Descriptions:
Fahd Mirza runs a no-retries, same-prompt coding benchmark across six of China’s most capable AI models: DeepSeek V4 Pro, Kimi K2.6 (Moonshot AI), Qwen 3.6 Max Preview (Alibaba), MiniMax M2.7, GLM 5.1 (Zhipu AI), and Myo V2.5 Pro (Xiaomi). All six are prompted simultaneously in thinking/expert mode to build a production-grade real-time collaborative code review tool — a Python Flask application requiring WebSocket support, a live code editor, inline commenting, and a database-backed UI — then deployed and executed on a local Ubuntu server.
The live deployment results are more revealing than benchmark leaderboard positions. Kimi K2.6 and Qwen 3.6 Max are the clear standouts, generating functional applications that launch and support basic collaboration flows. GLM 5.1 fails to follow the setup script instruction entirely. MiniMax M2.7 and DeepSeek V4 Pro both launch but fail on code editor interactivity. Myo V2.5 Pro partially works, enabling comment submission but not live code editing.
For developers choosing between Chinese frontier models for agentic coding tasks, this test offers a concrete first-pass signal that raw parameter counts and benchmark scores do not reliably predict deployment success. The video also provides brief model summaries — Kimi K2.6’s 1-trillion-parameter multimodal architecture, Qwen 3.6’s 1-million-token context window, and Myo V2.5 Pro’s reported 4.3-hour compiler build — giving useful context for each model’s design priorities.
📺 Source: Fahd Mirza · Published April 26, 2026
🏷️ Format: Benchmark Test
