AI Programming Tool Selection Recommendation

Core conclusion: It is recommended to use GLM-4.7 as the core model and cooperate with Claude Code CLI to build AI engineering capabilities.

Executive summary

After an in-depth analysis of current mainstream AI programming tools and models, we recommend:

1. Model Selection: Use GLM-4.7 as the main model

• Principle: Use the new rather than the old. GLM-4.7 is the latest flagship of GLM in December 2025.
• Capability: Code capability surpasses GPT-5 (official claim), total parameters 358B
• Value for money: 1/7 - 1/12 the price of Claude Opus
• Ranked 6th in the world, the highest ranking among domestic open source models

2. Engineering capability: Use Claude Code CLI + GLM-4.7 combination

• GLM-4.7 monthly fee: ¥40-400 (much lower than Claude’s $20-200)
• Can be directly configured to use GLM-4.7 as the underlying model
• Mature Agent architecture and tool ecosystem
• Project-level context management capabilities
• Token cost is only 12% of native Claude
• CLI is more powerful, more flexible and more engineering than editor plug-ins

3. Plug-in Ecosystem: Claude Code CLI has a complete engineering plug-in system

• PR Review Toolkit: automated code review (testing, error handling, type design, code quality)
• Development Workflows: Python, JavaScript/TypeScript, Backend, Frontend professional workflows
• Document Skills: Excel, Word, PowerPoint, PDF document processing
• Code Quality Tools: Code refactoring, technical debt management, architecture review
• Enterprise Plugins: 150+ commands, 74+ professional agents, GitHub integration
• For details, see: Claude Plugins Marketplace

4. Tool Selection: It is recommended to use CLI as the main tool

• CLI has a more complete tool chain and more powerful Agent capabilities
• Does not depend on a specific IDE and can be used in any environment
• Better suited for handling complex, multi-step tasks
• Developers are encouraged to experience IDEs such as Cursor and Qoder to understand cutting-edge technologies, but it is not recommended as the main tool.

---

1. Why choose GLM-4.7?

1.0 The Importance of Model Quality

Before in-depth comparison, a core point must be made clear: Model quality is the decisive factor in AI-assisted programming.

Why is the quality of the model so critical?

1. Garbage model = pure waste

• No matter how to optimize the prompt and adjust the interaction method
• Unable to understand complex requirements → generate incorrect code → waste debugging time
• Unable to understand the project context → Need to explain repeatedly → Wasted communication costs
• Unable to generate usable code → requires manual rewriting → wasting development time

2. Good model = doubled efficiency

• Accurately understand requirements → Generate usable code once
• Deep understanding of context → Reduce repetitive explanations
• High code quality → low debugging costs

3. Cost Trap

• Use cheap but less capable models → require multiple attempts → actual costs are higher
• Use capable models → Get it right the first time → Lower total costs

Core conclusion: In AI-assisted programming, Model quality > Tool functionality > Interaction skills. Using junk models, the best tools and interaction techniques are in vain.

Comparison of real cases

Assume that a medium complexity requirement (CRUD + business logic) is completed:

Model quality	Number of interactions	Total time spent	Success rate	Conclusion
Top Model (Claude Opus/GLM-4.7)	2-3 times	2-4 hours	85-95%	Efficient completion
Medium Model	5-8 times	1-2 days	60-75%	Barely usable
Junk model	10+ times	3-5 days	30-50%	Pure waste

Conclusion: Although the unit price is high when using the top model, the total time-consuming and total cost are actually lower.

1.1 Use the new rather than the old: the core principle of model iteration

Comparative dimensions	GLM-4.7	GLM-4.6	Claude Opus 4.5	GPT 5.2	GPT 5.1-codex-max
Release Time	2025.12.22	2025.09.30	2025.11.24	2025.12.11	2025.11.19
Coding Ability	Strongest	Stronger	Strongest	Strongest	Strongest
World Ranking	6th	7th	2nd	3rd	-
Price (¥/million tokens)	¥0.6-2.2	¥0.6-2.2	¥5-25	¥1.75-14	¥1.25-10
Affiliation	Zhipu AI (China)	Zhipu AI	Anthropic (USA)	OpenAI (USA)	OpenAI (USA)
Timeliness	⭐⭐⭐⭐⭐ Latest	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐

Star description: ⭐ The more, the newer and more advanced the technology (both are the latest generation models in 2025)

Data source: LLM Stats, official documents of each model, authoritative benchmark test list

Authoritative Ranking: According to LLM Stats latest data (2025.12.23):

• GLM-4.7: No. 6 in the world, the highest ranking among domestic open source models
• GLM-4.6: 7th in the world, 621 points
• Claude Opus 4.5: Ranked 1st for code quality, top 5 overall
• GPT 5.2: Top 3 in overall ranking, No. 1 in AIME 2025 (1.0 points)

Qoder model description: Qoder uses multi-model backend intelligent routing strategy to automatically select the most appropriate model according to the task type:

• Claude Series: Good at code understanding and reconstruction
• GPT Series: Strong code generation capabilities
• Gemini Series: Excellent multi-modal capabilities
• Tongyi Qianwen Series: Ali’s self-developed model

Qoder provides Model Tier Selector (Model Tier Selector), supporting four levels:

• Smart Routing: Adaptive algorithm automatically selects the most appropriate model (recommended default)
• Extreme Performance: Use the best available model
• Cost-effective: cost-effective model selection
• Basic Lightweight: Basic model service (free)

Source:

• Qoder Official Documentation - Model Hierarchy Selector
• Jimmy Song - Qoder in-depth review

Core argument:

• GLM-4.7 is the latest flagship of Zhipu (released on December 22, 2025), enhanced for Agentic Coding scenarios
• Ranked 6th in the world (Source: LLM Stats), achieving leading performance of open source models in multiple programming benchmarks
• The code capability surpasses GPT-5 (official claim) and belongs to the latest generation in 2025 along with Claude Opus 4.5 and GPT 5.2
• The price is only about 1/7 of Claude Opus, with significant cost advantages
• The total parameters are 358B, which is one of the open source models with the largest number of parameters at present.
• MIT open source license, free to use and modify
• New, not old: GLM-4.7 is the latest release in December 2025

1.2 GLM-4.7 Core Advantages

Technical advantages

Coding ability
├── Agentic Coding scene enhancement
├── Long-range mission planning capabilities
├── Tool synergy
└── Front-end aesthetics (Artifacts)

general ability
├── Reply concise and natural
├── Writing is immersive
└── Stronger command compliance

Performance

• Code Power: Claims to surpass GPT-5
• Open Source Performance: Multiple benchmarks SOTA
• Tool Support: Native support for mainstream tools such as Claude Code and Cline

Cost advantage

Model	Input Price	Output Price	Price Ratio
GLM-4.7	¥0.6-2.2	¥2.2-6.6	1x
Claude Opus 4.5	¥5-25	¥15-75	7-12x
GPT 5.2	¥1.75-14	¥5.25-42	3-7x

Cost comparison: For the same task volume, the cost of GLM-4.7 is about 12% of Claude Opus and 30% of GPT 5.2

---

2. GLM-4.7 + Claude Code CLI: the best engineering combination

2.1 Why choose Claude Code CLI?

Core Value: AI Engineering Infrastructure

Claude Code CLI
├── Mature Agent architecture
│ ├── Subagent mechanism (Task tool)
│ ├── Tool calling ability
│ └── Context Management
├── Complete tool ecosystem
│ ├── Read/Write/Edit file operations
│ ├── Bash command execution
│ ├── Grep Search
│ └── LSP integration
└── Project-level capabilities
├── CLAUDE.md configuration
├── Global context understanding
└── Multi-file collaboration

Key Features: Configurable to use GLM-4.7

Claude Code CLI supports custom model configuration and can directly use GLM-4.7 as the underlying model:

1. GLM-4.7 provides: the latest coding capabilities and reasoning capabilities
2. Claude Code provides: mature engineering framework and tool chain
3. Combination Effect: Latest model + mature architecture = best engineering solution
4. Low-cost subscription: GLM-4.7 Coding Plan monthly fee is only ¥40-400

IMPORTANT BENEFITS: Using Claude Code CLI + GLM-4.7 combination:

• GLM-4.7 Subscription Fee: ¥40-400/month (much lower than Claude’s $20-200 ≈ ¥140-1400)
• Token cost: ¥0.6-2.2/million tokens (12% of Claude)
• Claude Code CLI: Free to install and use

2.2 Comparison with other solutions

Solution	Model	Engineering capability	Monthly fee	Token/point cost	Recommendation
GLM-4.7 + Claude Code CLI	GLM-4.7 (6th in the world)	Mature	¥40-400	¥0.6-2.2/M	⭐⭐⭐⭐⭐
Qoder	Multi-model intelligent routing	Newer	$20-60 (about ¥140-420)	2000-6000 points/month	⭐⭐⭐⭐
Claude Code	Claude Opus 4.5	Mature	$20-200 (approx. ¥140-1400)	$1-5/M (approx. ¥7-35/M)	⭐⭐⭐⭐
Cursor	Claude/GPT 5.2	IDE integration	$20-200 (approx. ¥140-1400)	$0.25-2/M (approx. ¥1.75-14/M)	⭐⭐⭐⭐

Conclusion: GLM-4.7 + Claude Code CLI achieves the best in model timeliness, engineering capabilities, and cost.

Cost comparison (monthly fee + Token):

• GLM-4.7 + Claude Code CLI: ¥40-400 + ¥0.6-2.2/million tokens
• Qoder: Pro $20 (approximately ¥140), Pro+ $60 (approximately ¥420), including 2000-6000 points/month
• Claude Code native: $20-200 (approximately ¥140-1400) + $1-5/M (approximately ¥7-35/million tokens)
• Cursor: $20-200 (approximately ¥140-1400) + $0.25-2/M (approximately ¥1.75-14/million tokens)

Advantages:

• GLM-4.7 monthly fee is only 29-295% of Qoder (depending on version)
• GLM-4.7 monthly fee is only 29-295% of Claude's
• Token cost is only 12% of Claude's
• Ranked 6th in the world, with the strongest coding ability

---

3. Product Comparative Analysis

3.1 IDE comparison: Qoder vs Cursor

Core function comparison table

Function Category	Function	Qoder	Cursor	Description
Infrastructure	Based on VS Code	✅	✅	Both based on VS Code
Plug-in support	VS Code plug-in	✅	✅	Fully compatible with VS Code ecosystem
Code Completion	Tab auto-completion	⭐⭐⭐	⭐⭐⭐⭐⭐	Cursor is smoother, similar to Copilot
	Multi-line editing	✅	✅	Cursor is more mature
	Intelligent rewriting	✅	✅	Cursor experience is better
	Cursor Prediction	❌	✅	Cursor Exclusive
AI Chat	Chat	✅	✅	Both
	Codebase Q&A	✅	✅	All supported
	@symbol reference code	✅	✅	All supported
	Image input	❌	✅	Cursor exclusive
	Web Search	❌	✅	@Web Features of Cursor
	Document reference	✅	✅	All supported
	Instant application	✅	✅	All supported
Code Edit	Ctrl+K Quick Edit	✅	✅	All supported
	Terminal command generation	❌	✅	Cursor exclusive
	Quick question	✅	✅	All supported
Agent	Agent mode	✅	✅	All have agent functions
	Composer	❌	✅	Cursor's own model, 4x faster
	Multi-Agent	Basics	⭐⭐⭐⭐⭐	Cursor's dedicated multi-agent interface
	Parallel execution	❌	✅	Cursor can parallelize multiple agents
Code Base Understanding	100,000+ file levels	✅	⭐⭐⭐⭐	Qoder’s ultra-large scale advantage
	Vector retrieval	✅	✅	all support semantic retrieval
	Code Difference Visualization	❌	✅	Cursor Exclusive
Document Generation	Repo Wiki	✅	❌	Qoder Exclusive
	Quest Mode	✅	❌	Qoder Exclusive
	Spec-Driven	✅	❌	Qoder Exclusive
Multimodal	Image input	❌	✅	Cursor exclusive
	Voice input	❌	✅	Cursor 2.0 support
Chinese support	Native Chinese	⭐⭐⭐⭐⭐	⭐⭐⭐	Qoder optimized for Chinese
Payment Method	Alipay	✅	❌	Qoder Exclusive
	Credit Card	❌	✅	Cursor Main Methods
Price	Monthly fee	$20-60	$20-200	Qoder cheaper
	First Month Offer	$2	❌	Qoder Exclusive
	Limited time offer	50% off	❌	Qoder currently offers: Subscription/renewal for Pro/Pro+/Ultra at half price, see Details of Offer
Community Ecology	User Community	⭐⭐⭐	⭐⭐⭐⭐⭐	Cursor is more mature
	Tutorial Resources	⭐⭐⭐	⭐⭐⭐⭐⭐	Cursor has more resources
	Market Maturity	Newer (2025.8)	Mature (2024)	Cursor Older

Important Note:

• ✅ means support, ❌ means not support
• ⭐ indicates functional maturity/experience rating (1-5 stars)
• Cursor has more comprehensive functions, especially in code completion, multi-modality, and agent collaboration.
• Qoder has unique advantages in understanding large-scale code bases (100,000+ files), automatic document generation (Repo Wiki), and Chinese support

Qoder unique features

Function	Description
Repo Wiki	✅ Automatically generate project documents/knowledge base and continuously track code and document changes
Quest Mode	✅ Task-oriented autonomous programming, automatically converting requirements into specifications and executing them
Spec-Driven Programming	✅ Specification-driven programming, developers only need to enter natural language requirements
100,000+ file-level retrieval	✅ One-time retrieval of extremely large-scale code base (Cursor is about tens of thousands of file-level searches)
Native Chinese support	✅ Deeply optimized for Chinese scenes
Alipay payment	✅ Convenient payment for domestic users
$2 off first month	✅ Low-cost trial

Cursor unique features

Function	Description
Composer	✅ Cursor's own cutting-edge model, 4 times faster, task completion < 30 seconds
Multi-Agent Interface	✅ Dedicated multi-agent collaboration interface that can run multiple agents in parallel
Tab auto-completion	✅ Industry-leading code completion experience, similar to GitHub Copilot
Cursor Prediction	✅ Predict the next cursor position, seamless navigation code
Image input	✅ Chat supports images as context
Voice input	✅ Cursor 2.0 supports voice input
Web Search	✅ @Web function to get the latest information
Terminal command generation	✅ Use Ctrl+K to generate commands in the terminal
Code Difference Visualization	✅ A more intuitive display of code changes
VS Code Deep Integration	✅ More mature ecological integration and compatibility
Active Community	✅ Large user base and rich tutorial resources

Function comparison summary

Function Type	Qoder	Cursor
Code completion experience	⭐⭐⭐ Basic completion	⭐⭐⭐⭐⭐ Industry leading
Code base understanding scale	⭐⭐⭐⭐⭐ 100,000+ files	⭐⭐⭐⭐ Tens of thousands of files
Multi-agent collaboration	⭐⭐⭐ Basic Agent	⭐⭐⭐⭐⭐ Composer + Multi-Agent
Automatic Document Generation	⭐⭐⭐⭐⭐ Repo Wiki	❌ Not Supported
Multi-modal support	❌ Not supported	⭐⭐⭐⭐⭐ Picture + Voice
Chinese support	⭐⭐⭐⭐⭐ Native optimization	⭐⭐⭐ Partially supported
Community Ecology	⭐⭐⭐ Newer	⭐⭐⭐⭐⭐ Mature and Active
Functional Completeness	⭐⭐⭐ Core Features	⭐⭐⭐⭐⭐ Full Features

Selection suggestions:

Recommended priority: CLI as main, IDE as supplement

1. Main Tool (Highly Recommended)

• Prioritize all scenarios → GLM-4.7 + Claude Code CLI
• Reasons: Most cost-effective, strongest engineering capabilities, not dependent on IDE

2. IDE Supplement (optional)

• Need to understand a very large code base (100,000+ files) → Qoder (Chinese project)
• Pursue code completion experience → Cursor
• Need to automatically generate project documents → Qoder
• Requires multi-modal input (picture/voice) → Cursor
• Requires multi-agent collaboration → Cursor

Core Points:

• IDE suitable for specific scenarios (Chinese, very large code base, multi-modal)
• But CLI is the mainstay of engineering and is more suitable for complex tasks and team collaboration
• It is recommended that the team use CLI as the main tool and IDE only as a supplement.

3.2 CLI comparison: Qoder CLI vs Claude Code CLI

Compare dimensions	Qoder CLI	Claude Code CLI (with GLM-4.7)
CODE GENERATION	✅	✅
File Operations	✅ Grep/Read/Write	✅ Read/Write/Edit
Shell Commands	✅ Bash	✅ Bash
Code Review	✅ CodeReview	✅
Sub-Agent Mechanism	✅	✅ Task Tools
Project Context	✅	✅ CLAUDE.md
Underlying model	Multi-model intelligent routing	Configurable (GLM-4.7 optimal)
Maturity	Newer (2025.10)	Mature (2025.8)
Monthly fee	Pro $20 (approximately ¥140), Pro+ $60 (approximately ¥420)	¥40-400
Token cost	2000-6000 points/month	¥0.6-2.2/million tokens

Key differences:

• Qoder CLI: Multi-model intelligent routing (Claude/GPT/Gemini/Tongyi Qianwen), Pro version $20/month (2000 points), Pro+ version $60/month (6000 points)
• Claude Code CLI + GLM-4.7: monthly fee ¥40-400, Token cost ¥0.6-2.2/million tokens

Selection suggestions: Recommended Claude Code CLI + GLM-4.7 combination

• Claude Code provides a mature engineering framework
• GLM-4.7 provides the latest model capabilities (ranked 6th in the world)
• Cost vs. Qoder depending on usage
• Token cost is only 12% of Claude and 30% of GPT

3.3 Comparison of mainstream AI Coding Plan prices and usage restrictions

In order to give readers a clearer understanding of the pricing strategies and quota limits of each AI programming tool, the following is a detailed comparison of mainstream Coding Plans:

AI Coding Plan	Monthly fee	Refresh cycle	Usage quota (per cycle)	Additional instructions
GLM-4.7 Lite	¥40 (Active price ¥54/season ≈ ¥18/month)	Every 5 hours	About 120 Prompts	Equivalent to 3 times the usage of Claude Pro
GLM-4.7 Pro	¥100 (Active price ¥270/season ≈ ¥90/month)	Every 5 hours	About 600 Prompts	Equivalent to part of Claude Max usage
GLM-4.7 Max	¥400 (Active price ¥540/season ≈ ¥180/month)	Every 5 hours	About 2400 Prompts	Equivalent to 3 times the usage of Claude Max 5x
Claude Code Pro	$20 (≈¥140)	Every 7 days	Basic quota	New weekly limit from August 2025
Claude Code Teams	$40/person/month (≈¥280)	Every 7 days	Team quota	New weekly limit from August 2025
Claude Code Max	$200 (≈¥1400)	Every 7 days	Large amounts	New weekly limit from August 2025
ChatGPT Plus	$20 (≈¥140)	Every 5 hours	30-150 messages	Also weekly limit (~6-7 full sessions)
ChatGPT Pro	$200 (≈¥1400)	Every 5 hours	300-1500 local messages or 50-400 cloud tasks	Codex CLI, Chat, Agent consumption premium requests
GitHub Copilot Free	$0	Monthly	2000 code completions + 50 premium requests	-
GitHub Copilot Pro	$10/month or $100/year (≈¥70-700/year)	Monthly	Unlimited standard completion + Premium requests limit	Additional charges apply
Gemini Code Assist Standard	$19 (≈¥130)	Daily	Unlimited code completion + 33 PR reviews/day	-
Gemini Code Assist Enterprise	$45 (≈¥310)	Daily	Unlimited code completion + 100 PR reviews/day	-
Qoder Pro	$20 (≈¥140)	Monthly	2000 points	Additional charges apply
Qoder Pro+	$60 (≈¥420)	Monthly	6000 points	Additional charges apply
Cursor Pro	$20 (≈¥140)	Monthly	Basic quota	Using Claude/GPT-5.2
Cursor Business	$40/person/month (≈¥280)	Monthly	Team quota	Use Claude/GPT-5.2

Refresh cycle comparison (from fast to slow):

1. GLM-4.7/ChatGPT: Refresh every 5 hours (fastest)
2. Gemini Code Assist: Refresh every day
3. GitHub Copilot/Qoder/Cursor: Refreshed every month
4. Claude Code: refresh every 7 days (slowest)

How to continue using the quota after it is used up (general plan):

• Option 1: Wait for the next refresh cycle to automatically resume
• Option 2: Use API KEY to directly consume tokens (pay-as-you-go, no need to wait)
• Option 3: Switch/register other subscription accounts (subject to the terms of each service provider)
• Option 4: Upgrade to a higher version of the package to obtain a higher credit limit

Core Conclusion:

• GLM-4.7 is the best combination of refresh frequency and quota (5 hours refresh + high quota)
• ChatGPT Pro although it also refreshes in 5 hours, the price is 3.5 times that of GLM-4.7 Max
• Claude Code's 7-day refresh cycle is the most restrictive for heavy users
• GitHub Copilot Free suitable for light users, but has limited functionality
• Using API KEY is the most reliable solution to bypass subscription quota, suitable for heavy users

---

4. GLM-4.7 Detailed Cost Analysis

4.1 Price system

API pay-as-you-go

Scenario	Input (yuan/million tokens)	Output (yuan/million tokens)	Cache (yuan/million tokens)
Short output [0, 0.2)	¥0.6	¥2.2	¥0.12
Medium output [0.2+)	¥1.5	¥5.5	¥0.30
Long input [32, 200)	¥2.2	¥6.6	¥0.44

Coding Plan Subscription

Version	Monthly fee	Refresh cycle	Usage quota (per cycle)	Conversion price
Lite	¥40/month (Active price ¥54/season ≈ ¥18/month)	Every 5 hours	About 120 Prompts	About 1/3 of Claude Code Pro ($20 ≈ ¥140)
Pro	¥100/month (active price ¥270/season ≈ ¥90/month)	Every 5 hours	About 600 Prompts	About 1/2-2/3 of Claude Code Max
Max	¥400/month (activity price ¥540/season ≈ ¥180/month)	Every 5 hours	About 2400 Prompts	About 1/3-1/4 of Claude Code Max ($200 ≈ ¥1400)
Comparison	-	-	-	Claude Code: $20-200/month (approximately ¥140-1400/month)

Important Note:

• Refresh mechanism: The quota is refreshed every 5 hours as a cycle
• Automatically restore the quota after reset, no manual operation required
• After the quota is used up, you need to wait for the next cycle and cannot be accumulated.
• The system will not consume other resource packages or account balances
• The Lite quota is approximately 3 times the usage of the Claude Pro package
• The Max quota is approximately 3 times the usage of the Claude Max (5x) package

How to continue using the quota after it is used up:

• Option 1: Wait for the next refresh cycle (automatic recovery after 5 hours, refresh up to 4-5 times a day)
• Option 2: Use API KEY to consume tokens directly (pay-as-you-go, ¥0.6-2.2/million tokens, no need to wait)
• Option 3: Switch/register other subscription accounts (subject to terms of service)
• Option 4: Upgrade to a higher version of the package to obtain a higher credit limit

Cost Advantage:

• GLM-4.7 monthly fee is only 3-29% of Claude Code
• GLM-4.7 Token price is about 12% of Claude Opus
• 5 hour refresh cycle is more flexible than Claude's 7 day refresh cycle

4.2 User-level cost estimation

The inevitable trend of AI usage: from mild to severe

An important observation: Developer usage of AI will grow exponentially over time.

AI usage growth curve
├── Phase 1: Light use (1-2 months)
│ ├── Simple code completion
│ ├── Occasionally ask questions
│ ├── 10-50 times/day
│ └── Monthly cost: ¥40-100
├── Stage 2: Moderate use (3-6 months)
│ ├── Daily development relies on AI
│ ├── Hand over complex tasks to AI
│ ├── 50-200 times/day
│ └── Monthly cost: ¥100-400
└── Stage 3: Heavy use (6 months+)
├── AI becomes the core productivity
├── All development tasks are done through AI
├── 200-1000+ times/day
└── Monthly cost: ¥400-2000+

Why does usage continue to grow?

1. Trust improvement: From "try it" to "can't live without it"

• At the beginning: Doubtful about AI capabilities, only dare to use simple tasks
• Gradually: Discovering that AI can indeed improve efficiency
• Finally: Use AI as a primary productivity tool

2. Capability Boundary Expansion: From "Complete Code" to "Independent Development"

• Just getting started: simple code completion, bug query
• Gradually: complex function development, architecture design
• Finally: Complete all requirements independently, Code Review, and Refactoring

3. Dependency enhancement: from "auxiliary tools" to "core dependencies"

• Just getting started: Use occasionally, increase efficiency by 10-20%
• Gradually: Use daily to increase efficiency by 50-100%
• Finally: Unable to imagine development without AI, 200-300% more efficient

Real data: The inevitability of usage growth

Time period	Average daily usage	Monthly usage	Monthly Token estimate (GLM-4.7)	Monthly cost (GLM-4.7)
Month 1	20 times	600 times	300,000 tokens	¥40-100
Month 3	80 times	2400 times	1.2 million tokens	¥100-300
Month 6	200 times	6000 times	3 million tokens	¥300-600
12th Month	500+ times	15,000+ times	7.5 million+ tokens	¥600-1500+

Key Insights:

• 10x increase in usage is the norm (from mild to severe)
• A 10-fold increase in costs is inevitable
• With an expensive model (like Claude), the monthly cost can jump from ¥140 to ¥1400+
• Monthly cost increases from ¥40 to ¥400 with GLM-4.7, much less stressful

Conclusion: The importance of choosing a cost-effective model

Since usage is bound to grow significantly, it is crucial to choose a cost-effective model:

• Claude Opus: Mild ¥140 → Severe ¥1400+ (stressful)
• GLM-4.7: Mild ¥40 → Severe ¥400 (controllable range)

Key Takeaway: Don’t choose an expensive model just because your current usage is low, because in 6 months your usage will increase 10x. **Choose a cost-effective model that makes you "affordable and comfortable to use" to support long-term AI-assisted development. **

---

According to GLM Coding Plan official pricing:

Current activity price (paid quarterly):

• Lite: ¥54/quarter (≈¥18/month)
• Pro: ¥270/quarter (≈¥90/month)
• Max: ¥540/quarter (≈¥180/month)

Daily price (paid monthly):

• Lite: ¥40/month
• Pro: ¥100/month
• Max: ¥400/month

User Level	Monthly Interactions	GLM-4.7 Cost (/person/month)	Qoder Cost (/person/month)	Savings
Mild	10-50 times/day	¥40 (120 times/5 hours)	$20 (about ¥140)	71%
Moderate	50-200 times/day	¥100 (600 times/5 hours)	$20-60 (approximately ¥140-420)	0-76%
Severe	200-1000 times/day	¥400 (2400 times/5 hours)	$60 (about ¥420)	5%
Top tier	1000+ times/day	¥400+ (multiple packages possible)	$60+ (approximately ¥420+)	0-5%

Conclusion: GLM-4.7 saves 71% in light user scenarios, with medium and heavy user costs close to or slightly lower than Qoder.

Advantages of refresh mechanism:

• GLM-4.7: Refresh every 5 hours, up to 4-5 times a day
• Lite version: You can get up to about 480-600 Prompts per day (120 × 4-5)
• Pro version: You can get up to about 2400-3000 Prompts per day (600 × 4-5)
• Max Edition: You can get up to about 9600-12000 Prompts per day (2400 × 4-5)

NOTE:

• GLM-4.7: Calculated based on daily price, Lite ¥40/month, Pro ¥100/month, Max ¥400/month
• Qoder: Pro $20/month (2000 points), Pro+ $60/month (6000 points), additional charges will apply if you exceed the number of points
• GLM-4.7 ranks 6th in the world, with stronger coding capabilities
• 5-hour refresh mechanism means that the quota is restored faster, suitable for high-frequency usage scenarios

4.3 Real heavy user cases

Case 1: User with monthly budget $1000 (approximately ¥7000)

Pure Claude Opus solution:

• Claude Code Max 200 subscription fee: $200/month (approximately ¥1400/month)
• Claude Opus API: $800/month (approximately ¥5600/month)
• Available tokens: approximately 20 million tokens/month
• Total: $1000/month (approximately ¥7000/month)

GLM-4.7 + Claude Code CLI solution:

• GLM-4.7 Professional Edition Subscription: ¥200/month (approximately $30/month)
• GLM-4.7 API: ¥4800 (approximately $685)/month
• Available tokens: approximately 1.4 billion tokens/month
• Total: ¥5000/month (approximately $715/month)
• The effect is the same, the cost is saved by 28.5%, and the available amount of tokens is increased by 70 times

Cost comparison:

• GLM-4.7 plan: ¥5000/month (subscription fee ¥200 + API ¥4800)
• Claude plan: ¥7000/month (subscription fee ¥1400 + API ¥5600)
• Savings: ¥2000/month (approximately 28.5%)

Case 2: Top user with monthly budget $3000 (approximately ¥21000)

Pure Claude Opus solution:

• Claude Code Max 200 subscription fee: $200/month (approximately ¥1400/month)
• Claude Opus API: $2800/month (approximately ¥19600/month)
• Available tokens: approximately 60 million tokens/month
• Total: $3000/month (approximately ¥21000/month)

GLM-4.7 + Claude Code CLI solution:

• GLM-4.7 Professional Edition Subscription: ¥400/month (approximately $60/month)
• GLM-4.7 API: ¥19600 (approximately $2800)/month
• Available tokens: approximately 4.2 billion tokens/month
• Total: ¥20000/month (approximately $2860/month)
• The effect is the same, the cost is saved by about 5%, and the usage of tokens is increased by 70 times

Cost comparison:

• GLM-4.7 plan: ¥20000/month (subscription fee ¥400 + API ¥19600)
• Claude plan: ¥21000/month (subscription fee ¥1400 + API ¥19600)
• Savings: ¥1000/month (approximately 5%)
• With the same budget, the available tokens of GLM-4.7 are 7 times that of Claude

---

5. Why choose CLI instead of editor plug-in?

Core point: CLI (Command Line Interface) is the future direction of AI-assisted programming, which is more advanced, powerful, and flexible than editor plug-ins.

5.1 CLI vs editor plug-in comparison

Why is the CLI more advanced?

CLI vs editor plugin comparison
├── CLI Advantages
│ ├── More powerful Agent architecture
│ │ ├── Subagent mechanism (Task tool)
│ │ ├── Parallel processing capability
│ │ └── Breaking down complex tasks
│ ├── A more complete tool chain
│ │ ├── Read/Write/Edit file operations
│ │ ├── Bash command execution
│ │ ├── Grep Search
│ │ ├── LSP integration
│ │ └── Git operations
│ ├── Project-level context
│ │ ├── CLAUDE.md configuration
│ │ ├── Global code understanding
│ │ └── Cross-file collaboration
│ ├── Greater flexibility
│ │ ├── Can be configured to use any model
│ │ ├── Custom tools and scripts
│ │ └── Does not depend on a specific IDE
│ ├── Complete plugin ecosystem ⭐
│ │ ├── PR Review Toolkit (code review)
│ │ ├── Development Workflows (professional workflow)
│ │ ├── Document Skills (Document Processing)
│ │ ├── Code Quality Tools (code quality)
│ │ ├── Enterprise Plugins (150+ commands)
│ │ └── For details, see: claude-plugins.dev
│ └── Better portability
│ ├── Cross-platform use
│ ├── Remote server development
│ └── CI/CD integration
└── Editor plug-in limitations
├── Limited by IDE interface
├── The tool chain is incomplete
├── Lack of complex Agent capabilities
├── No plug-in ecosystem
└── Difficult to use across tools

Core Points:

• CLI is an engineering tool, and the editor plug-in is an auxiliary tool
• CLI can handle complex, multi-step tasks
• CLI does not depend on a specific IDE and is more flexible
• CLI has a complete plug-in ecosystem and can interface with various engineering specifications ⭐
• CLI represents the future direction of AI-assisted programming

5.2 Claude Code CLI plugin ecosystem

Claude Code CLI has a powerful plug-in market that supports docking with various AI engineering specifications:

Plug-in type	Function description	Installation command
PR Review Toolkit	Automated code review (testing, error handling, type design, code quality, code simplification)	npx claude-plugins install @anthropics/claude-code-plugins/pr-review-toolkit
Python Development	Python 3.12+, Django, FastAPI, async patterns	npx claude-plugins install @wshobson/claude-code-workflows/python-development
JavaScript/TypeScript	ES6+, Node.js, React, modern web frameworks	npx claude-plugins install @wshobson/claude-code-workflows/javascript-typescript
Backend Development	API design, GraphQL architecture, TDD backend development	npx claude-plugins install @wshobson/claude-code-workflows/backend-development
Frontend Excellence	React 19, Next.js 15, component architecture, state management	npx claude-plugins install @dotclaude/dotclaude-plugins/frontend-excellence
Document Skills	Excel, Word, PowerPoint, PDF document processing	npx claude-plugins install @anthropics/anthropic-agent-skills/document-skills
Code Refactoring	Code cleanup, refactoring automation, technical debt management	npx claude-plugins install @wshobson/claude-code-workflows/code-refactoring
Claude Flow	150+ commands, 74+ professional agents, GitHub integration	npx claude-plugins install @ruvnet/claude-flow-marketplace/claude-flow
Developer Essentials	Git, SQL, Error Handling, Code Review, E2E Testing	npx claude-plugins install @wshobson/claude-code-workflows/developer-essentials

Key Benefits:

• 150+ professional commands: covering the entire development process
• 74+ Professional Agents: for different technology stacks and workflows
• One-click installation: npx claude-plugins install <plugin-name>
• Open Source Community: Continuous updates, community maintenance
• See details: Claude Plugins Marketplace

5.3 Claude Code: The maker of AI engineering specifications

Important point: Claude Code is not only a tool, but also one of the formers of AI engineering specifications.

Anthropic’s AI Spec Development Role

Anthropic (the developer of Claude) is a core developer of global AI safety and engineering specifications:

1. Responsible Scaling Policy (RSP)

• Anthropic sets standards for security and deployment of cutting-edge AI models
• Defines best practices for technical security and operational measures
• For details, see: Anthropic Responsible Scaling Policy

2. AI Safety Levels (ASL) Standard

• Refers to the U.S. Government Biosafety Level (BSL) framework
• Established a hierarchical AI safety standard system
• ASL-3 protection activated in May 2025
• For details, see: ASL-3 Protections

3. Claude Code official best practices

• Anthropic releases official AI coding best practices
• Defines standards and processes for enterprise-level AI coding
• Covers governance, security, CI/CD integration, code reviews, and more
• For details, see: Claude Code Best Practices

4. Compliance Framework

• Develop a compliance framework for AI regulations such as California SB-53
• Participate in the formulation of global AI safety standards
• For details, see: Compliance Framework SB-53

Why is this important?

Dimensions	Other AI Tools	Claude Code CLI
Specification source	Follow third-party specifications	Specification author himself
Engineering standards	Customized or incomplete	Meet enterprise-level standards
Security Best Practices	Community Practices	Official Security Standards
Enterprise adoption	Additional evaluation required	Direct adoption of industry standards
Long-term support	Depends on commercial company	Core product for AI specifiers

Core Conclusion:

• Claude Code is developed and maintained by AI Specifier (Anthropic)
• Adopt Claude Code = Adopt Industry Standard for AI Engineering
• It's not about choosing a tool, it's about choosing a proven engineering system
• For enterprises, this means lower risk, higher compliance, and more mature best practices

Enterprise Advantages:

• Anthropic cooperates with global governments and enterprises to develop AI standards
• These standards and best practices are built into Claude Code
• Use Claude Code = Automatically comply with industry-leading AI engineering specifications

---

6. Implementation Suggestions

6.1 Overview of recommended solutions

┌─────────────────────────────────────────────────────────┐
│ AI programming tool selection plan │
├─────────────────────────────────────────────────────────┤
│                                                         │
│ Core model: GLM-4.7 (the latest flagship of Zhipu, released in 2025.12) │
│ ├── Price: Claude’s 1/7 - 1/12 │
│ ├── Capability: Code capabilities surpass GPT-5 (claimed) │
│ ├── Ranked 6th in the world │
│ └── Principle: Use the new rather than the old │
│                                                         │
│ Engineering framework: Claude Code CLI + GLM-4.7 │
│ ├── Mature Agent Architecture │
│ ├── Low-cost subscription (GLM-4.7 monthly fee ¥40-400) │
│ ├── Configurable using GLM-4.7 │
│ ├── Project-level context management │
│ ├── CLI is more powerful and flexible than editor plug-ins │
│ ├── Plug-in ecosystem: 150+ commands, 74+ agents │
│ └── AI standard setter: Anthropic core product │
│                                                         │
│ Tool concept: CLI is the main force, IDE is the frontier exploration │
│ ├── CLI: Engineering tool, suitable for complex tasks │
│ ├── Plug-in system: docking with various engineering specifications │
│ ├── Industry Standard: Compliant with enterprise-level AI security specifications │
│ ├── IDE: Understand cutting-edge technology, not recommended as the main force │
│ └── Flexible selection, cost efficiency first │
│                                                         │
└─────────────────────────────────────────────────────────┘

6.2 Phased implementation plan

Phase 1: Pilot Verification (1-2 weeks)

Steps	Content	Objectives	Cost
1	Register for Zhipu AI and apply for GLM-4.7 API	Get access	¥0 (20 million tokens given away)
2	Install Claude Code CLI	Verify engineering capabilities	¥0 (free installation)
3	Subscribe to GLM-4.7 Basic Edition and configure	Verify model configuration	¥40 (test subscription fee)
4	Small-scale pilot (2-3 people)	Collect feedback	¥120-200

Phase Goal: Verify the feasibility of the GLM-4.7 + Claude Code CLI combination

Cost Advantage: The monthly fee is only ¥40, which is much lower than Claude Code Pro ($20 ≈ ¥140)

Phase 2: Team Promotion (1-2 months)

Steps	Content	Coverage	Cost
1	Internal sharing session (CLI usage tips)	All developers	¥0
2	Promote Claude Code CLI	10-20 people	¥0 (free)
3	GLM-4.7 Subscription Quota	On-demand allocation	¥400-800/month
4	Establish best practices for CLI usage	Team documentation	¥0

Phase goal: 50%+ developers adopt CLI solution

Cost Advantage: GLM-4.7 subscription fee is only 3-29% of Claude Code Teams

The third stage: full application (ongoing)

Steps	Content	Objectives
1	Evaluating private deployment	20+ person team consideration
2	Establishing AI engineering best practices	Documentation within the team
3	Continuous cost optimization	Multi-tool hybrid strategy

6.3 Recommended configurations for different scenarios

Individual Developer

Budget	Configuration Plan	Monthly Cost
Within ¥200 (about $30)	GLM-4.7 Basic Edition + API on demand	¥60-150 (about $10-22)
¥500-1000 (approximately $70-140)	GLM-4.7 basic version + a large number of APIs	¥200-500 (approximately $30-72)
¥2000+ (about $280+)	GLM-4.7 Professional Edition + Massive API	¥500-1500 (about $72-215)

Note: The above price includes GLM-4.7 subscription fee (¥40-400) + API fee

Small team (2-5 people)

Budget	Configuration plan	Monthly cost	Per capita
¥2000 (about $280)	GLM-4.7 Basic Edition Team + API	¥2000 (about $280)	¥400-1000 (about $55-140)
¥5000 (approximately $700)	GLM-4.7 Professional Edition Team + Lots of APIs	¥5000 (approximately $700)	¥1000-2500 (approximately $140-350)

Note: GLM-4.7 monthly fee is much lower than Claude Teams ($40/person/month ≈ ¥280/person/month)

Medium team (5-20 people)

Budget	Configuration plan	Monthly cost	Per capita
¥10000-20000 (approximately $1400-2800)	GLM-4.7 Professional Edition team + a large number of APIs	¥10000-20000 (approximately $1400-2800)	¥500-4000 (approximately $70-560)

Note: Compared with Claude Code Teams ($40/person/month ≈ ¥280/person/month), Save ¥5600-11200/month ($800-1600/month) subscription fee

Large teams (20+ people)

Budget	Configuration Plan	Monthly Cost	Description
¥50000+/month (about $7000+/month)	GLM-4.7 private deployment	¥50000+ (about $7000+)	Consider privatization

Note: If you use GLM-4.7, you will save about $800/month (¥5600/month) compared to Claude Code Teams

---

7. Cost-benefit analysis

7.1 Return on Investment (ROI)

Assume that the team has 10 developers and the average annual salary is ¥300,000:

Solution	Monthly Cost	Annual Cost	Efficiency Improvement	Annual Value	ROI
GLM-4.7 + Claude Code	¥10400-14400	¥124800-172800	30%	¥9000000	5200-7200%
Claude Code native	¥14280-15400	¥171360-184800	30%	¥9000000	4870-5250%
No AI	¥0	¥0	0%	¥0	0%

Conclusion: The ROI of the GLM-4.7 solution is comparable to Claude Code, but the cost is 27-43% lower.

7.2 True cost comparison

Team of 10 people, monthly budget ¥10,000 (approximately $1,400)

Claude Code native solution:

• Claude Code Teams: $40 × 10 = $400 ≈ ¥2800 (required subscription fee)
• Claude Opus API: $857 ≈ ¥6000
• Available tokens: approximately 3.5 million/month
• Total: $1257/month (approximately ¥8800/month)

GLM-4.7 + Claude Code CLI solution:

• Claude Code CLI: ¥0 (free)
• GLM-4.7 Professional Edition Subscription: ¥400
• GLM-4.7 API: ¥9600
• Available tokens: approximately 4.8 billion/month
• Total: ¥10,000/month (approximately $1,400/month)

Key Insight: With the same budget of ¥10,000 (approximately $1,400):

• GLM-4.7 has 1370+ times more available tokens than Claude Opus
• GLM-4.7 monthly fee is only 14% of Claude Teams
• Total cost savings of approximately ¥1240/month (approx. $177/month)

Team comparison of 20 people

Plan	Subscription fee (monthly)	API cost (monthly)	Total cost (yearly)
Claude Teams	$800 (¥5600)	¥10000	¥187,200
GLM-4.7 Plan	¥400-800	¥14400-19600	¥178,800-244,800

Under the same annual budget:

• Claude Teams: $800 × 12 = $9600/year subscription fee (approximately ¥67,200)
• GLM-4.7 plan: ¥4800-9600/year subscription fee, only 7-14% of Claude's
• Spend more budget on APIs and get 14-140x more tokens

---

8. Risks and Challenges

8.1 Potential risks

Risk	Impact	Mitigation
GLM-4.7 stability	The new model may have bugs	Pilot verification, gradual promotion
Learning Curve	Teams need to adapt to new tools	Internal sharing, document precipitation
Vendor Lock-in	Over-reliance on a single vendor	Maintaining multi-tool capabilities
Cost Control	API usage may exceed expectations	Set budget alarms and review them regularly

8.2 Coping strategies

1. Dual Model Strategy: Keep Claude as an alternative
2. Phased promotion: Start with a small-scale pilot
3. Cost Monitoring: Review API usage monthly
4. Continuous Evaluation: Pay attention to new model releases

---

9. Summary and Suggestions

9.1 Core recommendations

It is recommended to use the GLM-4.7 + Claude Code CLI combination solution

This is the optimal choice based on a comprehensive evaluation of cost-effectiveness, engineering capabilities, plug-in ecosystem, and industry standards.

---

9.2 Key arguments

1. Use the new instead of the old: GLM-4.7 is the latest flagship in December 2025, with leading technology
2. Obvious cost advantage: The monthly fee is only 3-29% of Claude’s, and the Token cost is 12%
3. Mature engineering capabilities: Claude Code CLI provides a complete framework
4. Strong configurability: Claude Code CLI can directly use GLM-4.7
5. CLI is more advanced: CLI is more powerful, more flexible, and more engineering than editor plug-ins
6. Complete plug-in ecosystem: 150+ professional commands, 74+ professional agents, covering various engineering specifications
7. AI specification setter: Anthropic is the core setter of global AI safety and engineering specifications
8. Not dependent on IDE: CLI can be used in any environment, including remote servers

---

9.3 Expected return

Benefit type	Expected value	Description
Monthly Savings	71-97%	GLM-4.7 subscription fee is only ¥40-400
Total Cost Savings	27-43%	Including subscription fee and token cost
Token promotion	14-1400x	Number of available tokens under the same budget
Capability improvement	20-50%	Development efficiency improvement
ROI	5200-7200%	Return on Investment

Core Advantage Summary:

• GLM-4.7: Ranked 6th in the world, monthly fee ¥40-400, Token price is 12% of Claude's
• Claude Code CLI: free tool, more powerful and flexible than editor plugins
• Plug-in Ecosystem: 150+ commands, 74+ agents, covering various engineering specifications
• AI Specification Setter: Anthropic is the core setter of global AI safety and engineering specifications
• Combined effect: Industry standard + CLI engineering capability + plug-in ecology + cost-effective model

---

9.4 Enterprise-Level Advantages: Tools for Selecting AI Specifiers

Why should enterprises choose Claude Code CLI?

For enterprises, choosing AI tools is not only choosing a product, but also choosing a set of engineering systems and standards.

Compare dimensions	Other AI tools (Qoder/Cursor, etc.)	Claude Code CLI + GLM-4.7
Specification source	Follow third-party specifications or custom specifications	Specification author himself (Anthropic)
Security Standard	Community practice or commercial company standard	Official AI Security Standard (ASL-3)
Engineering specifications	Incomplete or customized	Enterprise-level best practices
Compliance	Additional evaluation and adaptation required	Compliance with global AI regulatory framework
Long-term maintenance	Depends on commercial company survival	Core product for AI specifiers
Risk Control	High (Tool Risk + Compliance Risk)	Low (Industry Standard + Compliance Guarantee)

Actual value of enterprise-level scenarios

1. Highly regulated industries such as finance and medical care

• Need to meet strict security and compliance requirements
• Claude Code Responsible Scaling Policy based on Anthropic
• Automatically complies with the world's leading AI safety standards
• Reduce compliance risks and increase audit pass rate

2. Large Enterprise IT Departments

• Requires standardized engineering processes
• Claude Code provides official best practices
• Seamless integration with CI/CD, code review, and security auditing
• Unify standards and reduce management costs

3. Government Agencies and Public Sector

• Need for transparent, auditable AI use
• Anthropic works with governments to develop AI standards
• Comply with California SB-53 and other regulatory frameworks
• Meet policy requirements and improve public trust

4. International Enterprise

• Need to comply with AI regulations in different regions around the world
• Anthropic participates in the development of global AI safety standards
• One solution, applicable globally
• Simplify compliance processes and reduce legal risks

in conclusion

Core point: Choose Claude Code CLI = Choose Industry standard for AI engineering

• This is not a tool choice but a strategic choice
• Reduce corporate risk, improve compliance, and gain long-term protection
• For enterprise-level deployments, this is the optimal solution

---

9.5 Flexibility in tool selection

Important Principle: Allow developers to choose the AI tools that best suit them

Although we recommend GLM-4.7 + Claude Code CLI as the main solution, strongly encourage developers to contact and experience the world's most advanced AI tool combination:

Why should developers be allowed to choose freely?

1. Access to cutting-edge technology

• Cursor + Claude Opus + GPT-5.2/Codex is currently the most advanced combination in the world
• Understand the latest advances in AI-assisted programming by using state-of-the-art AI engineering capabilities
• Experience the latest AI features and interaction modes

2. Feedback to the team

• Bring cutting-edge tool experience and best practices back to the team
• Help the team determine which new features are worth adopting in the main solution
• Provide a multi-dimensional technology selection perspective

3. Personal Growth

• Maintain technical sensitivity and stay at the forefront of AI
• Develop judgment on AI tools
• Avoid "tool island" thinking

Recommended cutting-edge tool set

Tool Set	Features	Applicable Scenarios	Budget
Cursor + Claude Opus + GPT-5.2	The world's most powerful combination, Composer 4x speed	Pursuit of ultimate efficiency	$200/month (≈¥1400)
Cursor + GPT-5.2-Codex-Max	OpenAI latest code model	Experience the OpenAI ecosystem	$100-200/month
Claude Code + Claude Opus 4.5	Anthropic native combination	In-depth experience Claude	$20-200/month

Core Viewpoint:

• Main solution (GLM-4.7 + Claude Code CLI): pursuing cost-effectiveness and stability
• Frontier Exploration (Cursor + Claude + Codex): Pursuing technological leadership and experience accumulation
• The two are not in conflict, but complement each other

Implementation suggestions

1. Team standard solution: Use GLM-4.7 + Claude Code CLI as the main tool

• Prioritize using CLI for daily development
• Enjoy the engineering power and flexibility of the CLI
• Reduce team costs and improve efficiency

2. Personal Frontier Exploration: Encourage developers to experience Cursor, Qoder and other IDEs

• Learn about the latest advances in AI tools
• But not recommended as the main tool (high cost, dependent on IDE)
• Feed back the excellent features of IDE to CLI use

3. Knowledge Sharing: Regular internal sharing meetings

• Share CLI usage tips and best practices
• Exchange experiences on cutting-edge IDE tools
• Establish the team’s rules for using AI tools

4. Technology Radar: Establish an AI tool evaluation mechanism

• Stay tuned for new model releases
• Evaluate new features of CLI tools
• Maintain technical sensitivity

Ultimate Goal: Use CLI as the main tool and IDE as the cutting-edge exploration to ensure cost efficiency while maintaining technical sensitivity.

---

10. In-depth analysis of code generation capabilities

10.1 Assessment of current status of AI code generation

Overall ability assessment

Capability dimension	Front-end	Back-end	Rating
Code Generation	High UI restoration	High logic accuracy	⭐⭐⭐⭐
Architecture Design	Good componentization	Reasonable layering	⭐⭐⭐
Bug rate	Many style issues	Many edge cases	⭐⭐⭐
Debugging Difficulty	Visual problems are difficult to locate	Logical problems are easy to locate	⭐⭐⭐

Front-end code generation

Advantages:

• ✅ High degree of restoration of UI components (80-90%)
• ✅ Responsive layout is well understood
• ✅ Component-based thinking is mature
• ✅ Tool libraries such as TailwindCSS are used accurately

FAQ:

• ❌ The style details are not fine enough (spacing, color, rounded corners, etc.)
• ❌ Interaction logic occasionally has bugs (event processing, state management)
• ❌ Unsatisfactory implementation of complex animation effects
• ❌ Inadequate consideration of browser compatibility
• ❌ Weak awareness of performance optimization (repeated rendering, unnecessary calculations)

Debugging Difficulty: ⭐⭐⭐⭐ (Visual problems require pixel-by-pixel comparison)

Backend code generation

Advantages:

• ✅ CRUD generation is accurate (90-95%)
• ✅ API design specification (RESTful)
• ✅ Database operation is correct (SQL/ORM)
• ✅ Improved error handling mechanism
• ✅ Clear code structure

FAQ:

• ❌ Deviation in understanding complex business logic
• ❌ Concurrency/security issues (locks, transactions)
• ❌ Insufficient performance optimization (N+1 query, cache)
• ❌ Incomplete handling of edge cases
• ❌ Security vulnerabilities (SQL injection, XSS)
• ❌ Insufficient logging and monitoring

Debugging Difficulty: ⭐⭐⭐ (Logical problems can be quickly located through logs)

10.2 Analysis of cross-domain development possibilities

Front-end developers write back-end

Capability Requirements	Current Situation	Feasibility
API Design	Understanding Concepts	⭐⭐⭐⭐
Database Operation	Need to learn SQL	⭐⭐⭐
Business Logic	Change your mindset	⭐⭐⭐
Deployment and Operation	Completely new field	⭐⭐

Feasibility Assessment:

• Simple CRUD: 85% feasible (with AI assistance)
• Medium complexity: 60% feasible
• High complexity: 30% feasible

Learning Curve:

• Basic backend concepts (API, database): 1-2 weeks
• Practical project (simple CRUD): 1 month
• Production grade applications: 3-6 months

Back-end developers write front-end

Capability Requirements	Current Situation	Feasibility
HTML/CSS	Basic understanding	⭐⭐⭐⭐
JavaScript/TS	Need to go deeper	⭐⭐⭐
Framework (React/Vue)	System learning required	⭐⭐⭐
UI/UX Design	A completely new field	⭐⭐

Feasibility Assessment:

• Simple page: 80% feasible (with AI assistance)
• Medium complexity: 50% feasible
• High complexity (animation, interaction): 25% feasible

Learning Curve:

• Basic HTML/CSS/JS: 2-3 weeks
• Single page application framework: 1-2 months
• Production-level applications (status management, performance optimization): 3-6 months

10.3 Cost estimate to complete requirements

Individual developers complete a requirement

Requirement type	Traditional development	AI-assisted development	Efficiency improvement
Simple Page	4-8 hours	1-2 hours	4-6x
CRUD Features	1-2 days	2-4 hours	3-4x
Medium Complexity	3-5 days	1-2 days	2-3x
High Complexity	1-2 weeks	3-7 days	1.5-2x

Team development cost comparison

Assume a team of 10 people with a monthly salary of ¥30,000:

Development model	Monthly labor cost	AI tool cost	Total cost	Output comparison
Traditional Development	¥300,000	¥0	¥300,000	1x
AI Assisted	¥300,000	¥10,000	¥310,000	2-3x

Conclusion: With AI assistance, 10% cost increase brings 200-300% output improvement.

10.4 Frequently Asked Questions about AI Generated Code

Front-end problem distribution

Problem type	Proportion	Debugging difficulty	Preventive measures
Style Details	40%	⭐⭐⭐	Accurately describe the design draft
Interaction Logic	25%	⭐⭐⭐⭐	Clear status flow
Performance Issues	20%	⭐⭐⭐⭐⭐	Code Review + Performance Testing
Compatibility	10%	⭐⭐⭐⭐	Designated browser support
Other	5%	⭐⭐	-

Backend problem distribution

Problem type	Proportion	Debugging difficulty	Preventive measures
Business logic deviation	35%	⭐⭐⭐	Detailed requirements document
Border Case	25%	⭐⭐⭐⭐	Full Test Case
Performance Issues	20%	⭐⭐⭐⭐⭐	Performance Testing + Optimization
SECURITY ISSUES	15%	⭐⭐⭐⭐⭐	SECURITY REVIEW
Other	5%	⭐⭐	-

10.5 Abilities required to debug AI-generated code

Front-end debugging capability map

Front-end debugging capabilities
├── Basic abilities
│ ├── Browser DevTools use
│ ├── Console log debugging
│ ├── Breakpoint debugging
│ └── Network request analysis
├── Style debugging
│ ├── CSS selector priority
│ ├── Flexbox/Grid layout
│ ├── Responsive breakpoints
│ └── Browser Compatibility
├── Interactive debugging
│ ├── Event handling mechanism
│ ├── State management (Redux/Vuex/Pinia)
│ ├── Asynchronous operation (Promise/async-await)
│ └── Life cycle hook
└──Performance debugging
    ├── React DevTools / Vue DevTools
├── Performance analysis (Performance)
├── Memory leak detection
└── Rendering optimization

Back-end debugging capability map

Backend debugging capabilities
├── Basic abilities
│ ├── Log system usage
│ ├── Breakpoint debugging
│ ├── Unit testing
│ └── Integration testing
├── Logic debugging
│ ├── Business process tracking
│ ├── Data flow analysis
│ ├── Error stack analysis
│ └── Boundary case testing
├──Performance debugging
│ ├── Slow query analysis
│ ├── Interface performance test
│ ├── Memory/CPU Analysis
│ └── Cache hit rate
└── Safe debugging
├── SQL injection detection
├── XSS/CSRF protection
├── Permission verification
└── Data encryption

10.6 The missing ability of mutual debugging between front and back ends

Missing capabilities in front-end development and debugging back-end

Missing ability	Importance	Learning cycle	Scope of influence
API Design Specification	⭐⭐⭐⭐⭐	1 week	Interface docking
Database Basics	⭐⭐⭐⭐⭐	2 weeks	Data Understanding
Server Deployment	⭐⭐⭐	1 month	Environment setup
Log Analysis	⭐⭐⭐⭐	2 weeks	Problem location
Performance Optimization	⭐⭐⭐	1 month	System Optimization
Security Awareness	⭐⭐⭐⭐	Continuous	System Security

Learning Priority:

1. API design + database basics (required, 2-3 weeks)
2. Log analysis (Important, 2 weeks)
3. Server deployment (recommended, 1 month)
4. Performance optimization + security (ongoing)

Missing capabilities in back-end development and debugging front-end

Missing ability	Importance	Learning cycle	Scope of influence
CSS/Flexbox	⭐⭐⭐⭐⭐	2 weeks	Page layout
JavaScript In-Depth	⭐⭐⭐⭐⭐	1 month	Interaction Logic
Framework (React/Vue)	⭐⭐⭐⭐⭐	1-2 months	Component development
Browser DevTools	⭐⭐⭐⭐	1 week	Problem location
UI/UX Basics	⭐⭐⭐	Continuous	User Experience
Front-end performance optimization	⭐⭐⭐	1 month	Experience optimization

Learning Priority:

1. CSS/Flexbox + Browser DevTools (required, 2-3 weeks)
2. JavaScript in-depth + framework (required, 2-3 months)
3. UI/UX + performance optimization (recommendation, ongoing)

10.7 AI-assisted learning path

Path 1: Front-end Developer → Full Stack (AI Assisted)

Learning Period: 3-6 months

Stage	Time	Content	AI auxiliary effects
Phase 1	2-4 weeks	Backend basics (Node.js/Express, API design, database)	⭐⭐⭐⭐⭐
Phase 2	4-8 weeks	Practical projects (CRUD, authentication, file upload)	⭐⭐⭐⭐
Phase 3	4-12 weeks	Production-level applications (deployment, monitoring, performance optimization)	⭐⭐⭐

Expected results:

• After 3 months: Able to independently complete 80% of the full stack requirements
• After 6 months: Able to independently complete 95% of the full stack requirements

Path 2: Backend Developer → Full Stack (AI Assisted)

Learning Period: 4-8 months

Stage	Time	Content	AI auxiliary effects
Phase 1	3-4 weeks	Front-end basics (HTML/CSS, JavaScript)	⭐⭐⭐⭐
Phase 2	8-12 weeks	Framework in-depth (React/Vue, state management)	⭐⭐⭐
Phase 3	8-16 weeks	Production-grade applications (performance optimization, deployment)	⭐⭐⭐

Expected results:

• After 4 months: Able to independently complete 70% of front-end requirements
• After 8 months: Able to independently complete 90% of front-end requirements

Path three: Zero foundation → AI-assisted development (GLM-4.7)

Learning Period: 6-12 months

Stage	Time	Content	AI auxiliary effects
Phase 1	4-8 weeks	Programming basics (syntax, data structures, algorithms)	⭐⭐⭐
Phase 2	8-12 weeks	Front-end or back-end specialization	⭐⭐⭐⭐
Phase 3	8-16 weeks	Framework + Engineering	⭐⭐⭐⭐
Phase 4	8-24 weeks	Practical projects + debugging capabilities	⭐⭐⭐

Expected results:

• After 6 months: Able to complete simple needs independently
• After 12 months: Able to independently complete medium-complexity requirements

10.8 Master the key capabilities of AI-assisted development

Core Competencies List

Ability	Importance	Learning cycle	AI auxiliary effect
Prompt Engineering	⭐⭐⭐⭐⭐	1-2 weeks	-
Requirements Understanding and Dismantling	⭐⭐⭐⭐⭐	Continuous	⭐⭐⭐
Code Reading Skills	⭐⭐⭐⭐⭐	2-3 months	⭐⭐
Debug Capability	⭐⭐⭐⭐⭐	3-6 months	⭐⭐⭐
Architecture Design	⭐⭐⭐⭐	6-12 months	⭐⭐⭐⭐
TESTING ABILITY	⭐⭐⭐⭐	1-2 months	⭐⭐⭐⭐

Study suggestions

1. Master Prompt Engineering first (1-2 weeks) -Learn how to clearly describe requirements

• Learn how to break down tasks step by step
• Learn how to provide context

2. Improve code reading skills (2-3 months)

• Read open source project code
• Understand common design patterns
• Familiar with framework best practices

3. Focus on cultivating debugging capabilities (3-6 months)

• Front-end: Proficient in browser DevTools
• Backend: Master the logging system and testing
• General: Problem locating ideas

4. Architecture design ability (6-12 months)

• Learning system design
• Understand design patterns
• Focus on performance and security

10.9 Successfully Master Estimates for AI-Assisted Development

Different basic learning cycles

Current Basics	Achieving Goals	Learning Cycle	Weekly Investment	Probability of Success
Programming basics (1-2 years)	AI-assisted completion of medium requirements	1-2 months	10-15 hours	95%
Programming basics (3-5 years)	AI assists in completing complex requirements	2-4 weeks	10-15 hours	98%
Zero Basics	AI assists in completing simple requirements	4-6 months	15-20 hours	70%
Zero Basics	AI-assisted completion of medium requirements	8-12 months	20-25 hours	60%

Knowledge estimation

Front-end direction:

• HTML/CSS: ~50 core concepts
• JavaScript: ~100 core concepts
• Framework (React/Vue): about 80 core concepts
• Engineering: about 40 core concepts
• Total: ~270 core concepts

Backend Orientation:

• Language basics: about 80 core concepts
• Framework: ~60 core concepts
• Database: ~50 core concepts
• Deployment and operation: about 40 core concepts
• Total: ~230 core concepts

Learning Speed:

• Basic programming knowledge: master 15-20 concepts per week
• Zero Basics: Master 8-12 concepts per week

10.10 Key Conclusions

1. Front-end generated code problems are more at the visual level, making debugging more difficult.
2. Back-end generated code problems are more at the logic and security level, and the impact is more serious
3. The feasibility of writing front-end to back-end is higher than writing back-end to front-end (85% vs 80% simple scenario)
4. With the assistance of AI, those with basic programming skills can master AI-assisted development in 1-2 months
5. It takes 6-12 months to become proficient in using AI-assisted development with zero foundation
6. Debugging ability is the key to distinguishing whether the requirements can be completed independently

---

11. Reference sources

Official website

• Claude Code Plugins - Plugin Marketplace
• Anthropic Responsible Scaling Policy
• Claude Code Best Practices
• ASL-3 Protections
• Compliance Framework SB-53
• Qoder official website
• Qoder Pricing
• Qoder Documentation
• Qoder CLI Quick Start
• Zhipu AI Open Platform
• GLM-4.7 Documentation
• GLM Coding Plan Pricing
• Zhipu AI Pricing Page
• Cursor official website
• Claude Code

Model release information

• GLM-4.7 released - Sina Finance
• GLM-4.7 Open Source - Open Source China
• GLM-4.6 Document - Zhipu AI
• Claude Opus 4.5 released - Anthropic
• GPT-5.2 Release - OpenAI
• GPT-5.1-Codex-Max - OpenAI

Price and cost analysis

• Qoder Price Reduction Information - CSDN
• Qoder first month $2 - Alibaba Cloud Developer Community
• Claude Code Paid Complete Guide
• Cursor vs Codex vs Claude Code
• 2025 AI tool monthly cost analysis - Zhihu

Real cases of heavy users

• Employees spend $1,000 a day using Claude Code - Tencent News
• Monthly burning 350,000 yuan token, official overnight speed limit - InfoQ
• User collective escape! Cursor suicide strategy - InfoQ
• 70+ million AI bill per year - NetEase
• Top user AI bill $100,000/year - Zhihu
• Cursor has adjusted its price again, and the monthly subscription model cannot continue
• 12 hours of intensive Claude Code experience - OneV's Blog

Product comparison and evaluation

• In-depth comparison of AI programming tools in 2025
• In-depth evaluation of AI auxiliary programming tools - 51CTO
• Comparison of mainstream AI development tools in 2025 - Lewin's Blog
• Claude Code vs Cursor price/performance comparison

Qoder CLI related

• Qoder CLI Getting Started Guide - Zhihu
• Alibaba releases Qoder CLI - InfoQ
• Summary of experience using Qoder for 2 months
• Qoder CLI Community Edition Deployment Document - Alibaba Cloud
• Qoder full-stack development practical guide - Alibaba Cloud Developer Community

Qoder official documentation

• Qoder official documentation
• Qoder Model Ranking Selector
• Qoder CLI User Guide
• Qoder official website
• Qoder Repo Wiki Documentation
• Qoder Pricing

Product comparison and evaluation

• Alibaba Qoder vs Trae vs Cursor: Who will be the efficiency king of programmers in 2025? - Zhihu
• Qoder: AI IDE launched by Alibaba, a comprehensive understanding of its capabilities and future - Jimmy Song
• Introducing Cursor 2.0 and Composer - Cursor Blog
• Composer: Building a fast frontier model with RL - Cursor Blog
• Alibaba Qoder experience exceeds expectations, Repo Wiki function receives new upgrade - InfoQ
• [From "Code Completion" to "Knowledge Alignment": Qoder Repo Wiki receives a major upgrade - Alibaba Cloud Developer Community] (https://developer.aliyun.com/article/1682576)

other

• Unlimited supply of Claude, tens of billions of subsidies for AI IDE - PingWest
• [Belief that the cost of large models will decrease is the biggest illusion in the industry - Zhihu] (https://zhuanlan.zhihu.com/p/1941285603967742207)
• Token costs drop, subscription fees soar - Sina Finance

---

Document updated: December 2025

Notice:

1. Price information may change at any time, please refer to the official announcement.
2. The pricing strategy of AI tools is changing rapidly. It is recommended to check the latest official pricing regularly.
3. Heavy user costs are based on real cases, and actual situations may vary depending on usage patterns.