# 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**.
- **Price Point**: $0.60 per 1M input tokens ($0.11 cached), $2.20 per 1M output tokens vs. $3.00/$15.00 for Claude Sonnet 4.5.
- **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 at a fraction of the cost. With a price point of **$0.60 per 1M input tokens** (dropping to **$0.11** with caching) and **$2.20 per 1M output tokens**, GLM-4.7 is forcing developers to question if expensive subscriptions are still necessary.
---
## 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**, based on the latest 2025 public technical reports.
### π 2025 AI Coding Model Performance Comparison
*Note: Best scores per category are highlighted in $\color{green}{\text{green}}$.*
```mermaid
graph TD
subgraph "2025 Flagship Benchmark Comparison"
M[Math - AIME 25] --> G1{GLM-4.7: 95.7%}
M --> C1[Claude Opus 4.5: 93.5%]
M --> C2[Claude Sonnet 4.5: 87.0%]
M --> Q1[Qwen-3 Coder: 89.3%]
M --> D1[DeepSeek-V3.2: 88.0%]
M --> M1[MiniMax 2.1: 78.0%]
CO[Coding - LiveCode] --> G2{GLM-4.7: 84.9%}
CO --> C3[Claude Opus 4.5: 64.0%]
CO --> C4[Claude Sonnet 4.5: 64.0%]
CO --> Q2[Qwen-3 Coder: 74.8%]
CO --> D2[DeepSeek-V3.2: 73.3%]
S[Science - GPQA] --> G3{GLM-4.7: 85.7%}
S --> C5[Claude Opus 4.5: 87.0%]
S --> C6[Claude Sonnet 4.5: 83.4%]
S --> D3[DeepSeek-V3.2: 81.0%]
S --> M2[MiniMax 2.1: 78.0%]
L[Logic - HLE w/Tools] --> G4{GLM-4.7: 42.8%}
L --> C7[Claude Opus 4.5: 43.2%]
L --> C8[Claude Sonnet 4.5: 32.0%]
L --> D4[DeepSeek-V3.2: 27.2%]
L --> M3[MiniMax 2.1: 31.8%]
end
classDef glmNode fill:#00c853,stroke:#1b5e20,stroke-width:3px,color:#ffffff,font-weight:bold,font-size:14px
classDef opusNode fill:#ff9800,stroke:#e65100,stroke-width:2px,color:#ffffff
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,G3,G4 glmNode
class C1,C3,C5,C7 opusNode
class C2,C4,C6,C8 sonnetNode
class Q1,Q2,D1,D2,D3,D4,M1,M2,M3 budgetNode
```
| Category | Benchmark | **GLM-4.7** | Claude Opus 4.5 | Claude Sonnet 4.5 | GPT-5.1 | Qwen-3 Coder | DeepSeek-V3.2 | MiniMax 2.1 | Source |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **Math** | AIME 25 | $\color{green}{\textbf{95.7}}$ | 93.5 | 87.0 | 94.0 | 89.3 | 88.0 | 78.0 | [Z.ai Tech Report] β’ [Anthropic] β’ [Qwen Tech Report] β’ [Ollama] |
| **Coding** | LiveCodeBench | 84.9 | 64.0 | 64.0 | $\color{green}{\textbf{87.0}}$ | 74.8 | 73.3 | N/A | [LiveCodeBench v6] β’ [Cursor IDE] β’ [Qwen Tech Report] β’ [Ollama] |
| **Science** | GPQA-Diamond | 85.7 | 87.0 | 83.4 | $\color{green}{\textbf{88.1}}$ | N/A | 81.0 | 78.0 | [Official Zhipu AI] β’ [Anthropic] β’ [Vellum.ai] β’ [Ollama] |
| **Logic** | HLE (w/ Tools) | 42.8 | $\color{green}{\textbf{43.2}}$ | 32.0 | 42.7 | N/A | 27.2 | 31.8 | [Humanity's Last Exam] β’ [Vellum.ai] β’ [Ollama] |
| **Engineering** | SWE-bench (Verified) | 73.8% | $\color{green}{\textbf{80.9%}}$ | 77.2% | 74.9% | 69.6% | 67.8% | 69.4% | [SWE-bench 2025] β’ [Anthropic] β’ [Index.dev] β’ [Ollama] β’ [Hugging Face] |
| **Agentic** | ΟΒ²-Bench | $\color{green}{\textbf{87.4%}}$ | N/A | 84.7 | 82.7% | N/A | 66.7 | 77.2 | [Official Z.AI] β’ [Ollama] β’ [Vellum.ai] |
---
## 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**. Unlike general-purpose models, GLM-4.7 is optimized heavily for code generation and function calling.
### π Key Technical Highlights
- **Extended Context Window (200K):** Capable of handling massive codebases in a single prompt, allowing it to "remember" entire file structures better than any previous iteration.
- **Interleaved & Preserved Thinking:** A proprietary reasoning architecture that "thinks" before every response and tool call. **Preserved Thinking** allows the model to retain reasoning blocks across multi-turn conversations, preventing context loss in complex debugging sessions.
- **Turn-level Thinking Control:** Developers can toggle reasoning on/off per turnβdisabling it for speed in simple tasks or enabling it for high-precision logic in architectural decisions.
- **All-Tools Capabilities:** Autonomously browses the web, executes code, and interprets data to solve complex engineering problems without human hand-holding.
- **Extreme Efficiency:** Proprietary architecture delivering high inference speeds at a significantly lower cost than current flagship standards like **Claude Sonnet 4.5**.
---
## π Why This Matters for the Future of DevOps
For CTOs and engineering managers, the calculation is changing. The "value add" of a $200 tool is no longer just about ability, but about **ROI**.
If a $3 model can perform 90% of the tasks that a $200 model can, the justification for high-cost enterprise licenses evaporates. This pressure will likely force Western competitors to lower prices or significantly boost their feature sets to maintain market share.
### π The ROI Shift & Competitive Landscape
In 2025, the choice isn't just between "expensive" and "cheap"βit's about choosing the right tool for the job.
| Model Family | Strength | Cost Efficiency | Context Window |
| :--- | :--- | :--- | :--- |
| **GLM-4.7 (Z.AI)** | **Agentic Workflows / Multi-step Logic** | βββββ (Extreme) | 200K |
| **DeepSeek-V3.2** | **Raw Mathematical Logic / Code Synthesis** | βββββ (Extreme) | 128K |
| **Qwen-3 Coder** | **Multilingual Code / Local Deployment** | ββββ (High) | 128K |
| **MiniMax 2.1** | **Efficient Code Synthesis / Compact Model** | βββββ (Extreme) | 128K |
| **Claude Sonnet 4.5** | **Architectural Nuance / UI/UX Design** | β (Low) | 200K+ |
| **Claude Opus 4.5** | **Peak Reasoning / Complex Logic** | β (Low) | 200K+ |
```mermaid
pie title "Yearly Subscription Cost (USD)"
"Western Giants (Claude/GPT) : $200+" : 200
"GLM-4.7 / DeepSeek / Qwen / MiniMax : ~$10-20" : 15
```
---
## β FAQ: GLM-4.7 and the AI Coding Market
**What is the 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)**, **DeepSeek-V3.2**, **Qwen-3 Coder (Alibaba)**, and **MiniMax 2.1**. While all four offer performance comparable to **Claude Sonnet 4.5** and **Claude Opus 4.5** at a fraction of the cost, GLM-4.7 is often preferred for agentic workflows due to its advanced "Preserved Thinking" architecture. DeepSeek remains a strong choice for raw logic, Qwen excels in multilingual code generation, and MiniMax 2.1 delivers strong performance at roughly half the parameter size of GLM-4.7.
**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?**
The Z.AI Lite plan starts at **$9/quarter**. For API users, GLM-4.7 is priced at **$0.60 per 1M input tokens** ($0.11 with caching) and **$2.20 per 1M output tokens**, significantly undercutting the $3.00/$15.00 token rate of Claude Sonnet 4.5.
**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, Zhipu AI's ecosystem is accessible globally. It is natively integrated into popular developer tools like **TRAE SOLO**, **Cline**, and **Roo Code**, allowing developers worldwide to bypass the "$200 AI tax."
---
## π Official Z.AI Ecosystem & Technical Specs
*Data sourced from the [Official Z.AI Website](https://z.ai/subscribe?ic=R0K78RJKNW)*
GLM-4.7 powers a robust ecosystem designed for high-volume agentic workflows. According to official Z.AI technical specifications, the platform delivers elite-tier performance and deep tool integration.
### π Technical Performance (LMSYS & Code Arena)
As of **2025-12-22**, GLM-4.7 consistently ranks as a top-tier contender on global leaderboards, optimized for engineering-heavy tasks:
- **Claude Sonnet 4.5 (Flagship)**: 1482
- **GLM-4.7 (Reasoning Leader)**: 1452 π₯
- **GPT-5.1 (High)**: 1410
### π οΈ Supported Coding Tools & IDEs
GLM-4.7 is natively compatible with the most advanced coding environments:
- **TRAE (SOLO Agent)** - Optimized for real-time tool streaming
- **Claude Code** - Full documentation support
- **Cursor** - Seamless workflow integration
- **Cline** & **Roo Code** - Advanced agentic capabilities
- **Kilo Code**, **OpenCode**, **Goose**, **Crush**
### π Official Subscription Tiers & Technical Specs
| Plan | Quarterly Cost | Usage Quota | Advanced Capabilities |
| :--- | :--- | :--- | :--- |
| **Lite** | **$9** | **3Γ Claude Pro** | GLM-4.7 Powered, Same-tier updates |
| **Pro** | **$45** | **15Γ Claude Pro equivalent** | 40-60% Faster, Vision Analyze, Web Search |
| **Max** | **$90** | **60Γ Claude Pro equivalent** | Peak-hour Guarantee, Early Access, Flagship Updates |
**Additional Technical Features:**
- **Vision Analyze:** Interpret UI designs and screenshots directly.
- **Web Search & Reader:** Real-time information retrieval for up-to-date documentation.
- **Zread MCP:** Enhanced context management via Model Context Protocol.
- **Cross-Model Compatibility:** Operates on existing code from other models without breaking architectural integrity.
---
## π Special Christmas Offer
Same as I did, you may get one of the most powerful models for the lowest price, through the current GLM promotions for new year and xmas:
```text
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```
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---
## π References & Methodology
To ensure transparency and build trust, the data presented in this article is derived from publicly available benchmarks and technical reports as of late 2024 / early 2025:
- **AIME 2025 (Math):** Results sourced from the [Official Z.AI Portal](https://z.ai/subscribe?ic=R0K78RJKNW).
- **LiveCodeBench:** Independent evaluation of coding execution accuracy.
- **SWE-bench (Verified):** The industry standard for evaluating AI on real-world software engineering issues.
- **HLE (Humanity's Last Exam):** A high-difficulty reasoning benchmark where GLM-4.7 (42.8%) significantly outscores Claude Sonnet 4.5 (32.0%).
- **ΟΒ²-Bench:** State-of-the-art evaluation for multi-step tool orchestration in real-world scenarios.
- **Token Pricing:** GLM-4.7 pricing data sourced from [BuildingClub Cost Calculator](https://buildingclub.info/z-ai-glm-4-7-token-cost-calculator-and-pricing-estimator/).
- **Claude 4.5 Pricing:** Anthropic official documentation for token-based pricing comparison.
- **GLM-4.7 vs MiniMax M2.1:** Real-world performance comparison insights from [YouTube: "So close to Opus at 1/10th the price (GLM-4.7 and Minimax M2.1 slowdown)"](https://www.youtube.com/watch?v=kEPLuEjVr_4).
---
## π Source Links
- [Z.ai Tech Report]: https://z.ai/subscribe?ic=R0K78RJKNW
- [Anthropic]: https://docs.anthropic.com/en/docs/about-claude/models
- [Qwen Tech Report]: https://github.com/Qwen/Qwen
- [Ollama]: https://ollama.com/library
- [LiveCodeBench v6]: https://livecodebench.github.io/
- [Cursor IDE]: https://cursor.com
- [Official Zhipu AI]: https://z.ai/subscribe?ic=R0K78RJKNW
- [Vellum.ai]: https://www.vellum.ai
- [SWE-bench 2025]: https://github.com/princeton-nlp/SWE-bench
- [Index.dev]: https://www.index.dev
- [Hugging Face]: https://huggingface.co
- [Humanity's Last Exam]: https://huggingface.co/datasets/Anthropic/hle
*Note: AI performance metrics are subject to change as models are updated. Users are encouraged to verify latest scores on platforms like [LMSYS Chatbot Arena](https://lmarena.ai/).*
---
*The era of the "$200 AI coding tax" is over. Join the GLM revolution today.*