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# 🚀 GLM-4.7 vs. The $200 Giants: Is China’s $3 AI Coding Tool the New Market King?
<meta name="description" content="GLM-4.7 vs Claude Sonnet 4.5, GPT-5.1: Comprehensive benchmark comparison showing GLM-4.7's 95.7% AIME 2025 score, 84.9% LiveCodeBench, and $0.11/$2.20 token pricing. Cost-effective AI coding alternative for 2025.">
<meta name="keywords" content="GLM-4.7, Zhipu AI, AI coding model, Claude Sonnet 4.5 comparison, GPT-5.1 benchmark, cost-effective AI, token pricing, AIME 2025, LiveCodeBench, SWE-bench Verified, DeepSeek-V3.2, Qwen-3 Coder">
<meta name="author" content="GLM-4.7 Benchmark Analysis">
<meta name="robots" content="index, follow">
<meta property="og:title" content="GLM-4.7 vs The $200 Giants: Is China's $3 AI Coding Tool the New Market King?">
<meta property="og:description" content="Comprehensive 2025 benchmark analysis: GLM-4.7 beats Claude 4.5 in AIME (95.7% vs 87%) and LiveCodeBench (84.9% vs 64%) at 1/20th the cost.">
<meta property="og:type" content="article">
<meta property="og:url" content="https://github.com/roman-ryzenadvanced/glm.47-launched-public-benchmarks-specs-here-">
<meta property="og:image" content="https://z.ai/subscribe?ic=R0K78RJKNW">
<script type="application/ld+json">
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"@context": "https://schema.org",
"@type": "TechArticle",
"headline": "GLM-4.7 vs The $200 Giants: Is China's $3 AI Coding Tool the New Market King?",
"description": "Comprehensive benchmark comparison of GLM-4.7, Claude Sonnet 4.5, GPT-5.1, DeepSeek-V3.2, and Qwen-3 Coder for AI coding in 2025.",
"author": {
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"name": "GLM-4.7 Benchmark Analysis"
},
"datePublished": "2025-12-23",
"keywords": ["GLM-4.7", "Zhipu AI", "AI coding", "Claude Sonnet 4.5", "GPT-5.1", "benchmark comparison", "token pricing", "AIME 2025", "LiveCodeBench", "SWE-bench"],
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}
</script>
# 🚀 GLM-4.7 vs. The $200 Giants: Is China's $3 AI Coding Tool the New Market King?
```text
██████╗ ██╗ ███╗ ███╗ ██╗ ██╗ ███████╗
@@ -10,16 +47,16 @@
THE FRONTIER AGENTIC REASONING MODEL (2025)
```
### 💡 Key Takeaways (TL;DR for SEO/GEO)
### 💡 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 tokens vs. $15.00+ for Western flagship models.
- **Price Point**: $0.11 per 1M input tokens, $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 hovering around **$0.60 per 1M tokens**, GLM-4.7 is forcing developers to question if expensive subscriptions are still necessary.
**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 **$0.11 per 1M input tokens** and **$2.20 per 1M output tokens**, GLM-4.7 is forcing developers to question if expensive subscriptions are still necessary.
---
@@ -27,42 +64,59 @@ The global landscape for AI-powered development is shifting. While Western tools
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.
### 📊 Performance Visualization
### 📊 2025 AI Coding Model Performance Comparison
```mermaid
graph TD
subgraph "2025 Flagship Benchmark Comparison"
M[Math - AIME 25] --> G1{<b>GLM-4.7: 95.7%</b>}
M --> C1[Claude Sonnet 4.5: 87.0%]
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{<b>GLM-4.7: 84.9%</b>}
CO --> C2[Claude Sonnet 4.5: 64.0%]
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{<b>GLM-4.7: 85.7%</b>}
S --> C3[Claude Sonnet 4.5: 83.4%]
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{<b>GLM-4.7: 42.8%</b>}
L --> C4[Claude Sonnet 4.5: 32.0%]
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 rivalNode fill:#f1f8e9,stroke:#c5e1a5,stroke-width:1px,color:#558b2f
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,C2,C3,C4 rivalNode
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 Sonnet 4.5 | GPT-5.1 | Source |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **Math** | AIME 25 | **95.7** | 87.0 | 94.0 | [Z.ai Tech Report] |
| **Coding** | LiveCodeBench | **84.9** | 64.0 | 87.0 | [LiveCodeBench v6] |
| **Science** | GPQA-Diamond | **85.7** | 83.4 | 88.1 | [Official Zhipu AI] |
| **Logic** | HLE (w/ Tools) | **42.8** | 32.0 | 42.7 | [Humanity's Last Exam] |
| **Engineering** | SWE-bench (Verified) | **73.8%** | 77.2% | 74.9% | [SWE-bench 2025] |
| **Agentic** | τ²-Bench | **87.4%** | 87.2% | 82.7% | [Official Z.AI] |
| 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 | **95.7** | 93.5 | 87.0 | 94.0 | 89.3 | 88.0 | 78.0 | [Z.ai Tech Report][Anthropic][Qwen Tech Report][Ollama] |
| **Coding** | LiveCodeBench | **84.9** | 64.0 | 64.0 | 87.0 | 74.8 | 73.3 | N/A | [LiveCodeBench v6][Cursor IDE][Qwen Tech Report][Ollama] |
| **Science** | GPQA-Diamond | **85.7** | 87.0 | 83.4 | 88.1 | N/A | 81.0 | 78.0 | [Official Zhipu AI][Anthropic][Vellum.ai][Ollama] |
| **Logic** | HLE (w/ Tools) | **42.8** | 43.2 | 32.0 | 42.7 | N/A | 27.2 | 31.8 | [Humanity's Last Exam][Vellum.ai][Ollama] |
| **Engineering** | SWE-bench (Verified) | **73.8%** | **80.9%** | 77.2% | 74.9% | **69.6%** | **67.8%** | **69.4%** | [SWE-bench 2025][Anthropic][Index.dev][Ollama][Hugging Face] |
| **Agentic** | τ²-Bench | **87.4%** | N/A | 84.7 | 82.7% | N/A | 66.7 | 77.2 | [Official Z.AI][Ollama][Vellum.ai] |
---
## 🛠️ What is GLM-4.7? The Technical Breakdown
## 🛠️ 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.
@@ -89,12 +143,14 @@ In 2025, the choice isn't just between "expensive" and "cheap"—it's about choo
| **GLM-4.7 (Z.AI)** | **Agentic Workflows / Multi-step Logic** | ⭐⭐⭐⭐⭐ (Extreme) | 200K |
| **DeepSeek-V3.2** | **Raw Mathematical Logic / Code Synthesis** | ⭐⭐⭐⭐⭐ (Extreme) | 128K |
| **Qwen-3 Coder** | **Multilingual Code / Local Deployment** | ⭐⭐⭐⭐ (High) | 128K |
| **MiniMax 2.1** | **Efficient Code Synthesis / Compact Model** | ⭐⭐⭐⭐⭐ (Extreme) | 128K |
| **Claude Sonnet 4.5** | **Architectural Nuance / UI/UX Design** | ⭐ (Low) | 200K+ |
| **Claude Opus 4.5** | **Peak Reasoning / Complex Logic** | ⭐ (Low) | 200K+ |
```mermaid
pie title "Yearly Subscription Cost (USD)"
"Western Giants (Claude/GPT) : $200+" : 200
"GLM-4.7 / DeepSeek / Qwen : ~$10-15" : 15
"GLM-4.7 / DeepSeek / Qwen / MiniMax : ~$10-20" : 15
```
---
@@ -102,13 +158,13 @@ pie title "Yearly Subscription Cost (USD)"
## ❓ 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**, and **Qwen-3 Coder (Alibaba)**. While all three offer 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. DeepSeek remains a strong choice for raw logic, while Qwen excels in multilingual code generation.
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 approximately **$0.60 per 1M tokens**, significantly undercutting the $15.00/1M token rate of premium Western models.
The Z.AI Lite plan starts at **$9/quarter**. For API users, GLM-4.7 is priced at **$0.11 per 1M input tokens** 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.
@@ -156,6 +212,8 @@ GLM-4.7 is natively compatible with the most advanced coding environments:
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:
![GLM-4.7 Christmas 2025 Discount Coupon: 50% off first purchase plus 10% additional stackable discount with invite code R0K78RJKNW](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxIiBoZWlnaHQ9IjEiPjwvc3ZnPg==)
```text
___________________________________________________________
/ \
@@ -189,6 +247,25 @@ To ensure transparency and build trust, the data presented in this article is de
- **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%).
- **τ²-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).
---
## 🔗 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/).*