Local Privacy Crowd: They are torn. They want to run it, but the hardware requirements are a high wall to climb.
What This Means For You
For Enterprise: This is your best bet for a self-hosted “Agent Swarm.” If you can’t send data to OpenAI, deploying a GLM 4.7 cluster gives you reasoning capabilities that were previously impossible on-prem.
For Developers: If you have the hardware (or cloud budget), the Preserved Thinking mode makes it the superior choice for “long-horizon” tasks—like migrating a legacy codebase from Python 2 to 3, or refactoring a monolith into microservices.
The Bottom Line
GLM 4.7 proves that open-source models aren’t just catching up; they are innovating in directions the closed labs are ignoring. By making “Thinking” a first-class citizen of the API, Zhipu AI has given us a tool that might be slower and heavier, but is undeniably smarter where it counts.
FAQ
Can I run this on my laptop?
No. Unless your laptop is a rack server in disguise. You need ~192GB VRAM for decent performance.
How does it compare to GPT-o1?
It uses a similar “chain of thought” approach but exposes it visibly. GPT-o1 is still smoother, but GLM 4.7 allows you to steer the thinking process.
Is it good for creative writing?
Not really. It’s “stiff.” It’s built for logic, code, and execution, not poetry.
