Comparing GPT-5.4, Opus 4.6, GLM-5.1, Kimi K2.5, MiMo V2 Pro and MiniMax M2.7
6 hours ago
- #Model Comparison
- #AI Coding
- #LLM Benchmarking
- A benchmarking experiment compared six large language models (GPT-5.4, Opus 4.6, GLM-5.1, Kimi K2.5, MiMo V2 Pro, MiniMax M2.7) for building a native macOS app to manage drive ejection issues.
- All models performed similarly in planning and compilation phases, with MiMo V2 Pro notably using kernel API instead of shell commands, though this was considered irrelevant.
- Post-compilation, some apps crashed initially, but all could be fixed with comparable effort, highlighting a lack of end-to-end testing tools for native apps as a limitation.
- In code quality, GPT-5.4 and GLM-5.1 were preferred for leaner, more maintainable output, though prompting could guide any model toward cleaner code.
- A peer review ranking placed GPT-5.4 and Opus 4.6 consistently in the top three, with GPT-5.4 leading; MiMo V2 Pro also ranked high but scored lower on cleanliness, while GLM-5.1 excelled in cleanliness but placed fourth overall.
- Kimi K2.5 and MiniMax M2.7 consistently ranked at the bottom, correlating with their lower cost, and models showed little self-bias in scoring, except GPT-5.4, which ranked itself first, aligning with consensus.
- The conclusion recommends any top model for practical use, noting GLM and MiMo offer competitive performance at lower costs, and emphasizes picking one model to avoid procrastination.