<p>Data drives our world today, and content generation is a very rewarding task. Traditionally content in any form (text, audio, images, etc.) are human generated, and often require experienced individuals for its’ inception. Large Language Models are processing units that are capable of generating content based on some large corpus of pre-fed data. <span>ChatGPT</span> is one of the popular text based Large Language Model. Now, since machines are capable of replicating human actions, competition evidently draws in and lately, such man-machine qualitative contrast gained a lot of attention. This research is towards the same directions, but is more of an evidence based, quantitative approach towards the comparison. To test both the subjects (machine and human intelligence), in this research we use CSES Problem Set (CSESPS), a collective set of problems that requires devising algorithms to solve them. Though, CSESPS contains several problems categories, we tend to check the programming skills, logic building, and geometric interpretation abilities of our subjects. Thus, for the analysis we considered problems from three specific categories, “<i>Introductory Problems</i>”, “<i>Mathematics</i>”, and “<i>Geometry</i>”. The statistical / empirical analysis conducted through our research evidences the lagging capabilities of machine intelligence than human intelligence. Quantitively, we conclude that Machines (to this day) are 46% less abled then average Human Intelligence (averaged for humans across the globe).</p>