From 21657f9dbb09b0b0610a438f012e26fcdd40e824 Mon Sep 17 00:00:00 2001 From: Gemini AI Date: Thu, 25 Dec 2025 00:29:51 +0400 Subject: [PATCH] Update README.md to use only Z.ai blog data - remove OpenRouter pricing and unsupported models --- README.md | 218 ++++++++++++++++-------------------------------------- 1 file changed, 64 insertions(+), 154 deletions(-) diff --git a/README.md b/README.md index bb8b243..31fef34 100644 --- a/README.md +++ b/README.md @@ -12,225 +12,135 @@ ### 💡 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{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: 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{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[Coding - LiveCode] --> G2_C{GLM-4.7: 84.9%} + 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{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: 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{GLM-4.7: 42.8%} - 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: -### � OpenRouter Pricing Comparison (USD per 1M Tokens) -*Current market rates via [OpenRouter.ai](https://openrouter.ai/models) as of late 2024/early 2025.* +### � 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. +### �️ 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.*