The version of GLM 4.7 released by Zhipu AI’s is already among the finest open-source large language models.
This is especially useful when you do a lot of coding, use agents, and work for a long time to finish real tasks.
It is not a small nudge in the standings. GLM-4.7 actually addresses the pain points that GLM-4.6 experienced in the real life.
It comes in particularly handy when you are using the model as a real code agent rather than an expensive code-completer.
What GLM-4.7 is Actually Optimized For
GLM-4.7 is not attempting to capture all the generic intelligence leader boards. Rather, it is concerned with three utilitarian things:
- Agentic coding which does not crash after 10+ turns.
- Terminal & tool usage which recovers intelligently on a partial failure.
- Consistent reasoning that does not re-invent its logic every time the message is sent.
Such priorities manifest themselves in benchmarks that are the most important in everyday dev work.
Massive Gains in Core Coding Capabilities
Let us examine the real numbers which count.
On the SWE-bench Confirmed (real GitHub-problem resolution):
GLM -4.7 73.8% – a good improvement of +5.8 on GLM -4.6 68.0%. That is good already with the best open models.
Better still: SWE-bench Multilingual yields to 66.7 percent (5.3 more than 4.6) increases.
This big boost across many languages shows the model is not just copying English patterns. It can think better about code and instructions in mixed languages
The actual game-changer : Terminal Bench 2.0
GLM-4.6 performed dismally at 24.5% – agents would lose state, give incorrect sequence commands and never recover.
GLM‑4.7 rockets to 41.0% (+16.5%).
Despite the drawbacks of the retries, you can instantly notice the difference: cleverer attempts, lesser number of delusional wins, and actual strength following failure.
This makes a shaky demo agent reliable enough to rely upon during unattended runs.
Reasoning & Agent Benchmarks That Matter in Practice
Image Source : z.ai/blog/glm-4.7
GLM-4.7 demonstrates good improvement in critical tests:
- LiveCodeBench v6: 84.9% (clean improvement, competitive best closed models)
- AIME 2025: 95.7% (elite math performance)
- HLE (Humanitys Last Exam, tough long term reasoning stress test): Base 24.8, but with tools on, 42.8 (vast improvement over 17.2 of 4.6). That is much more dramatically better tool integration devoid of self-sabotage.
On τ²-Bench (brutal planning and tool sequencing test): 87.4% (+12.2% over 4.6’s 75.2%).
In web browsing tasks, the score goes from 52% to 67.5% with context management. This helps the model remember and build on earlier web information instead of forgetting it
The Secret Sauce: Smarter, More Stable Thinking in GLM 4.7
This is what most people brush over yet it is the reason why all other things got so much better.
GLM-4.7 is based on Interleaved Thinking (reason before every action/tool call) and two killer features are introduced:
1) Preserved Thinking : Retains all the previous blocks of reasoning in turn rather than reinitiating.
This is fatal to the old fashioned forget what I decided 5 turns ago problem in long agentic coding sessions.
2) Turn-level Thinking : You think on a turn-by-turn basis. Turn it off when you need quick answers to syntax questions. Turn it on when you need help with complex debugging or planning.
These changes make the model feel calmer, easier to predict, and less wild during long tasks.
Cleaner UI & “Vibe” Coding
Benchmarks cannot capture this, but you notice it when you write HTML, CSS, JS, or slides:
GLM-4.7 generates much more clean layouts, improved spacing, professional look, and less guess-the-pixel fiascos than 4.6.
It doesn’t give you a complete design change, but it puts a lot fewer battles in your path a welcome but pleasant quality-of-life improvement.
How to Actually Use GLM-4.7 :
1) Free chat & testing: Go to chat.z.ai — Use GLM-4.7 directly in the browser, no charges when using the basic version.
2) Ultra-fast inference on Cerebras: GLM-4.7 also runs on the Cerebras Inference Cloud, where it works extremely fast—about 1,000 tokens per second.
This speed is very advanced and much faster than using GPUs.
3) Local run: Public on Hugging Face Hugging Face huggingface/GLM-4.7 (and its more efficient versions such as cerebras/GLM-4.7-REAP).
Infer with vLLM, SGLang or Ollama. You can run it on consumer hardware with good performance using quantized versions (Q4, GGUF, etc.).
4) API access: API is offered on Z.ai platform, OpenRouter, Cerebras, or third-party gateways.
5) Coding agents: Just drops in tools such as Claude Code, Cline, Roo Code, Kilo Code, OpenCode, etc.
Conclusion
GLM-4.7 does not pursue eye-catching leaderboard trophies.
It feels like the model was tested with the assumption that it would never face noisy, lengthy, and failure-prone real-world applications.
It breaks less often. Recovers smarter. Remembers decisions. Manipulates instruments without losing track.
And now with Cerebras you get it at scalding real-time speeds that transform the pace of iteration.
“GLM-4.6 showed promise but was not very stable. GLM-4.7 feels reliable and earns your trust for real work, especially when you need to work fast.
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