Benchmarking 34 LLMs on Nonogram (Logic Puzzle) Solving (nonobench.mauricekleine.com)

A recent benchmarking study evaluated 34 large language models (LLMs) on their ability to solve Nonogram logic puzzles of varying sizes (5×5, 10×10, 15×15). The findings revealed that top performers included Claude 4.5 with an overall accuracy of 56.7%, outperforming other models like GPT-5, which had an accuracy of 53.3%. Such evaluations not only highlight the strengths and weaknesses of different models but also lay the groundwork for future improvements in puzzle-solving capabilities within AI systems. This benchmarking is significant for the AI/ML community as it provides valuable insights into the cognitive capabilities of different models when faced with structured problem-solving tasks, such as Nonograms. The study's methodology involves measuring average costs and time taken for each model, helping stakeholders understand the trade-offs involved in choosing specific LLMs for problem-solving applications. With an average overall accuracy of 53.2%, the results indicate a robust foundation for further research and development aimed at increasing the efficiency and accuracy of AI in logical reasoning tasks.