The bias towards larger models in training-free metrics such as SWAP-Score can have significant implications on the search outcomes during Neural Architecture Search (NAS). Larger models tend to yield higher metric values due to their increased complexity and parameter count. This bias may lead NAS algorithms utilizing these metrics to favor larger architectures over smaller ones when selecting candidates for evaluation or further optimization. As a result, there is a risk that smaller yet potentially efficient architectures might be overlooked or undervalued in the search process. Addressing this bias is crucial for ensuring fair evaluations and promoting diversity in architecture exploration during NAS.