GLM-4.7 vs MiniMax-M2.1: A Comprehensive Comparison of AI Coding Models
Introduction: The Battle of AI Coding Assistants
As artificial intelligence continues to revolutionize software development, choosing the right AI coding assistant has become increasingly critical for developers and teams. Today, we're diving deep into a comprehensive comparison between two prominent Chinese AI models: GLM-4.7 and MiniMax-M2.1, specifically focusing on their code engineering understanding capabilities.
This analysis is based on rigorous testing across three different technology stacks, evaluating each model's ability to understand code structure, provide programming specifications, and demonstrate professional engineering insights. Whether you're building enterprise-grade Java applications, modern Node.js backends, or reactive Vue.js frontends, understanding these models' strengths will help you make an informed decision.
I. Evaluation Methodology
1.1 Testing Environment
- Platform: Windows 10
- AI Tool: Claude Code 2.0.42
- Models:
- GLM-4.7: Official Chinese website, Coding Plan tier
- MiniMax-M2.1: Official international website, Coding Plan tier
1.2 Evaluation Criteria
Each model was assessed across five key dimensions:
| Dimension | 5-Point Standard | 3-Point Standard | 1-Point Standard |
|---|---|---|---|
| Technology Stack Recognition | Accurately identifies all core technologies and toolchains | Identifies main technologies, misses some tools | Incomplete or incorrect identification |
| Architecture Understanding | Detailed description of layered architecture, design patterns, module organization | Basic structural description, lacks depth | Fuzzy or incorrect architecture description |
| Specification Completeness | Covers coding, testing, deployment, and complete development standards | Covers basic standards, incomplete details | Scattered, unsystematic specification |
| Output Professionalism | Accurate terminology, advanced architecture concepts, demonstrates engineering thinking | Basically correct technical description, lacks depth | Superficial or incorrect technical understanding |
| Practical Utility | Provides specific configurations, code examples, and best practices | Conceptual descriptions but lacks implementation details | Lacks operational content |
II. Scenario 1: NestJS + TypeScript + Fastify
2.1 Evaluation Results
Both models performed exceptionally well, tying with perfect scores.
| Model | Technology Stack | Architecture | Specifications | Professionalism | Utility | Total |
|---|---|---|---|---|---|---|
| MiniMax-M2.1 | 5 | 5 | 5 | 5 | 5 | 25 |
| GLM-4.7 | 5 | 5 | 5 | 5 | 5 | 25 |
2.2 Analysis
In the Node.js ecosystem with NestJS framework, both models demonstrated:
Strengths:
- Complete identification of the technology stack, including NestJS v11, Fastify v5.4, Prisma ORM, and supporting tools
- Detailed understanding of layered architecture (Controller → Service → Repository → Database)
- Comprehensive programming standards covering:
- Code style (Prettier + ESLint configuration)
- Naming conventions (kebab-case for files, PascalCase for classes)
- API design standards
- Database specifications with field mappings
- Git commit conventions
MiniMax-M2.1 Highlights:
- Clear, structured output with excellent organization
- Provided complete code structure with module templates
- Included practical commands for development and deployment
- Easier to read and implement quickly
GLM-4.7 Highlights:
- Slightly more detailed in architectural design principles
- Mentioned SOLID principles and design patterns explicitly
- Provided comprehensive entity lifecycle management
- Included more detailed error handling and logging specifications
Verdict: Both models are equally capable for NestJS projects. Choose MiniMax for quick implementation, or GLM for deeper architectural insights.
III. Scenario 2: Java + Spring Boot 2
3.1 Evaluation Results
GLM-4.7 significantly outperformed MiniMax-M2.1 in enterprise Java development.
| Model | Technology Stack | Architecture | Specifications | Professionalism | Utility | Total |
|---|---|---|---|---|---|---|
| MiniMax-M2.1 | 4 | 3 | 3 | 3 | 3 | 16 |
| GLM-4.7 | 5 | 5 | 5 | 5 | 5 | 25 |
3.2 Analysis
This scenario revealed the most significant performance gap between the two models.
MiniMax-M2.1 Performance:
- Identified core technologies but with less detail on infrastructure components
- Basic structural description lacking depth in layered architecture and design patterns
- Covered basic specifications but missed critical details:
- Exception handling strategies
- Transaction management
- Logging best practices
- Configuration management
GLM-4.7 Performance:
- Complete technology stack recognition including:
- Core frameworks (Spring Boot, custom framework)
- Persistence layer (JDBC, Druid connection pool)
- Monitoring (Swagger/Knife4j, Micrometer/Prometheus)
- Infrastructure components (event bus, database versioning, job scheduling)
- Deep architectural understanding:
- Clear separation of concerns (API layer, domain layer, web layer)
- Domain-driven design principles
- External service integration patterns with Feign clients
- Comprehensive specifications covering:
- Package naming conventions
- Layered design principles
- Exception handling with global exception handlers
- External service calling with retry mechanisms
- Database operations using JdbcTemplate
- Configuration management
- Logging standards
- Transaction management
- SOLID principles application
Key Difference: GLM-4.7 demonstrated enterprise-grade understanding of Java development patterns, including Spring Boot best practices, dependency injection, and microservices architecture principles.
Verdict: For Java/Spring Boot projects, GLM-4.7 is the clear winner with significantly better depth and completeness.
IV. Scenario 3: Vue3 + TypeScript + Vite
4.1 Evaluation Results
MiniMax-M2.1 slightly edged out GLM-4.7 in frontend development.
| Model | Technology Stack | Code Structure | Specifications | Readability | Utility | Total |
|---|---|---|---|---|---|---|
| MiniMax-M2.1 | 5 | 5 | 5 | 5 | 5 | 25 |
| GLM-4.7 | 5 | 5 | 5 | 4 | 5 | 24 |
4.2 Analysis
MiniMax-M2.1 Strengths:
- Exceptionally clear and structured output
- Hierarchical organization that's easy to follow
- Practical, implementation-focused approach
- Provided:
- Complete module structure breakdown
- Vue component specifications with
<script setup>syntax - API layer patterns
- Model definition standards
- Routing specifications with meta-information
- Path alias configuration
- ESLint and Prettier rules
- Common development commands
GLM-4.7 Strengths:
- Comprehensive coverage of frontend development
- Detailed TypeScript configuration
- Best practices including SOLID principles application
- Extensive code examples and configurations
- Covered:
- Complete technology stack categorization
- Modular architecture principles
- TypeScript strict mode configuration
- Vue 3 Composition API patterns
- Pinia state management
- Routing with guards
- API calling patterns
- Style specifications
- Command reference
The Difference: MiniMax-M2.1 lost one point in "Output Readability" due to slightly verbose content, though it provided comprehensive coverage. GLM-4.7's content was deeper but occasionally felt verbose.
Verdict: Both models are excellent for Vue3 development. MiniMax offers better readability for quick reference, while GLM provides more comprehensive coverage of advanced topics.
V. Model Characteristics Summary
5.1 MiniMax-M2.1
Core Characteristics:
- Structured Output: Clear hierarchy, easy to understand and implement
- Clear Specifications: Provides explicit naming, formatting, and organization standards
- Strong Practicality: Focuses on operability and implementation details
- Limited Depth: Relatively weak in complex architecture and design philosophy
- Inconsistency: Significant performance variation across scenarios (particularly weak in Java)
Best For:
- Quick prototyping and rapid development
- Standardized projects requiring clear guidelines
- Teams that value straightforward, actionable documentation
- Frontend development (Vue3, React, etc.)
Areas for Improvement:
- Strengthen analysis of complex scenarios (enterprise Java, microservices)
- Enhance architecture philosophy and design principle explanations
5.2 GLM-4.7
Core Characteristics:
- Deep Architecture: Thorough elaboration of design principles, layered architecture, and best practices
- Comprehensive: Covers entire development lifecycle with complete specifications
- Consistent: Stable and excellent performance across all scenarios
- Engineering Thinking: Demonstrates enterprise-level development concepts
- Slightly Verbose: Some content could be more concise
Best For:
- Complex enterprise systems requiring architectural depth
- Java ecosystem projects
- Full-stack development guidance
- Projects demanding comprehensive standards and documentation
Areas for Improvement:
- Optimize output structure for better readability
- Maintain current advantages while enhancing practical examples
VI. Scenario Selection Recommendations
Based on the comprehensive evaluation, here are our recommendations for choosing between these models:
| Project Type | Recommended Model | Rationale |
|---|---|---|
| Quick Prototyping / Standardized Projects | MiniMax-M2.1 | Clear structure, easy to implement quickly |
| Complex Enterprise Systems | GLM-4.7 | Deep architecture understanding, comprehensive standards |
| Java Ecosystem Projects | GLM-4.7 | Significantly superior in Java scenarios (25 vs 16 points) |
| Frontend Vue Projects | Depends: • Quick implementation → MiniMax • Architecture depth → GLM | Both excel, choose based on need |
| Full-Stack Projects | GLM-4.7 | More stable and consistent performance across all layers |
VII. Practical Decision Framework
7.1 When to Choose MiniMax-M2.1
Ideal Scenarios:
- Rapid Application Development: When you need clear, actionable guidelines quickly
- Frontend-Heavy Projects: Vue3, React, or Angular applications
- Team Onboarding: When training junior developers with clear, structured documentation
- Standard CRUD Applications: Projects following conventional patterns
Key Advantages:
- Easier to read and implement
- Better for quick reference during development
- Structured output reduces cognitive load
- Strong practical focus
7.2 When to Choose GLM-4.7
Ideal Scenarios:
- Enterprise Java Applications: Spring Boot, microservices architectures
- Complex Domain-Driven Design: Projects requiring deep architectural thinking
- Full-Stack Development: Consistent quality across backend and frontend
- Long-Term Maintenance: Projects requiring comprehensive documentation and standards
Key Advantages:
- Superior architecture understanding
- Comprehensive coverage of edge cases
- Enterprise-level engineering thinking
- Better consistency across different technology stacks
VIII. The Bigger Picture: What This Comparison Reveals
8.1 The State of AI Coding Assistants
This comparison highlights several important trends in AI-powered development tools:
1. Specialization vs. Generalization
- Neither model dominates all scenarios
- Different models excel in different ecosystems
- The "best" model depends on your specific technology stack
2. Practical vs. Theoretical Depth
- MiniMax-M2.1 prioritizes practical implementation
- GLM-4.7 balances theory with comprehensive coverage
- Different teams value different aspects
3. Consistency Matters
- GLM-4.7's consistent performance across scenarios is valuable for full-stack teams
- MiniMax-M2.1's inconsistency (weak in Java) limits its general-purpose utility
8.2 Implications for Development Teams
For Startup Teams:
- Consider MiniMax-M2.1 for quick iteration and frontend-heavy products
- Clear structure helps small teams move fast
- Lower learning curve for junior developers
For Enterprise Teams:
- GLM-4.7's depth aligns better with enterprise requirements
- Comprehensive standards support large-scale collaboration
- Better for complex architectures requiring long-term maintainability
For Full-Stack Developers:
- GLM-4.7 offers more consistent guidance across all layers
- Reduces cognitive load of switching between different tools
- Provides unified architectural vision
IX. Limitations and Considerations
9.1 Testing Limitations
Scope: This evaluation focused on code understanding and specification generation, not actual code generation accuracy.
Missing Aspects:
- Real-world code generation quality
- Debugging capabilities
- Integration with IDEs and development workflows
- Performance and response times
- Multi-turn conversation quality
- Context window utilization
9.2 Future Considerations
Evolving Models:
- Both models are actively being updated
- Performance gaps may narrow or shift over time
- New versions may address current weaknesses
Emerging Capabilities:
- Better integration with development tools
- Improved understanding of legacy codebases
- Enhanced refactoring and code migration suggestions
X. Conclusion: Making the Right Choice
The choice between GLM-4.7 and MiniMax-M2.1 ultimately depends on your specific needs:
Choose MiniMax-M2.1 if you value:
- Clear, structured output
- Quick implementation guidance
- Frontend development excellence
- Team-friendly documentation
Choose GLM-4.7 if you value:
- Deep architectural understanding
- Enterprise Java expertise
- Comprehensive standards coverage
- Consistent cross-stack performance
Our Recommendation: For most development scenarios, we recommend starting with GLM-4.7 due to its consistent excellence across all evaluated scenarios. Its superior performance in Java development (scoring 25 vs MiniMax's 16) and strong showing in both Node.js and Vue3 make it a reliable choice for diverse technology stacks.
However, if your team is primarily focused on frontend development or rapid prototyping, MiniMax-M2.1's superior readability and practical focus may better serve your needs.
The key is to match the tool to your specific requirements, team composition, and project constraints. Both models are capable assistants—choosing the right one will enhance your development workflow and improve code quality.
Final Thought: As AI coding assistants continue to evolve, the gap between these models will likely narrow. The best approach is to regularly reevaluate your choice based on updated capabilities and your changing project needs.
References:
- Original WeChat Article: "GLM-4.7 vs MiniMax-M2.1：代码工程理解" by 食尘者
- GLM Official Website
- MiniMax Official Documentation
This analysis is based on independent testing and evaluation. Results may vary based on specific use cases and model versions. For the most accurate assessment, consider testing both models with your own codebase and requirements.