GLM-4.7 vs MiniMax M2.1: Which AI Coding Model Should You Choose in 2025?

Introduction: The Open-Source AI Coding Revolution

December 2025 witnessed the release of two groundbreaking open-source AI models that are reshaping the coding landscape: GLM-4.7 from Zhipu AI (Z.AI) and MiniMax M2.1 from MiniMax. Both models claim state-of-the-art coding capabilities, but which one should developers actually use?

In this comprehensive comparison, we analyze benchmarks, speed, cost, and real-world performance to help you make an informed decision.

Model Specifications at a Glance

Benchmark Comparison: SWE-bench

SWE-bench Verified is the gold standard for measuring real-world software engineering capability. Here is how both models compare:

Verdict: MiniMax M2.1 has a slight edge at 74.0% vs 73.8%, and significantly outperforms on multilingual coding tasks (72.5% vs 66.7%).

LiveCodeBench Performance

LiveCodeBench V6 measures algorithmic reasoning and code generation:

• GLM-4.7: 84.9% (Open-source SOTA, beats Claude Sonnet 4.5)
• MiniMax M2.1: ~82% (estimated)

Verdict: GLM-4.7 excels at algorithmic challenges and competitive programming tasks.

Terminal Bench 2.0

This benchmark tests command-line and terminal-based interactions:

• MiniMax M2.1: 47.9%
• GLM-4.7: 41.0%

Verdict: MiniMax M2.1 handles terminal and CLI tasks more effectively.

Speed and Cost Comparison

Key Insight: MiniMax M2.1 is 25% cheaper and 4.5x faster than GLM-4.7. This makes it ideal for high-volume, latency-sensitive applications.

Architectural Philosophy

GLM-4.7: Deep Reasoning Focus

• Interleaved Thinking: Thinks before every response and tool call
• Preserved Thinking: Retains reasoning across multi-turn conversations
• Best for: Complex architectural decisions, long-horizon agent tasks, UI generation

MiniMax M2.1: Efficiency at Scale

• Lightning Attention: Enables efficient long-context processing
• Lightweight MoE: Only 10B parameters activated per inference
• Best for: Bulk code refactoring, multilingual projects, high-throughput workflows

Real-World Test: 3D Solar System Project

In a practical coding test building a 3D solar system visualization:

• MiniMax M2.1: Completed fastest with functional code, but lacked design depth
• GLM-4.7: Slower but delivered better visual and structural quality

This reflects their core philosophies: MiniMax prioritizes speed, GLM prioritizes reasoning quality.

Language Support Comparison

MiniMax M2.1 Excels At:

Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript

GLM-4.7 Excels At:

Python, complex multi-file refactoring, agent-based workflows

Use Case Recommendations

Choose GLM-4.7 When:

• Building complex AI agents (Claude Code, Cline, Roo Code)
• Working on architectural decisions and system design
• Competitive programming or algorithm-heavy tasks
• Projects requiring deep reasoning across turns

Choose MiniMax M2.1 When:

• Processing large codebases at scale
• Building multilingual applications
• Cost and latency are primary concerns
• Terminal-based automation and CLI tools
• High-volume code generation tasks

Industry Recognition

• GLM-4.7: #1 open model for WebDev on LM Arena, #6 overall (alongside OpenAI, Google, Anthropic)
• MiniMax M2.1: 94th percentile on coding baseline benchmarks

Conclusion: The Verdict

Both GLM-4.7 and MiniMax M2.1 represent remarkable achievements in open-source AI coding.

GLM-4.7 is the thinking developers choice - superior for complex reasoning, agent workflows, and quality-focused development. It is the #1 open-source model for web development.

MiniMax M2.1 is the efficiency champion - 25% cheaper, 4.5x faster, and better for multilingual coding at scale. Perfect for teams prioritizing throughput and cost.

Final Ratings

For most developers, MiniMax M2.1 offers better value. For complex agent development and deep reasoning tasks, GLM-4.7 remains unmatched in the open-source space.