I compared qwq-32b preview to Marco 01

The article compares the performance of two language models, QwQ-32B-Preview and Marco-01, across various problem-solving domains, such as mathematical reasoning, logical puzzles, abstract reasoning, contextual understanding, common-sense reasoning, and coding problems. Despite the significant difference in size, with QwQ-32B being much larger, Marco-01 demonstrates notable performance, often providing more direct and concise solutions. Both models achieve similar results in solving systems of equations, logic puzzles, and identifying patterns in abstract reasoning, although QwQ-32B tends to explore multiple approaches in more depth. In contextual understanding, both models propose optimal distributions for venue capacity challenges, while in common-sense reasoning, they predict similar changes to a wooden spoon in boiling water. Their coding problem solutions involve recursion to flatten nested lists, with both models effectively managing lists of varying depths.