Everyone's talking about the new GLM-4.7 benchmarks. 73.8% on SWE-bench. MIT license. 200K context window.
But benchmarks don't tell you what it's like to actually use the thing.
So I spent two weeks building real projects with it—web apps, debugging sessions, UI generation, the works. Here's what I learned that the spec sheet won't tell you.
The Feature That Changes Everything
Most AI coding assistants have a fatal flaw: they forget. Ask them to add authentication to an app you discussed three days ago, and they'll act like they've never heard of your project.
GLM-4.7's "preserved thinking" mechanism actually maintains context across sessions. I tested this by building a full-stack application over multiple days. On day three, when I asked it to add authentication, it referenced architectural decisions from our first conversation.
That never happens with traditional models.
The Real Cost Math
Let me show you what this actually costs:
Side project developer: ~$0.74/month
5-person startup: ~$52/month (with caching)
Enterprise scale: ~$5,200/month
Compare that to Claude Pro at $20/month per person or enterprise GPT-4 costs of $25,000-35,000/month for similar usage.
The math is honestly ridiculous.
What Actually Works (And What Doesn't)
The good:
- UI generation that doesn't look like 2010 Bootstrap
- Multilingual coding that actually understands mixed-language codebases
- Terminal commands that recover from failures instead of panicking
The reality check:
- Inference speed is middling (55 tokens/sec)
- Not quite frontier-level on the hardest reasoning tasks
- Running locally requires serious GPU hardware
Three Ways to Try It
- Easiest: Web interface at chat.z.ai
- Best for dev work: Integrate with Claude Code or Cline
- Full control: Self-host via Hugging Face + vLLM
I've tested all three approaches and documented the exact setup process, real-world gotchas, and when each makes sense.
The Bottom Line
GLM-4.7 isn't the most powerful model available. But it might be the most practical for real-world development at scale.
It's the first time an open-source model feels like it was trained for actual work, not demos.
Read the full deep-dive with code examples, benchmarks, and setup guides here: GLM-4.7: The Open-Source LLM That Codes Like a Pro
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