Reconstructing the benchmark behind Luc Julia's 64% LLM reliability claim (github.com)

A new repository has reconstructed the reasoning benchmark behind Luc Julia's frequently cited claim of 64% accuracy for large language models (LLMs). This figure, derived from a 2023 paper by Bang et al., inaccurately represents the performance of ChatGPT on a limited set of 634 carefully curated reasoning questions. The repository not only provides the full set of original questions and gold-standard answers but also develops a reduced, balanced set of 200 questions along with explicit grading rules, enabling reproducibility for any LLM currently available. This reconstruction is significant as it challenges the misleading narrative surrounding LLM reliability metrics, which Julia has popularized without appropriate context. The results show improved performance from newer models like GPT-5.6 Sol (96.5%) and Claude Opus 5 (98.5%) on this benchmark, suggesting that generative AI's capabilities have advanced significantly since the snapshot from December 2022. Furthermore, it emphasizes the importance of context and specificity in evaluating LLM performance, as the performance metrics are heavily dependent on the difficulty of tasks in the benchmark rather than being representative of the models' general reliability.