Kimi and MiMo dominate Chinese LLM coding showdown

Benchmarking the Great Firewall of Code

Evaluating large language models (LLMs) requires moving beyond theoretical chat to rigid, automated testing. This specific trial pits six prominent Chinese models—Kimi K2.6, MiMo 2.5 Pro, DeepSeek V4 Pro, GLM-5.1, Minimax M2.7, and Qwen 3.6 Plus—against a practical Laravel Filament admin panel task. The goal: generate a functional interface using PHP enums and best practices without triggering test failures.

Precision Leaders: Kimi and MiMo

Kimi K2.6 emerged as the undisputed champion of accuracy, delivering zero test failures across three separate attempts. This level of consistency is rare in non-deterministic systems. Close behind, MiMo 2.5 Pro impressed with only a single failure related to a missing fillable property—a real error, but one separate from the complex Filament logic. Both models maintain a balance between cost and reliability that makes them viable alternatives to Western giants like GPT-4o.

The Speed Trap of Minimax

Minimax M2.7 holds the title for the fastest generation time, averaging around 42 seconds. However, speed is a hollow metric when accuracy cratered. It produced the highest volume of errors, proving that rapid output is worthless if the developer must spend the saved time debugging fundamental architectural flaws. In the context of developer productivity, Minimax M2.7 is a liability rather than an asset.

Consistency and Cost Dynamics

Models like Qwen 3.6 Plus and GLM-5.1 displayed frustrating inconsistency, passing all tests in only one out of three attempts. This volatility highlights why single-prompt evaluations are misleading. While these Chinese models often offer lower API costs via OpenCode, the "hidden cost" of human oversight remains high for any model that cannot guarantee a 100% pass rate on standardized unit tests.