Large language model (LLM)s’ next-word predictions have shown impressive performance in capturing human expectations during real-time language comprehension. This finding has enabled a line of research on psychometric benchmarking of LLMs against human language-comprehension data in order to reverse-engineer humans’ linguistic subjective probability distributions and representations. However, to date, this work has exclusively involved unimodal (language-only) comprehension data, whereas much human language use takes place in rich multimodal contexts. Here we extend psychometric benchmarking to visual language models (VLMs). We develop a novel experimental paradigm, Image-Conditioned Maze Reading, in which participants first view an image and then read a text describing an image within the Maze paradigm, yielding word-by-word reaction-time measures with high signal-to-noise ratio and good localization of expectation-driven language processing effects. We find a large facilitatory effect of correct image context on language comprehension, not only for words such as concrete nouns that are directly grounded in the image but even for ungrounded words in the image descriptions. Furthermore, we find that VLM surprisal captures most to all of this effect. We use these findings to benchmark a range of VLMs, showing that models with lower perplexity generally have better psychometric performance, but that among the best VLMs tested perplexity and psychometric performance dissociate. Overall, our work offers new possibilities for connecting psycholinguistics with multimodal LLMs for both scientific and engineering goals.