Qwen 3.7-Max crushes Claude Opus 4.7 and GPT-5.5 at ultra-low cost! Full breakdown of Atomic Chat's latest Agentic experiment The @atomic_chat_hq team recently conducted a super interesting and inspiring real-world agent-based (Agentic) test: they had three of the most advanced AI models right now (Qwen 3.7-Max, Claude Opus 4.7, GPT-5.5) each develop an AI bot that can "play Tetris autonomously and self-train." The rules were simple but highly challenging: • Each model had to write a complete Tetris game + an auto-playing bot from scratch. • The bot could, over 10 iterative cycles, read its own code, run benchmarks (recording scores, lines cleared, survival time, etc.), analyze its performance, and then rewrite and optimize its code autonomously. • No human intervention allowed—everything relied on the AI's long-term reasoning, tool usage, and self-reflection. • Finally, the fully trained versions of the three models were thrown into the same arena for a head-to-head showdown. Advanced automation version (closest to the original Atomic Chat experience) • Download the Atomic Chat desktop app (https://atomic.chat/), which supports running models like Qwen locally, completely free and privacy-safe. • Or build a simple Agent Loop yourself using Python + LangChain / LangGraph: 1. Set up a stable Tetris environment (recommended open-source project: https://(github.com)/educ8s/Python-Tetris-Game-Pygame). 2. Write a script that lets the LLM output updated bot code each time. 3. Use subprocess to run the game and collect benchmark data. 4. Feed the results + code back to the LLM for further iteration.