153 lines
10 KiB
Markdown
153 lines
10 KiB
Markdown
# 🚀 GLM-4.7 vs. The $200 Giants: Is China's $3 AI Coding Tool the New Market King?
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```text
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██████╗ ██╗ ███╗ ███╗ ██╗ ██╗ ███████╗
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██╔════╝ ██║ ████╗ ████║ ██║ ██║ ╚════██║
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██║ ███╗██║ ██╔████╔██║█████╗███████║ ██╔╝
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██║ ██║██║ ██║╚██╔╝██║╚════╝╚════██║ ██╔╝
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╚██████╔╝███████╗██║ ╚═╝ ██║ ██║ ██║
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╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═╝ ╚═╝
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THE FRONTIER AGENTIC REASONING MODEL (2025)
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```
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### 💡 Key Takeaways (TL;DR)
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- **GLM-4.7** is the new **SOTA (State of the Art)** AI coding model for 2025.
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- Developed by **Zhipu AI**, it offers enterprise-level performance matching or exceeding flagship models like **Claude Sonnet 4.5** and **GPT-5.1 High**.
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- **Context Window**: Massive **200K tokens** for full codebase analysis.
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- **Best For**: Cost-conscious developers, agentic workflows, and high-complexity debugging.
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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.
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**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/).
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---
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## ⚔️ The Frontier Battle: Verified Benchmarks
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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).
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### 📊 2025 AI Coding Model Performance Comparison
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*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).*
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```mermaid
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graph TD
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subgraph "2025 Flagship Benchmark Comparison"
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M[Math - AIME 25] --> G1{<b>GLM-4.7: 95.7%</b>}
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M --> C1[Claude Sonnet 4.5: 87.0%]
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M --> G2[Gemini 3.0 Pro: 95.0%]
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M --> D1[DeepSeek-V3.2: 93.1%]
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M --> P1[GPT-5.1 High: 94.0%]
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CO[Coding - LiveCode] --> G2_C{<b>GLM-4.7: 84.9%</b>}
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CO --> C2[Claude Sonnet 4.5: 64.0%]
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CO --> D2[DeepSeek-V3.2: 83.3%]
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CO --> P2[GPT-5.1 High: 87.0%]
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CO --> G2_CO[Gemini 3.0 Pro: 90.7%]
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S[Science - GPQA] --> G3{<b>GLM-4.7: 85.7%</b>}
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S --> C3[Claude Sonnet 4.5: 83.4%]
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S --> D3[DeepSeek-V3.2: 82.4%]
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S --> P3[GPT-5.1 High: 88.1%]
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S --> G3_S[Gemini 3.0 Pro: 91.9%]
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L[Logic - HLE w/Tools] --> G4{<b>GLM-4.7: 42.8%</b>}
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L --> C4[Claude Sonnet 4.5: 32.0%]
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L --> D4[DeepSeek-V3.2: 40.8%]
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L --> P4[GPT-5.1 High: 42.7%]
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L --> G4_L[Gemini 3.0 Pro: 45.8%]
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end
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classDef glmNode fill:#00c853,stroke:#1b5e20,stroke-width:3px,color:#ffffff,font-weight:bold,font-size:14px
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classDef sonnetNode fill:#f1f8e9,stroke:#c5e1a5,stroke-width:1px,color:#558b2f
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classDef budgetNode fill:#e3f2fd,stroke:#2196f3,stroke-width:1px,color:#0d47a1
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class G1,G2_C,G3,G4 glmNode
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class C1,C2,C3,C4 sonnetNode
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class D1,D2,D3,D4,G2,P1,P2,P3,P4,G2_CO,G3_S,G4_L budgetNode
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```
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| Category | Benchmark | **GLM-4.7** | Claude Sonnet 4.5 | GPT-5.1 High | DeepSeek-V3.2 | Gemini 3.0 Pro | Source |
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| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
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| **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) |
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| **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) |
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| **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) |
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| **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) |
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| **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) |
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| **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) |
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**📊 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/)
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---
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## 🛠️ What is GLM-4.7? Technical Specifications and Features
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GLM-4.7 is the latest iteration of the General Language Model (GLM) series developed by Beijing-based **Zhipu AI**.
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### 🚀 Key Technical Highlights (from Z.ai blog)
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- **Interleaved Thinking:** GLM-4.7 thinks before every response and tool calling, improving instruction following and quality of generation.
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- **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.
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- **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.
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- **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.
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---
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## 📈 GLM-4.7 vs GLM-4.6: Key Improvements
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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:
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### <20> Performance Gains
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| Benchmark | GLM-4.6 | GLM-4.7 | Improvement |
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| :--- | :--- | :--- | :--- |
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| **SWE-bench** | 68.0% | 73.8% | **+5.8%** |
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| **SWE-bench Multilingual** | 53.8% | 66.7% | **+12.9%** |
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| **Terminal Bench 2.0** | 24.5% | 41.0% | **+16.5%** |
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| **HLE (w/ Tools)** | 30.4% | 42.8% | **+12.4%** |
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| **LiveCodeBench-v6** | 82.8% | 84.9% | **+2.1%** |
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### <20>️ Enhanced Capabilities
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- **Interleaved Thinking:** GLM-4.7 thinks before every response and tool calling, improving instruction following and quality of generation.
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- **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.
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- **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.
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---
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## ❓ FAQ: GLM-4.7 and the AI Coding Market
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**What is best cost-effective AI for coding in 2025?**
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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.
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**Is GLM-4.7 better than GPT-5.1 or Claude Sonnet 4.5 for coding?**
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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.
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**How much does the GLM-4.7 coding tool cost?**
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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).
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**Who developed GLM-4.7?**
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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.
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**Can I use GLM-4.7 in the US and Europe?**
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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**.
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---
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## 📚 References & Methodology
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All data presented in this article is derived from the [Z.ai Official Technical Report](https://z.ai/blog/glm-4.7) (December 2025):
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- **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.
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- **Core Coding:** SWE-bench (73.8%, +5.8%), SWE-bench Multilingual (66.7%, +12.9%), Terminal Bench 2.0 (41%, +16.5%).
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- **Reasoning:** HLE (w/ Tools): 42.8%, AIME 2025: 95.7%, GPQA-Diamond: 85.7%.
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- **Agentic:** τ²-Bench: 87.4%, BrowseComp: 52.0%.
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- **Features:** Interleaved Thinking, Preserved Thinking, Turn-level Thinking for stable multi-turn conversations.
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- **Supported Tools:** Claude Code, Kilo Code, Cline, and Roo Code for agent workflows.
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---
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## 🔗 Source Links
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- [Z.ai Tech Report](https://z.ai/blog/glm-4.7)
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- [HuggingFace Model Card](https://huggingface.co/zai-org/GLM-4.7)
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- [Ollama Library](https://ollama.com/library/glm-4.7)
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- [LLM-Stats Analysis](https://llm-stats.com/models/glm-4.7)
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- [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/)
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---
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*The era of "$200 AI coding tax" is over. Join the GLM revolution today.*
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