# π GLM-4.7 vs. The $200 Giants: Is China's $3 AI Coding Tool the New Market King?
```text
βββββββ βββ ββββ ββββ βββ βββ ββββββββ
ββββββββ βββ βββββ βββββ βββ βββ ββββββββ
βββ βββββββ βββββββββββββββββββββββββ ββββ
βββ ββββββ βββββββββββββββββββββββββ ββββ
ββββββββββββββββββββ βββ βββ βββ βββ
βββββββ βββββββββββ βββ βββ βββ
THE FRONTIER AGENTIC REASONING MODEL (2025)
```
### π‘ 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/).
---
## βοΈ 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).
### π 2025 AI Coding Model Performance Comparison
*Note: Best scores per category are highlighted in $\color{green}{\text{green}}$. Data sourced from [Z.ai Official Blog](https://z.ai/blog/glm-4.7).*
```mermaid
graph TD
subgraph "2025 Flagship Benchmark Comparison"
M[Math - AIME 25] --> G1{GLM-4.7: 95.7%}
M --> C1[Claude Sonnet 4.5: 87.0%]
M --> G2[Gemini 3.0 Pro: 95.0%]
M --> D1[DeepSeek-V3.2: 93.1%]
M --> P1[GPT-5.1 High: 94.0%]
CO[Coding - LiveCode] --> G2_C{GLM-4.7: 84.9%}
CO --> C2[Claude Sonnet 4.5: 64.0%]
CO --> D2[DeepSeek-V3.2: 83.3%]
CO --> P2[GPT-5.1 High: 87.0%]
CO --> G2_CO[Gemini 3.0 Pro: 90.7%]
S[Science - GPQA] --> G3{GLM-4.7: 85.7%}
S --> C3[Claude Sonnet 4.5: 83.4%]
S --> D3[DeepSeek-V3.2: 82.4%]
S --> P3[GPT-5.1 High: 88.1%]
S --> G3_S[Gemini 3.0 Pro: 91.9%]
L[Logic - HLE w/Tools] --> G4{GLM-4.7: 42.8%}
L --> C4[Claude Sonnet 4.5: 32.0%]
L --> D4[DeepSeek-V3.2: 40.8%]
L --> P4[GPT-5.1 High: 42.7%]
L --> G4_L[Gemini 3.0 Pro: 45.8%]
end
classDef glmNode fill:#00c853,stroke:#1b5e20,stroke-width:3px,color:#ffffff,font-weight:bold,font-size:14px
classDef sonnetNode fill:#f1f8e9,stroke:#c5e1a5,stroke-width:1px,color:#558b2f
classDef budgetNode fill:#e3f2fd,stroke:#2196f3,stroke-width:1px,color:#0d47a1
class G1,G2_C,G3,G4 glmNode
class C1,C2,C3,C4 sonnetNode
class D1,D2,D3,D4,G2,P1,P2,P3,P4,G2_CO,G3_S,G4_L budgetNode
```
| 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** | ΟΒ²-Bench | 87.4% | 87.2% | 82.7% | 85.3% | $\color{green}{\textbf{90.7%}}$ | [Z.ai](https://z.ai/blog/glm-4.7) |
---
## π οΈ 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**.
### π 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 sessionβdisable 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 ΟΒ²-Bench and on web browsing via BrowseComp.
---
## π 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:
### οΏ½ 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%** |
### οΏ½οΈ 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 sessionβdisable thinking for lightweight requests to reduce latency/cost, enable it for complex tasks to improve accuracy and stability.
---
## β 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**.
---
## π 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:** ΟΒ²-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.
---
## π 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.*