Labelbox has introduced a new Complex Reasoning Leaderboard to evaluate the reasoning and problem-solving abilities of advanced AI models. This initiative aims to assess AI performance across diverse and challenging domains, including mathematics, programming, and abstract reasoning, using a series of simulations designed by the expert Alignerr network. Despite the sophisticated methodology, which includes evaluating the models' reasoning processes and final answers, no model has yet surpassed a 75% aggregate score, indicating significant room for improvement. Google's Gemini 2.5 Pro emerged as the top performer, particularly excelling in pure math, computer science, and general reasoning tasks. However, the results highlight an existing performance gap, suggesting a need for further development and training to enhance the reliability and consistency of AI models in complex reasoning tasks. The leaderboard's findings underscore the importance of creating challenging but solvable datasets to accurately benchmark AI capabilities, with future efforts focusing on evaluating agentic models in dynamic real-world scenarios.