Testing 2 open-weight models across coding tasks: GLM 4.7 and MiniMax M2.1 (blog.kilo.ai)

Two new open-weight models, GLM 4.7 from z.AI and MiniMax M2.1, exhibited strong coding capabilities in a recent test involving a complex CLI tool development task. Both models successfully implemented all specified features of the tool, demonstrating their ability to manage dependencies, parallel execution, and provide comprehensive logging. GLM 4.7 stood out for its detailed planning and documentation, producing a thorough 741-line architecture document, while MiniMax M2.1 delivered the same functional output more cost-effectively, with a 284-line plan and operational costs significantly lower at $0.15 compared to its counterpart's pricing. The significance of this development lies in the rising proficiency of open-weight models, which are becoming increasingly viable alternatives to expensive frontier models like Claude and GPT. Both GLM 4.7 and MiniMax M2.1 showed impressive autonomy, running for extended periods without human intervention and even debugging issues encountered during implementation. This evolution in open-source AI not only enhances cost efficiency for developers but also democratizes access to advanced coding capabilities, suggesting that the gap between high-quality and affordable AI tools is narrowing rapidly.