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README.md

🚀 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}}. Data sourced from Z.ai Official Blog.

flowchart TD
    subgraph GLM [🚀 GLM-4.7 Dominance]
        direction TB
        MATH[🧮 MATH<br/>AIME 25]
        CODING[💻 CODING<br/>LiveCodeBench v6]
        SCIENCE[🔬 SCIENCE<br/>GPQA-Diamond]
        LOGIC[🧠 LOGIC<br/>HLE w/Tools]
        ENGINEERING[⚙️ ENGINEERING<br/>SWE-bench]
        AGENTIC[🤖 AGENTIC<br/>τ²-Bench]
        
        GLM_MATH[<b>GLM-4.7<br/>95.7%</b>]
        GLM_CODING[<b>GLM-4.7<br/>84.9%</b>]
        GLM_SCIENCE[<b>GLM-4.7<br/>85.7%</b>]
        GLM_LOGIC[<b>GLM-4.7<br/>42.8%</b>]
        GLM_ENG[<b>GLM-4.7<br/>73.8%</b>]
        GLM_AGENT[<b>GLM-4.7<br/>87.4%</b>]
        
        MATH --> GLM_MATH
        CODING --> GLM_CODING
        SCIENCE --> GLM_SCIENCE
        LOGIC --> GLM_LOGIC
        ENGINEERING --> GLM_ENG
        AGENTIC --> GLM_AGENT
    end
    
    subgraph COMPETITORS [📊 Top Competitors]
        direction LR
        
        subgraph GEM [💎 Gemini 3.0 Pro]
            G_MATH[95.0%]
            G_CODING[<b>90.7%</b>]
            G_SCIENCE[<b>91.9%</b>]
            G_LOGIC[<b>45.8%</b>]
            G_ENG[76.2%]
            G_AGENT[<b>90.7%</b>]
        end
        
        subgraph GPT [🔵 GPT-5.1 High]
            P_MATH[94.0%]
            P_CODING[87.0%]
            P_SCIENCE[<b>88.1%</b>]
            P_LOGIC[42.7%]
            P_ENG[76.3%]
            P_AGENT[82.7%]
        end
        
        subgraph DS [🟢 DeepSeek-V3.2]
            D_MATH[93.1%]
            D_CODING[83.3%]
            D_SCIENCE[82.4%]
            D_LOGIC[40.8%]
            D_ENG[73.1%]
            D_AGENT[85.3%]
        end
        
        subgraph CLAUDE [🟣 Claude Sonnet 4.5]
            C_MATH[87.0%]
            C_CODING[64.0%]
            C_SCIENCE[83.4%]
            C_LOGIC[32.0%]
            C_ENG[<b>77.2%</b>]
            C_AGENT[87.2%]
        end
    end
    
    GLM_MATH -.-> G_MATH
    GLM_MATH -.-> P_MATH
    GLM_MATH -.-> D_MATH
    GLM_MATH -.-> C_MATH
    
    GLM_CODING -.-> G_CODING
    GLM_CODING -.-> P_CODING
    GLM_CODING -.-> D_CODING
    GLM_CODING -.-> C_CODING
    
    GLM_SCIENCE -.-> G_SCIENCE
    GLM_SCIENCE -.-> P_SCIENCE
    GLM_SCIENCE -.-> D_SCIENCE
    GLM_SCIENCE -.-> C_SCIENCE
    
    GLM_LOGIC -.-> G_LOGIC
    GLM_LOGIC -.-> P_LOGIC
    GLM_LOGIC -.-> D_LOGIC
    GLM_LOGIC -.-> C_LOGIC
    
    GLM_ENG -.-> G_ENG
    GLM_ENG -.-> P_ENG
    GLM_ENG -.-> D_ENG
    GLM_ENG -.-> C_ENG
    
    GLM_AGENT -.-> G_AGENT
    GLM_AGENT -.-> P_AGENT
    GLM_AGENT -.-> D_AGENT
    GLM_AGENT -.-> C_AGENT
    
    classDef glmNode fill:#00C853,stroke:#004D40,stroke-width:3px,color:#FFFFFF,font-weight:bold,font-size:13px,radius:8px
    classDef geminiNode fill:#FFB74D,stroke:#E65100,stroke-width:2px,color:#FFFFFF,font-weight:bold,font-size:12px,radius:6px
    classDef gptNode fill:#64B5F6,stroke:#1565C0,stroke-width:2px,color:#FFFFFF,font-weight:bold,font-size:12px,radius:6px
    classDef deepseekNode fill:#4DB6AC,stroke:#00695C,stroke-width:2px,color:#FFFFFF,font-weight:bold,font-size:12px,radius:6px
    classDef claudeNode fill:#AB47BC,stroke:#6A1B9A,stroke-width:2px,color:#FFFFFF,font-weight:bold,font-size:12px,radius:6px
    classDef categoryNode fill:#37474F,stroke:#263238,stroke-width:2px,color:#ECEFF1,font-weight:bold,font-size:11px,radius:4px
    classDef subgraphNode fill:#FAFAFA,stroke:#B0BEC5,stroke-width:2px,stroke-dasharray: 5 5
    
    class GLM_MATH,GLM_CODING,GLM_SCIENCE,GLM_LOGIC,GLM_ENG,GLM_AGENT glmNode
    class G_MATH,G_CODING,G_SCIENCE,G_LOGIC,G_ENG,G_AGENT geminiNode
    class P_MATH,P_CODING,P_SCIENCE,P_LOGIC,P_ENG,P_AGENT gptNode
    class D_MATH,D_CODING,D_SCIENCE,D_LOGIC,D_ENG,D_AGENT deepseekNode
    class C_MATH,C_CODING,C_SCIENCE,C_LOGIC,C_ENG,C_AGENT claudeNode
    class MATH,CODING,SCIENCE,LOGIC,ENGINEERING,AGENTIC categoryNode
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

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

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