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🚀 GLM-4.7 vs. The $200 Giants: Is China's $3 AI Coding Tool the New Market King?
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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 or use via OpenRouter.
⚔️ 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}}.
╔════════════════════════════════════════════════════════════════════════════════════════╗
║ 🏆 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) ║
╚════════════════════════════════════════════════════════════════════════════════════════╝
| 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 |
| Coding | LiveCodeBench v6 | 84.9 | 64.0 | 87.0 | 83.3 | \color{green}{\textbf{90.7}} |
Z.ai |
| Science | GPQA-Diamond | 85.7 | 83.4 | 88.1 | 82.4 | \color{green}{\textbf{91.9}} |
Z.ai |
| Logic | HLE (w/ Tools) | 42.8 | 32.0 | 42.7 | 40.8 | \color{green}{\textbf{45.8}} |
Z.ai |
| Engineering | SWE-bench (Ver.) | 73.8% | \color{green}{\textbf{77.2%}} |
76.3% | 73.1% | 76.2% | Z.ai |
| Agentic | τ²-Bench | 87.4% | 87.2% | 82.7% | 85.3% | \color{green}{\textbf{90.7%}} |
Z.ai |
📊 Additional Sources: HuggingFace Model Card | Ollama Library | LLM-Stats Analysis | Vertu Comparison
🛠️ 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, GLM-4.7 delivers significant gains across core benchmarks compared to its predecessor GLM-4.6:
<EFBFBD> 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% |
<EFBFBD>️ 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 and through OpenRouter. For detailed pricing, visit Z.ai subscription page.
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. 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 (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
- HuggingFace Model Card
- Ollama Library
- LLM-Stats Analysis
- Vertu: GLM-4.7 vs Claude Opus 4.5
The era of "$200 AI coding tax" is over. Join the GLM revolution today.