Can LLMs write Base64 as well as they read it? (arvidsu.github.io)

Recent benchmarks have examined the capabilities of large language models (LLMs), focusing on their ability to both read and write Base64-encoded data. These evaluations reveal significant disparities among different models, highlighting variations in encoding fidelity, instruction-following, and logical reasoning. For instance, the GPT-5.6 model demonstrated a high pass rate of 92% in instruction-following but lagged in logic-based tasks with a 67% pass rate, emphasizing its strengths and weaknesses in practical applications. This analysis is crucial for the AI/ML community as it offers a detailed breakdown of model performances, providing insights into their practical utility for encoding data, a common requirement in data handling and communication. The implications of these findings suggest that while certain models excel in specific tasks, none yet achieve consistent performance across all categories, which could inform future model improvements and guide developers in selecting the most appropriate LLMs for specific use cases. Understanding these capabilities aids researchers in pushing the boundaries of AI applications, particularly in areas involving data processing and machine learning tasks.