Large multimodal models (LMMs) typically process visual inputs with uniform resolution across the entire field of view, leading to inefficiencies when non-critical image regions are processed as precisely as key areas. Inspired by the human visual system's foveated approach, we apply a biologically inspired method to leading architectures such as MDETR, BLIP2, InstructBLIP, LLaVA, and ViLT, and evaluate their performance with variable resolution inputs. Results show that foveated sampling boosts accuracy in visual tasks like question answering and object detection under tight pixel budgets, improving performance by up to 2.7% on the GQA dataset, 2.1% on SEED-Bench, and 2.0% on VQAv2 compared to uniform sampling. Furthermore, our research indicates that indiscriminate resolution increases yield diminishing returns, with models achieving up to 80% of their full capability using just 3% of the pixels, even on complex tasks. Foveated sampling also prompts more human-like processing within models, such as neuronal selectivity and globally-acting self-attention in vision transformers. This paper provides a foundational analysis of foveated sampling's impact on existing models, suggesting that more efficient architectural adaptations, mimicking human visual processing, are a promising research venue for the community.