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Update README.md to use only Z.ai blog data - remove OpenRouter pricing and unsupported models

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2025-12-25 00:29:51 +04:00
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### 💡 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.
- 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 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.
**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) or use via [OpenRouter](https://openrouter.ai/).
---
## ⚔️ 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.
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 $\color{green}{\text{green}}$.*
*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).*
```mermaid
graph TD
subgraph "2025 Flagship Benchmark Comparison"
M[Math - AIME 25] --> G1{<b>GLM-4.7: 95.7%</b>}
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: 96.0%]
M --> M1[MiniMax 2.1: 78.0%]
M --> C1[Claude Sonnet 4.5: 87.0%]
M --> G2[Gemini 3.0 Pro: 95.0%]
M --> D1[DeepSeek-V3.2: 93.1%]
M --> P1[GPT-5.1 High: 94.0%]
CO[Coding - LiveCode] --> G2{<b>GLM-4.7: 84.9%</b>}
CO --> C3[Claude Opus 4.5: 64.0%]
CO --> C4[Claude Sonnet 4.5: 64.0%]
CO --> Q2[Qwen-3 Coder: 74.8%]
CO[Coding - LiveCode] --> G2_C{<b>GLM-4.7: 84.9%</b>}
CO --> C2[Claude Sonnet 4.5: 64.0%]
CO --> D2[DeepSeek-V3.2: 83.3%]
CO --> P2[GPT-5.1 High: 87.0%]
CO --> G2_CO[Gemini 3.0 Pro: 90.7%]
S[Science - GPQA] --> G3{<b>GLM-4.7: 85.7%</b>}
S --> C5[Claude Opus 4.5: 87.0%]
S --> C6[Claude Sonnet 4.5: 83.4%]
S --> D3[DeepSeek-V3.2: 80.3%]
S --> M2[MiniMax 2.1: 78.0%]
S --> C3[Claude Sonnet 4.5: 83.4%]
S --> D3[DeepSeek-V3.2: 82.4%]
S --> P3[GPT-5.1 High: 88.1%]
S --> G3_S[Gemini 3.0 Pro: 91.9%]
L[Logic - HLE w/Tools] --> G4{<b>GLM-4.7: 42.8%</b>}
L --> C7[Claude Opus 4.5: 43.2%]
L --> C8[Claude Sonnet 4.5: 28.0%]
L --> D4[DeepSeek-V3.2: 21.7%]
L --> M3[MiniMax 2.1: 31.8%]
L --> C4[Claude Sonnet 4.5: 32.0%]
L --> D4[DeepSeek-V3.2: 40.8%]
L --> P4[GPT-5.1 High: 42.7%]
L --> G4_L[Gemini 3.0 Pro: 45.8%]
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
class G1,G2_C,G3,G4 glmNode
class C1,C2,C3,C4 sonnetNode
class D1,D2,D3,D4,G2,P1,P2,P3,P4,G2_CO,G3_S,G4_L 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 | 94.6 | 89.3 | $\color{green}{\textbf{96.0}}$ | 78.0 | [Z.ai](https://z.ai/blog/glm-4.7) • [Anthropic](https://www.anthropic.com) • [Qwen](https://qwenlm.github.io/blog/qwen2.5-coder/) • [Ollama](https://ollama.com/library/glm4) |
| **Coding** | LiveCodeBench | 84.9 | 64.0 | 64.0 | $\color{green}{\textbf{87.0}}$ | 74.8 | 83.3 | N/A | [LiveCodeBench](https://livecodebench.github.io/leaderboard.html) • [Cursor](https://cursor.com/blog) • [Qwen](https://qwenlm.github.io/blog/qwen2.5-coder/) • [Ollama](https://ollama.com/library/glm4) |
| **Science** | GPQA-Diamond | 85.7 | 87.0 | 83.4 | $\color{green}{\textbf{88.1}}$ | N/A | 80.3 | 78.0 | [Zhipu AI](https://zhipuai.cn/) • [Anthropic](https://www.anthropic.com) • [Vellum](https://www.vellum.ai/llm-leaderboard) • [Ollama](https://ollama.com/library/glm4) |
| **Logic** | HLE (w/ Tools) | 42.8 | $\color{green}{\textbf{43.2}}$ | 28.0 | 31.6 | N/A | 21.7 | 31.8 | [Scale AI](https://scale.com/leaderboard/humanitys_last_exam) • [Vellum](https://www.vellum.ai/llm-leaderboard) • [Ollama](https://ollama.com/library/glm4) |
| **Engineering** | SWE-bench (Verified) | 73.8% | $\color{green}{\textbf{80.9%}}$ | 77.2% | 74.9% | 69.6% | 73.1% | 69.4% | [SWE-bench](https://www.swebench.com/) • [Anthropic](https://www.anthropic.com) • [Index](https://index.dev/blog/) • [Ollama](https://ollama.com/library/glm4) • [Hugging Face](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard) |
| **Agentic** | τ²-Bench | $\color{green}{\textbf{87.4%}}$ | N/A | 84.7 | 82.7% | N/A | 66.7 | 77.2 | [Official Z.AI](https://z.ai/blog/glm-4.7) • [Ollama](https://ollama.com/library/glm4) • [Vellum](https://www.vellum.ai/llm-leaderboard) |
| 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](https://z.ai/blog/glm-4.7) |
| **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) |
| **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) |
| **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) |
| **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) |
| **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) |
---
## 🛠️ 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**.
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.
### 🚀 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 turn—disabling 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**.
### 🚀 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.
---
## 📈 The ROI Shift & Competitive Landscape
## 📈 GLM-4.7 vs GLM-4.6: Key Improvements
The "value add" of an AI coding tool is no longer just about its ability to generate code, but about its **ROI**. With the entry of GLM-4.7 into global marketplaces like **OpenRouter**, the pricing floor has been permanently lowered.
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:
### <20> OpenRouter Pricing Comparison (USD per 1M Tokens)
*Current market rates via [OpenRouter.ai](https://openrouter.ai/models) as of late 2024/early 2025.*
### <20> 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%** |
| Model Name | Input Price | Output Price | Context | Provider |
| :--- | :--- | :--- | :--- | :--- |
| **GLM-4.7 (Zhipu)** | **$0.10** | **$0.10** | 128K+ | Z.AI via OpenRouter |
| **DeepSeek V3.2** | $0.22 | $0.32 | 163K | DeepSeek |
| **Qwen 2.5 Coder 32B** | $0.03 | $0.11 | 32K | Alibaba |
| **Claude Sonnet 4.5** | $3.00 | $15.00 | 200K | Anthropic |
| **Claude Opus 4.5** | $15.00 | $75.00 | 200K | Anthropic |
| **GPT-4o (Omni)** | $2.50 | $10.00 | 128K | OpenAI |
| **GPT-5.1 (Early Access)**| $5.00 | $15.00 | 400K+ | OpenAI |
### 🚀 Why OpenRouter is the Strategic Choice
By using GLM-4.7 via OpenRouter, developers gain:
- **No Monthly "Tax":** Pay only for what you use instead of a flat $20/month fee.
- **Aggregated Access:** Switch between GLM-4.7 for heavy reasoning and Qwen-Coder for rapid, cheap boilerplate.
- **Global Availability:** Bypasses regional restrictions, bringing SOTA Chinese intelligence to Western workflows.
### <20>️ 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 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.
**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?**
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.
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).
**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."
---
## 🌐 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.
### 📊 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 🥇
- **GPT-5.1 (High)**: 1410
### 🛠️ 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**
### 💎 Official Subscription Tiers & Technical Specs
| Plan | Quarterly Cost | Usage Quota | Advanced Capabilities |
| :--- | :--- | :--- | :--- |
| **Lite** | **$9** | **3× Claude Pro** | GLM-4.7 Powered, Same-tier updates |
| **Pro** | **$45** | **15× Claude Pro equivalent** | 40-60% Faster, Vision Analyze, Web Search |
| **Max** | **$90** | **60× 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.
---
## 🎄 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:
![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
___________________________________________________________
/ \
| 🎄 GLM-4.7 CHRISTMAS EXCLUSIVE CODE 🎄 |
| _______________________________________________________ |
| | | |
| | 🎁 50% OFF FIRST PURCHASE | |
| | 🚀 +10% ADDITIONAL STACKABLE DISCOUNT | |
| |_______________________________________________________| |
| |
| INVITE CODE: R0K78RJKNW |
| STATUS: ACTIVE [STRICTLY LIMITED] |
| |
\___________________________________________________________/
\ ^__^
\ (oo)\_______
(__)\ )\/\
||----w |
|| ||
```
🔗 **Get +10% additional discount here:** [https://z.ai/subscribe?ic=R0K78RJKNW](https://z.ai/subscribe?ic=R0K78RJKNW)
🎟️ **Discount Invite Code:** `R0K78RJKNW`
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**.
---
## 📚 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%).
- **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](https://www.youtube.com/watch?v=kEPLuEjVr_4).
- **OpenRouter Model Catalog:** Real-time pricing and availability data from [OpenRouter.ai](https://openrouter.ai/models).
All data presented in this article is derived from the [Z.ai Official Technical Report](https://z.ai/blog/glm-4.7) (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](https://z.ai/blog/glm-4.7)
- [Anthropic Benchmarks](https://www.anthropic.com/news/claude-3-5-sonnet)
- [Qwen Technical Report](https://qwenlm.github.io/blog/qwen2.5-coder/)
- [LiveCodeBench Leaderboard](https://livecodebench.github.io/leaderboard.html)
- [SWE-bench Verified](https://www.swebench.com/)
- [Humanity's Last Exam (HLE)](https://scale.com/leaderboard/humanitys_last_exam)
- [LMSYS Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
- [τ²-Bench Tool Use](https://z.ai/blog/glm-4.7)
- [Vellum LLM Leaderboard](https://www.vellum.ai/llm-leaderboard)
- [BuildingClub Pricing Data](https://buildingclub.info/z-ai-glm-4-7-token-cost-calculator-and-pricing-estimator/)
*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.*
*The era of "$200 AI coding tax" is over. Join the GLM revolution today.*