做智能体开发或自动化任务的团队，可以拿 Qwen-3.7-max 替代高价闭源模型，成本直降 9 倍效果反而更好，值得立刻跑个 benchmark 验证。
Qwen-3.7-max 在真实智能体任务（编写自训练俄罗斯方块机器人）中，以 1.32 美元成本实现 56% 的改进，远超 Claude Opus 4.7（12.15 美元，28% 改进）和 GPT-5.5（2.85 美元，7% 改进）。该模型在长智能体循环中表现突出，成本仅为 Opus 4.7 的 1/9、GPT-5.5 的 1/2。这一结果出乎意料，展示了开源模型在复杂自主任务上的潜力。
Wait so Qwen-3.7-max can beat both GPT-5.5 and Opus 4.7?! While being waaay cheaper: - 9x cheaper ...
Wait so Qwen-3.7-max can beat both GPT-5.5 and Opus 4.7?! While being waaay cheaper: - 9x cheaper than Opus 4.7 - 2x cheaper than GPT-5.5 Very impressive and honestly not expected. atomic.chat @atomic_chat_hq Qwen 3.7-max beats Opus 4.7 and GPT-5.5 We tested three frontier models on a real agentic task: write a Tetris bot that plays the game and trains itself. Each model could read its own code, run benchmarks, and rewrite itself across 10 iterations. Then we compared the final bots head to head. Qwen 3.7-Max: training cost $1.32, bot improvement +56% Claude Opus 4.7: training cost $12.15, bot improvement +28% GPT-5.5: training cost $2.85, bot improvement +7% Qwen won on every dimension - biggest jump, 9× cheaper than Claude, 2× cheaper than GPT. Long agentic loops is where Qwen Max actually delivers. Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 1 ❤️ 14 👀 1886 📊 5 ⚡