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Most theories of humor emphasize the role of incongruity detection and resolution (e.g., frame\u2010shifting), as well as cognitive capacities like Theory of Mind and pragmatic reasoning. In multiple preregistered experiments, we ask whether and to what extent exposure to purely linguistic input can account for the human ability to recognize one\u2010line jokes and identify their entailments. We find that GPT\u20103, a large language model (LLM) trained on only language data, exhibits above\u2010chance performance in tasks designed to test its ability to detect, appreciate, and comprehend jokes. In exploratory work, we also find above\u2010chance performance in humor detection and comprehension in several open\u2010source LLMs, such as Llama\u20103 and Mixtral. Although all LLMs tested fall short of human performance, both humans and LLMs show a tendency to misclassify nonjokes with surprising endings as jokes. 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