评测大模型能否像人一样进行开放式审美批评，发现其表现与人类差异显著。

Open-ended aesthetic critique is a challenge for multimodal large language models (MLLMs): it has no single correct answer, and most aesthetic evaluation measures models against numeric scores rather than the written critiques people actually give. We ask whether MLLM critiques are close to human ones, scoring eight open-weight MLLMs from $7$B to $397$B, plus GPT-5.5, against multiple ranked human critiques for each of $1{,}227$ \texttt{r/photocritique} posts under eight prompt conditions. Reference-based similarity gives a misleading picture. In absolute terms the stricter lexical and learned metrics align only weakly with human critiques while a coarse embedding cosine reports broad topical overlap, yet requesting shorter critiques raises those scores and withholding the image barely changes them: the similarity reflects length, the post text, and a stable critiquing style more than image-specific observation. An LLM judge sharpens the question rather than settling it: in the primary condition all four judges prefer the frontier models' critiques to the human ones, but on the $7$--$8$B models they diverge wildly, from $9\%$ to $81\%$ preference on identical pairs. Asked instead how similar each pair is in substance, those judges and two human annotators agree, rating every model between $1.81$ and $2.59$ on a $1$--$5$ scale, close to ``mostly different''. Behaviorally, the models diverge in ways the scores do not surface: they cover nearly every aesthetic aspect where humans are selective and repeat themselves across critiques of one photo, even when prompted to write at human length. We argue that reference-based similarity rewards a fluent, comprehensive critique style rather than the selectivity and specificity of human critique.