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Siddhesh Surve
Siddhesh Surve

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The "GPT Killer" No One Saw Coming: GLM-4.7 Just Broke the Benchmarks

#ai #webdev #programming #opensource

Is the era of closed-source dominance finally over? Zhipu AI’s latest release suggests the answer might be "Yes."

If you’ve been watching the AI leaderboard ping-pong between GPT-5.1 and Claude Sonnet 4.5 all year, you might have missed the quiet revolution brewing in the open-weights community. That changed yesterday.

Zhipu AI (Z.AI) just dropped GLM-4.7, a massive Mixture-of-Experts (MoE) model that doesn't just "compete"—it effectively resets the bar for what open-source models can handle in complex coding workflows.

Here is why every developer needs to pay attention to this release, whether you are building agents, generating UI, or just want a local coding copilot that actually thinks.

🧠 The "Thinking" Upgrade: It's Not Just a Buzzword

The biggest frustration with coding agents in 2025 has been "context amnesia"—where a model forgets its own logic five turns into a debugging session. GLM-4.7 fixes this with three distinct Thinking Modes:

  1. Interleaved Thinking: The model reasons before every single tool call or response. No more random npm install commands without checking package.json first.
  2. Preserved Thinking: This is the game changer. In agentic workflows, the model retains its reasoning blocks across multi-turn conversations. It remembers why it decided to refactor that class ten minutes ago.
  3. Turn-Level Control: You can toggle reasoning on/off per request. Need a quick regex? Turn it off for speed. Debugging a race condition? Turn it on for deep analysis.

📊 The Benchmarks (That Actually Matter)

We all know benchmarks can be gamified, but GLM-4.7’s performance on the HLE (Humanity's Last Exam) benchmark is turning heads.

  • HLE Score: 42.8% (with tools). This is a 38% jump over its predecessor (GLM-4.6) and puts it within striking distance of GPT-5.1.
  • SWE-bench Verified: 73.8%. This is currently the SOTA (State of the Art) for open-source models.
  • Terminal Bench 2.0: 41%. If you use tools like OpenCode or Cline, this metric translates directly to "less likely to crash your terminal."

✨ "Vibe Coding" & Frontend Mastery

One of the most surprising updates is what Z.AI calls "Vibe Coding." GLM-4.7 has been fine-tuned to understand visual code specifications better than most flagship models.

If you ask for a landing page, it doesn't just give you raw HTML; it produces:

  • Modern, aesthetic layouts (Flexbox/Grid by default).
  • Harmonious color palettes.
  • Correctly sized components.

The result? Frontend prototypes that look 90% finished on the first prompt, rather than 50%.

🛠️ How to Try It (Right Now)

GLM-4.7 is a 355B parameter model (32B active), meaning it's a beast, but efficient. You can access it via the Z.ai API, OpenRouter, or run it locally if you have the hardware (hello, Mac Studio users).

Python SDK Example

If you want to test the new Thinking Mode, here is a quick snippet using the Z.ai SDK:

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="YOUR_API_KEY") 

response = client.chat.completions.create(
    model="glm-4-7", 
    messages=[
        {"role": "user", "content": "Analyze this memory leak in my Rust server."}
    ],
    # Enable the new thinking features
    extra_body={
        "thinking_mode": "interleaved", 
        "preserved_thinking": True
    }
)

print(response.choices[0].message.content)
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💡 The Verdict: Should You Switch?

If you value data privacy and want to run a model that rivals GPT-5.1 on your own infrastructure (or just want cheaper API costs), GLM-4.7 is a no-brainer.

It represents a mature step forward for open-source AI: moving away from raw "token prediction" toward stateful, reasoning-heavy agents.

Are you planning to test GLM-4.7 in your workflow? Let me know in the comments! 👇

GLM 4.7: New SOTA Coding KING? Powerful, Fast, & Cheap! Really Good AT Coding! (Fully Tested)

This video provides a hands-on review of GLM-4.7, testing its coding capabilities against benchmarks and demonstrating why it might be the new king of open-source models.

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