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---

# 🚀 GLM-4.7 vs. The $200 Giants: Is China's $3 AI Coding Tool the New Market King?

```text
██████╗ ██╗     ███╗   ███╗      ██╗  ██╗    ███████╗
██╔════╝ ██║     ████╗ ████║      ██║  ██║    ╚════██║
██║  ███╗██║     ██╔████╔██║█████╗███████║        ██╔╝
██║   ██║██║     ██║╚██╔╝██║╚════╝╚════██║       ██╔╝ 
╚██████╔╝███████╗██║ ╚═╝ ██║           ██║       ██║  
 ╚═════╝ ╚══════╝╚═╝     ╚═╝           ╚═╝       ╚═╝  
      THE FRONTIER AGENTIC REASONING MODEL (2025)
```

### 💡 Key Takeaways (TL;DR)
- **GLM-4.7** is the new **SOTA (State of the Art)** AI coding model for 2025.
- Developed by **Zhipu AI**, it offers enterprise-level performance matching or exceeding flagship models like **Claude Sonnet 4.5** and **GPT-5.1 High**.
- **Context Window**: Massive **200K tokens** for full codebase analysis.
- **Best For**: Cost-conscious developers, agentic workflows, and high-complexity debugging.

The global landscape for AI-powered development is shifting. While Western tools like **Cursor Pro** and **GitHub Copilot** have dominated by charging premium subscription rates (often reaching $200 per year), a new contender from Beijing, China, has arrived to dismantle that pricing model.

**Zhipu AI** has released **GLM-4.7**, a large language model specifically engineered for coding, offering performance that rivals top-tier US models. For pricing information, visit [Z.ai subscription page](https://z.ai/subscribe?ic=R0K78RJKNW) or use via [OpenRouter](https://openrouter.ai/).

---

## ⚔️ The Frontier Battle: Verified Benchmarks

GLM-4.7 demonstrates competitive performance against the newest generation of flagship models, including **Claude Sonnet 4.5** and **GPT-5.1 High**, based on the official Z.ai Technical Report (Dec 2025).

### 📊 2025 AI Coding Model Performance Comparison
*Note: Best scores per category are highlighted in $\color{green}{\text{green}}$.*

<div align="center">

```
╔════════════════════════════════════════════════════════════════════════════════════════╗
║                          🏆 GLM-4.7: SOTA 2025 AI Model                    ║
╠════════════════════════════════════════════════════════════════════════════════════════╣
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  🧮 MATH (AIME 25)                                                 │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  GLM-4.7           ████████████████████ 95.7% 🥇           │       │        ║
║  │  │  Gemini 3.0 Pro     ███████████████████░ 95.0%                │       │        ║
║  │  │  GPT-5.1 High       ██████████████████░░ 94.0%                │       │        ║
║  │  │  DeepSeek-V3.2      ███████████████░░░░ 93.1%                │       │        ║
║  │  │  Claude Sonnet 4.5  ███████████░░░░░░░ 87.0%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [Z.ai](https://z.ai/blog/glm-4.7)                              │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  💻 CODING (LiveCodeBench v6)                                        │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  Gemini 3.0 Pro     ████████████████████ 90.7% 🥇           │       │        ║
║  │  │  GPT-5.1 High       ███████████████████░ 87.0%                │       │        ║
║  │  │  GLM-4.7           ████████████████░░░ 84.9%                │       │        ║
║  │  │  DeepSeek-V3.2      ███████████████░░░░ 83.3%                │       │        ║
║  │  │  Claude Sonnet 4.5  ██████████░░░░░░░░ 64.0%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [Z.ai](https://z.ai/blog/glm-4.7)                              │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  🔬 SCIENCE (GPQA-Diamond)                                           │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  Gemini 3.0 Pro     ████████████████████ 91.9% 🥇           │       │        ║
║  │  │  GPT-5.1 High       ███████████████████░ 88.1%                │       │        ║
║  │  │  GLM-4.7           ████████████████░░░ 85.7%                │       │        ║
║  │  │  Claude Sonnet 4.5  ██████████████░░░░░ 83.4%                │       │        ║
║  │  │  DeepSeek-V3.2      ██████████████░░░░░░ 82.4%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [Z.ai](https://z.ai/blog/glm-4.7)                              │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  🧠 LOGIC (HLE w/Tools)                                             │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  Gemini 3.0 Pro     ██████████░░░░░░░░ 45.8% 🥇           │       │        ║
║  │  │  GLM-4.7           ██████████░░░░░░░░ 42.8%                │       │        ║
║  │  │  GPT-5.1 High       ██████████░░░░░░░░ 42.7%                │       │        ║
║  │  │  DeepSeek-V3.2      █████████░░░░░░░░░ 40.8%                │       │        ║
║  │  │  Claude Sonnet 4.5  ███████░░░░░░░░░░ 32.0%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [Z.ai](https://z.ai/blog/glm-4.7)                              │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  ⚙️ ENGINEERING (SWE-bench)                                          │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  Claude Sonnet 4.5  ███████████████████░ 77.2% 🥇           │       │        ║
║  │  │  GPT-5.1 High       █████████████████░░░ 76.3%                │       │        ║
║  │  │  Gemini 3.0 Pro     ███████████████░░░░ 76.2%                │       │        ║
║  │  │  GLM-4.7           ██████████████░░░░░ 73.8%                │       │        ║
║  │  │  DeepSeek-V3.2      █████████████░░░░░░ 73.1%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [SWE-bench](https://github.com/princeton-nlp/SWE-bench)          │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  ┌────────────────────────────────────────────────────────────────────────────┐        ║
║  │  🤖 AGENTIC (τ²-Bench)                                              │        ║
║  │  ┌─────────────────────────────────────────────────────────────────────┐       │        ║
║  │  │  Gemini 3.0 Pro     ████████████████████ 90.7% 🥇           │       │        ║
║  │  │  GLM-4.7           ██████████████████░░ 87.4%                │       │        ║
║  │  │  Claude Sonnet 4.5  ██████████████████░░ 87.2%                │       │        ║
║  │  │  DeepSeek-V3.2      ███████████████░░░░ 85.3%                │       │        ║
║  │  │  GPT-5.1 High       ███████████░░░░░░░ 82.7%                │       │        ║
║  │  └─────────────────────────────────────────────────────────────────────┘       │        ║
║  │  Source: [Z.ai](https://z.ai/blog/glm-4.7)                              │        ║
║  └────────────────────────────────────────────────────────────────────────────┘        ║
║                                                                              ║
║  🎯 Key Wins: Math (1st) | Agentic (2nd) | Logic (2nd) | Coding (3rd)           ║
╚════════════════════════════════════════════════════════════════════════════════════════╝
```

</div>

| Category | Benchmark | **GLM-4.7** | Claude Sonnet 4.5 | GPT-5.1 High | DeepSeek-V3.2 | Gemini 3.0 Pro | Source |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Math** | AIME 25 | $\color{green}{\textbf{95.7}}$ | 87.0 | 94.0 | 93.1 | 95.0 | [Z.ai](https://z.ai/blog/glm-4.7) |
| **Coding** | LiveCodeBench v6 | 84.9 | 64.0 | 87.0 | 83.3 | $\color{green}{\textbf{90.7}}$ | [Z.ai](https://z.ai/blog/glm-4.7) |
| **Science** | GPQA-Diamond | 85.7 | 83.4 | 88.1 | 82.4 | $\color{green}{\textbf{91.9}}$ | [Z.ai](https://z.ai/blog/glm-4.7) |
| **Logic** | HLE (w/ Tools) | 42.8 | 32.0 | 42.7 | 40.8 | $\color{green}{\textbf{45.8}}$ | [Z.ai](https://z.ai/blog/glm-4.7) |
| **Engineering**| SWE-bench (Ver.) | 73.8% | $\color{green}{\textbf{77.2%}}$ | 76.3% | 73.1% | 76.2% | [Z.ai](https://z.ai/blog/glm-4.7) |
| **Agentic** | τ²-Bench | 87.4% | 87.2% | 82.7% | 85.3% | $\color{green}{\textbf{90.7%}}$ | [Z.ai](https://z.ai/blog/glm-4.7) |

**📊 Additional Sources:** [HuggingFace Model Card](https://huggingface.co/zai-org/GLM-4.7) | [Ollama Library](https://ollama.com/library/glm-4.7) | [LLM-Stats Analysis](https://llm-stats.com/models/glm-4.7) | [Vertu Comparison](https://vertu.com/lifestyle/glm-4-7-vs-claude-opus-4-5-the-thinking-open-source-challenger/)

---

## 🛠️ What is GLM-4.7? Technical Specifications and Features
GLM-4.7 is the latest iteration of the General Language Model (GLM) series developed by Beijing-based **Zhipu AI**.

### 🚀 Key Technical Highlights (from Z.ai blog)
- **Interleaved Thinking:** GLM-4.7 thinks before every response and tool calling, improving instruction following and quality of generation.
- **Preserved Thinking:** In coding agent scenarios, GLM-4.7 automatically retains all thinking blocks across multi-turn conversations, reusing existing reasoning instead of re-deriving from scratch.
- **Turn-level Thinking:** GLM-4.7 supports per-turn control over reasoning within a session—disable thinking for lightweight requests to reduce latency/cost, enable it for complex tasks to improve accuracy and stability.
- **Tool Using:** GLM-4.7 achieves significant improvements in tool using, with better performances on benchmarks such as τ²-Bench and on web browsing via BrowseComp.

---

## 📈 GLM-4.7 vs GLM-4.6: Key Improvements

Based on [Z.ai Technical Report](https://z.ai/blog/glm-4.7), GLM-4.7 delivers significant gains across core benchmarks compared to its predecessor GLM-4.6:

### � Performance Gains
| Benchmark | GLM-4.6 | GLM-4.7 | Improvement |
| :--- | :--- | :--- | :--- |
| **SWE-bench** | 68.0% | 73.8% | **+5.8%** |
| **SWE-bench Multilingual** | 53.8% | 66.7% | **+12.9%** |
| **Terminal Bench 2.0** | 24.5% | 41.0% | **+16.5%** |
| **HLE (w/ Tools)** | 30.4% | 42.8% | **+12.4%** |
| **LiveCodeBench-v6** | 82.8% | 84.9% | **+2.1%** |

### �️ Enhanced Capabilities
- **Interleaved Thinking:** GLM-4.7 thinks before every response and tool calling, improving instruction following and quality of generation.
- **Preserved Thinking:** In coding agent scenarios, GLM-4.7 automatically retains all thinking blocks across multi-turn conversations, reusing existing reasoning instead of re-deriving from scratch.
- **Turn-level Thinking:** GLM-4.7 supports per-turn control over reasoning within a session—disable thinking for lightweight requests to reduce latency/cost, enable it for complex tasks to improve accuracy and stability.

---

## ❓ FAQ: GLM-4.7 and the AI Coding Market

**What is best cost-effective AI for coding in 2025?**
The market for high-performance, budget-friendly AI has expanded significantly in 2025. Leading the pack are **GLM-4.7 (Zhipu AI)** and **DeepSeek-V3.2**, both offering performance comparable to **Claude Sonnet 4.5** at a fraction of the cost. GLM-4.7 is often preferred for agentic workflows due to its advanced "Preserved Thinking" architecture, while DeepSeek-V3.2 remains a strong choice for raw logic and reasoning tasks.

**Is GLM-4.7 better than GPT-5.1 or Claude Sonnet 4.5 for coding?**
Objectively, **Claude Sonnet 4.5** and **GPT-5.1** currently hold the edge in massive-scale architectural planning and natural language nuance. However, GLM-4.7 has achieved parity or leadership in execution-heavy benchmarks (LiveCodeBench: 84.9) and mathematical reasoning (AIME 25: 95.7). For developers, the choice is often between paying for the absolute peak (Claude/GPT) or achieving 95% of that performance with GLM-4.7 for 1/20th the price.

**How much does the GLM-4.7 coding tool cost?**
GLM-4.7 is available via the [Z.ai API platform](https://docs.z.ai/guides/llm/glm-4.7) and through [OpenRouter](https://openrouter.ai/). For detailed pricing, visit [Z.ai subscription page](https://z.ai/subscribe?ic=R0K78RJKNW).

**Who developed GLM-4.7?**
GLM-4.7 was developed by **Zhipu AI**, a leading artificial intelligence company based in Beijing, China, emerging from the Knowledge Engineering Group (KEG) at Tsinghua University.

**Can I use GLM-4.7 in the US and Europe?**
Yes, GLM-4.7 is available worldwide through [OpenRouter](https://openrouter.ai/). It is compatible with coding agent frameworks mentioned in the Z.ai blog: **Claude Code**, **Kilo Code**, **Cline**, and **Roo Code**.

---

## 📚 References & Methodology
All data presented in this article is derived from the [Z.ai Official Technical Report](https://z.ai/blog/glm-4.7) (December 2025):
- **Benchmark Performance:** GLM-4.7 compared against GLM-4.6, Kimi K2 Thinking, DeepSeek-V3.2, Gemini 3.0 Pro, Claude Sonnet 4.5, GPT-5 High, and GPT-5.1 High across 17 benchmarks.
- **Core Coding:** SWE-bench (73.8%, +5.8%), SWE-bench Multilingual (66.7%, +12.9%), Terminal Bench 2.0 (41%, +16.5%).
- **Reasoning:** HLE (w/ Tools): 42.8%, AIME 2025: 95.7%, GPQA-Diamond: 85.7%.
- **Agentic:** τ²-Bench: 87.4%, BrowseComp: 52.0%.
- **Features:** Interleaved Thinking, Preserved Thinking, Turn-level Thinking for stable multi-turn conversations.
- **Supported Tools:** Claude Code, Kilo Code, Cline, and Roo Code for agent workflows.

---

## 🔗 Source Links
- [Z.ai Tech Report](https://z.ai/blog/glm-4.7)
- [HuggingFace Model Card](https://huggingface.co/zai-org/GLM-4.7)
- [Ollama Library](https://ollama.com/library/glm-4.7)
- [LLM-Stats Analysis](https://llm-stats.com/models/glm-4.7)
- [Vertu: GLM-4.7 vs Claude Opus 4.5](https://vertu.com/lifestyle/glm-4-7-vs-claude-opus-4-5-the-thinking-open-source-challenger/)

---

*The era of "$200 AI coding tax" is over. Join the GLM revolution today.*