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• We introduce a psychophysics-inspired way to probe the perception of Multimodal Large Language Models (MLLMs) • We estimate the contrast sensitivity functions (CSFs) of MLLMs using prompt-based psychometric functions • We show the CSF shape and scale vary widely across models, diverging from human vision • We reveal that models and CSF estimates are highly sensitive to prompt phrasing • We demonstrate the CSF of models predicts classification robustness under filtering and adversarial noise Understanding how Multimodal Large Language Models (MLLMs) process low-level visual features is critical for evaluating their perceptual abilities and has not been systematically characterized. Inspired by human psychophysics, we introduce a behavioural method for estimating the Contrast Sensitivity Function (CSF) in MLLMs by treating them as end-to-end observers. Models are queried with structured prompts while viewing noise-based stimuli filtered at specific spatial frequencies. Psychometric functions are derived from the binary verbal responses. Therefore, contrast thresholds (and CSFs) are obtained without relying on internal activations or classifier-based proxies, as opposed to previous reports on artificial networks. Our results reveal that some models resemble human CSFs in shape or scale, but none capture both. We also find that CSF estimates are highly sensitive to prompt phrasing, indicating limited linguistic robustness. Finally, we show that CSFs predict model performance under frequency-filtered and adversarial conditions. These findings highlight systematic differences in frequency tuning across MLLMs and establish this CSF estimation as a scalable diagnostic tool for multimodal perception.