In the realm of assembly processes, collaborative robots (cobots) are recognized as valuable assets for enhancing production performance, encompassing assembly time, product quality, and worker satisfaction. Nonetheless, there's a noticeable absence of models that effectively assess cobot integration and assist decision-makers in selecting the most economically efficient assembly setup. This study aims to bridge this gap by proposing an innovative cost evaluation model, facilitating a practical comparison among various assembly configurations to aid in choosing the most optimal one. The proposed model accounts for diverse cost dimensions such as manufacturing, setup, prospective, retrospective, product quality, and worker well-being. Furthermore, it incorporates the influence of learning effects on assembly time and quality, which is particularly pertinent in scenarios of low-volume and mass-customized productions.