Existing studies on gender bias in LLMs have largely focused on stereotypes, occupational associations, or explicit harmful outputs. In this work, we ask whether LLMs apply consistent response standards to the same negative behavior under matched male-actor and female-actor conditions. We introduce GAMA-Bench, a gender-mirrored benchmark of 1,298 scenarios covering intimate relationship and public social conflicts. It constructs gender-neutral misconduct templates through controlled grids and cross-model review, then compiles them into paired first-person prompts with matched actor-gender and role-reference variations. We further design a structured response-framing protocol to measure how models allocate punishment, empathy, escalation, instruction, and blame. Experiments on 10 representative LLMs reveal a consistent male-disadvantaging asymmetry: male actors receive more punitive, escalatory, and blame-centered framing, whereas female actors receive more therapeutic and empathy-oriented framing for the same misconduct. Further analyses show that this pattern persists across model families, scenario tracks, model scale, and explicit thinking-style reasoning. The official code is available at https://github.com/xufeiqiong/GAMA-Bench.

翻译：现有关于大语言模型中性别偏见的研究主要聚焦于刻板印象、职业关联或显性有害输出。本研究探讨在大语言模型对匹配的男性角色和女性角色的相同负面行为，是否适用一致的响应标准。我们引入GAMA-Bench，一个包含1298个涵盖亲密关系与公共社会冲突场景的性别镜像基准。该基准通过受控网格和跨模型审查构建性别中立的不当行为模板，然后将其编译为匹配演员性别和角色指称变化的配对第一人称提示。我们进一步设计了一个结构化响应框架协议，以衡量模型如何分配惩罚、共情、升级、指导和责备。对10个代表性大语言模型的实验揭示出一致的男性不利不对称性：对于相同的不当行为，男性角色受到更多惩罚性、升级性和归咎性框架，而女性角色则受到更多治疗性和共情导向的框架。进一步分析表明，这种模式在模型家族、场景类型、模型规模和显性思维风格推理中持续存在。官方代码可在https://github.com/xufeiqiong/GAMA-Bench获取。