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I just discovered a way to cut my AI coding costs when using Claude Code by 85% — using a model that is almost as smart as Claude Sonnet 4.
I’m talking about z.ai — a Chinese AI Lab that released their own subscription that integrates directly with Claude Code:
In the CC-Bench, the model performed competitively against Claude Sonnet 4 (ranking better or on-par 50% of the time):
GLM-4.5 performed similarly to Claude Sonnet 4 at a fraction of price. https://x.com/Zai_org/status/1962522761630482700/photo/1
It might not be 100% there, but here’s the thing: for most of what I do, it’s good enough.
However, there are a few gotchas that you need to be aware of, so make sure to read till the end!
The AI Tool Spectrum Nobody Talks About
Engineers face a spectrum of coding problems.
On one end:
I want cheap but smart.
On the other:
I want the smartest for my hard problems.
Most AI Labs try to compete on one spectrum or another.
The middle ground :
Reasonably priced and reasonably smart
Is where the losers are.
In my opinion, the spectrum currently looks like this:
Left side: GLM-4.5 at $3/month — Good enough for most tasks
Middle: Claude Code at $20–200/month — Expensive “middle ground”. At the current state of the models and problems with Anthropic models, I put them below GPT-5 (which is also confirmed by benchmarks as well as overall sentiment on Reddit)
Right side: GPT-5 High — The absolute best for complex problems
So if you’re interested in cheap but smart Claude Code with GLM-4.5 might be the perfect fit for you!
Keep reading to see how Claude Code with GLM-4.5 compares to Claude Code with Claude Sonnet 4 on an example task!
My GLM-4.5 vs Claude Code Experiment
Let me be clear about what happened when I tested this head-to-head.
The Test Prompt (on a fresh Claude Code project):
Create a modern expense tracker app using React with TypeScript, including:
Add/edit/delete transactions with categories (Food, Transport, Entertainment)
Monthly spending chart using Chart.js
Expense summary by category
Clean, responsive design with Tailwind CSS
Form validation and error handling
Production-ready TypeScript interfaces and component organization
I ran the same prompt twice with different models as my assistants.
Claude Code Result
One-shotted results with Claude Code with Sonnet 4. Looks clean!
Transaction tab
One-shotted a polished UI with 3 clean tabs. The interface was immediately ready with:
TypeScript interfaces
Clean component organization
Flawless Tailwind implementation
Professional-grade form validation
Smooth Chart.js integration
GLM-4.5 Result
Results using Claude Code with GLM-4.5. Not bad, but definitely less impressive that Claude!
Very similar-looking app with the same core functionality. The output included:
Solid TypeScript structure (though less refined)
Functional component architecture
Working Chart.js integration
Tailwind styling (but struggled with some setup details)
All required features implemented
The Honest Verdict
GLM-4.5 doesn’t outperform Claude Code. It performs about 80–90% as well for 15% of the cost.
Claude’s version was more polished, had cleaner code organization, and required zero cleanup. GLM-4.5’s version was functional and well-structured but needed minor refinements (I had to guide it to get tailwind configuration right).
That’s the trade-off. And for many use cases, it’s a trade-off worth making.
What GLM-4.5 Actually Is (And Isn’t)
GLM-4.5 isn’t just “Claude but cheaper.” There are specific downgrades you need to understand:
Missing Features when integrating GLM into Claude Code:
No reasoning mode: This is huge for complex tasks that require step-by-step thinking
Web search doesn’t work: Can’t pull in external information
Less polish: Output is functional but requires more cleanup
What This Means in Practice:
Simple UI components: GLM-4.5 handles well
Complex architectural decisions: You’ll miss Claude’s reasoning capabilities
Debugging complex systems: The reasoning limitation becomes apparent
Research-heavy tasks: No web search is a real constraint
The downgraded quality is exactly what makes the price possible. It’s not magic — it’s engineering trade-offs.
The Setup: 5 Minutes to 85% Savings
Here’s how GLM-4.5 integrates with Claude Code using Z.AI’s implementation:
Step 1: Subscribe at z.ai/subscribe
$3/month: 120 prompts per 5-hour cycle
$15/month: 600 prompts per 5-hour cycle
Step 2: Configure your environment (you can follow the instructions from the official page: https://docs.z.ai/devpack/tool/claude)
export ANTHROPIC_API_BASE="https://api.z.ai/anthropic"
export ANTHROPIC_API_KEY="your-zai-key"Step 3: Use Claude Code normally The interface stays identical. GLM-4.5 handles the requests behind the scenes.
The elegant part: you keep your existing workflow. The AI changes, but your tools don’t.
A quick hack — I made two custom commands in my .zshrc (I’m using Mac) that allow me to switch between GLM-4.5 and Claude Sonnet as the AI Assistant in Claude Code:
glm() {
export ANTHROPIC_BASE_URL="https://open.bigmodel.cn/api/anthropic"
export ANTHROPIC_AUTH_TOKEN="your-zai-key"
claude $1
}
claude_unset() {
unset ANTHROPIC_BASE_URL
unset ANTHROPIC_AUTH_TOKEN
claude $1
}The Privacy Trade-off
There’s one more consideration: GLM-4.5 is hosted in China through Z.AI’s infrastructure.
This represents a significant privacy trade-off that you need to evaluate. For proprietary code or sensitive business logic, this might be a deal-breaker. For learning projects, open-source work, or non-sensitive development, the cost savings might outweigh the privacy concerns.
The decision depends entirely on your specific context and risk tolerance.
The Honest Recommendation
GLM-4.5 isn’t revolutionary. It’s not going to change how you think about AI coding.
But it is a solid, cost-effective alternative for routine development work.
The secret is understanding what you actually need. Most coding tasks don’t require the world’s most advanced AI. They require reliable, consistent output that saves you time.
GLM-4.5 delivers that at a price that makes sense for individual developers and small teams.
Your Next Step
Calculate what you’re actually using your AI coding tool for:
If 80%+ is routine coding work: Try GLM-4.5 for a month
If you regularly need complex reasoning: Stick with premium tools
If you’re cost-sensitive but need reliability: The hybrid approach might work well (i.e. use GLM-4.5 for simpler tasks, switch to other tools for more complex stuff)
Test GLM-4.5 with your typical workflow. Run the same expense tracker test I did, or use your own standard coding tasks. See where the 80–90% performance threshold works for your specific use case.
Now go build something that matters.
Luke