General-purpose large language models (LLMs) are routinely used as baselines when evaluating specialized pathology models on whole-slide images (WSIs). Because WSIs exceed contemporary model context limits, LLM baselines routinely use small, high-magnification patches processed independently via majority voting, without systematic evaluation of seemingly inconsequential design choices such as patch size, patch count, and magnification. Generalist LLMs have consistently underperformed specialized systems, reinforcing the perception that domain-specific training or architectural adaptation is necessary for pathology tasks involving WSIs. Here, we conduct a systematic factorial analysis of four input design factors: inference mode, patch size, magnification, and patch count. We demonstrate that prior studies have overstated the gap between specialized models and general-purpose LLMs by choosing non-optimized input configurations. On the MultiPathQA benchmark, switching to a single balanced configuration (large patches at lower magnification, processed jointly) raises GPT-5 from 15.1% to 39.5% on cancer-type classification (TCGA) and from 38.1% to 62.9% on organ classification (GTEx). Per-task optimization yields further gains up to 43.9% (TCGA) and 71.6% (GTEx). The same configuration generalizes to two other models and to a fully held-out CPTAC cohort, where it improves Gemini 3 Flash by 23.4 percentage points without any task-specific tuning.

翻译：通用型大语言模型（LLM）在比较全切片图像（WSI）上的专用病理模型时，常被用作基线。由于WSI超出当代模型的上下文限制，LLM基线通常使用通过多数投票独立处理的小尺寸、高放大倍率病理切片，而未对这些看似无关的设计选择（如切片尺寸、切片数量和放大倍率）进行系统评估。通用型LLM始终表现不如专用系统，这强化了针对涉及WSI的病理任务需要进行领域特定训练或架构调整的观点。在此，我们对四种输入设计因素（推理模式、切片尺寸、放大倍率和切片数量）进行了系统析因分析。我们证明，先前研究通过选择非优化的输入配置，夸大了专用模型与通用型LLM之间的差距。在MultiPathQA基准上，切换至单一平衡配置（低放大倍率下的大尺寸切片，联合处理）将GPT-5在癌症类型分类（TCGA）上从15.1%提升至39.5%，在器官分类（GTEx）上从38.1%提升至62.9%。逐任务优化进一步带来增益，分别达到43.9%（TCGA）和71.6%（GTEx）。相同配置可泛化至其他两种模型以及完全保留的CPTAC队列，在其中无需任何任务特定调优即可将Gemini 3 Flash提升23.4个百分点。