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研究团队表示，三款模型基于相同的基础训练数据集，高一致率的结果符合预期。真正具备研究价值的是模型间25%的分歧部分，这种差异大概率并非源于模型对工具质量的独立判断，而是由基于人类反馈的强化学习（RLHF）调优策略不同，以及生成环节的专属微调差异导致。

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Claude，算是一个让人又爱又恨的模型了。 一方面是真的好用，另外一方面就是国内的各种不友好。 但是，它的新功能真是的层出不穷。 比如远程控制： 比如这个代码安全功能，对安全圈估又是一个大的打击，后面不知道有多少团队受此影响。

在这一高难度的“系统构建”场景下，模型表现呈现出了明显的两极分化。GPT-5.3-codex 凭借 86.4% 的通过率（19/22）稳居榜首，Claude Opus 4.6 以 68.2%（15/22）紧随其后。相比之下，其他参评模型（包括开源模型及部分闭源模型）在简单任务上的表现尚可，但一旦进入中高难度领域，成功率便跌至个位数甚至为零。