A common assumption in AI is that multimodal models learn language in a more human-like way than language-only models, as they can ground text in images or audio. However, empirical studies checking whether this is true are largely lacking. We address this gap by comparing word representations from contrastive multimodal models vs. language-only ones in the extent to which they capture experiential information---as defined by an existing norm-based 'experiential model'---and align with human fMRI responses. Our results indicate that, surprisingly, language-only models are superior to multimodal ones in both respects. Additionally, they learn more unique brain-relevant semantic information beyond that shared with the experiential model. Overall, our study highlights the need to develop computational models that better integrate the complementary semantic information provided by multimodal data sources.