Machine-vision representations of faces can be aligned to people’s first impres- sions of others (e.g., perceived trustworthiness) to create highly predictive models of biases in social perception. Here, we use deep tensor fusion to create a unified model of first impressions that combines information from three channels: (1) visual information from pretrained machine-vision models, (2) linguistic information from pretrained language models, and (3) demographic information from self-reported demographic variables. We test the ability of the model to generalize to held-out faces, traits, and participants and measure its fidelity to a large dataset of people’s first impressions of others.