This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as essential tools for evaluating Large Language Models (LLMs) in Korean. The Open Ko-LLM Leaderboard is built on two principles: alignment with the English Open LLM Leaderboard and the use of private test sets. These principles enable robust and fair evaluation of LLMs, minimizing data contamination risks. The Ko-H5 Benchmark includes multiple datasets, some derived from English benchmarks through translation and human review, and others created from scratch, such as Ko-CommonGen v2. The benchmark is designed to assess diverse aspects of LLM performance, including reasoning, commonsense, and truthfulness. The Ko-H5 benchmark includes private test sets that show minimal overlap with popular training datasets, reducing data leakage risks. Analysis of the benchmark reveals that Ko-CommonGen v2 introduces a new axis of evaluation, distinguishing the Open Ko-LLM Leaderboard from its English counterpart. Correlation studies show that Ko-TruthfulQA has lower correlation with other tasks, while Ko-CommonGen v2 has mid-level correlation with other tasks. Temporal analysis of the Ko-H5 score indicates that performance improves with model size, and that larger models perform better on certain tasks. The paper also discusses the need to expand beyond set benchmarks to ensure diverse and comprehensive evaluation of LLMs. It highlights the importance of community efforts in improving the leaderboard, including adherence to model card guidelines and avoiding merged models. The Open Ko-LLM Leaderboard is designed to be a standardized evaluation platform for Korean LLMs, with a focus on fairness, transparency, and ethical considerations. The leaderboard is continuously evolving, with new tasks being added to enhance its scope and utility. The paper concludes that the Open Ko-LLM Leaderboard and Ko-H5 Benchmark are valuable tools for advancing the evaluation of Korean LLMs, fostering linguistic diversity and promoting the development of more robust and inclusive AI systems.This paper introduces the Open Ko-LLM Leaderboard and the Ko-H5 Benchmark as essential tools for evaluating Large Language Models (LLMs) in Korean. The Open Ko-LLM Leaderboard is built on two principles: alignment with the English Open LLM Leaderboard and the use of private test sets. These principles enable robust and fair evaluation of LLMs, minimizing data contamination risks. The Ko-H5 Benchmark includes multiple datasets, some derived from English benchmarks through translation and human review, and others created from scratch, such as Ko-CommonGen v2. The benchmark is designed to assess diverse aspects of LLM performance, including reasoning, commonsense, and truthfulness. The Ko-H5 benchmark includes private test sets that show minimal overlap with popular training datasets, reducing data leakage risks. Analysis of the benchmark reveals that Ko-CommonGen v2 introduces a new axis of evaluation, distinguishing the Open Ko-LLM Leaderboard from its English counterpart. Correlation studies show that Ko-TruthfulQA has lower correlation with other tasks, while Ko-CommonGen v2 has mid-level correlation with other tasks. Temporal analysis of the Ko-H5 score indicates that performance improves with model size, and that larger models perform better on certain tasks. The paper also discusses the need to expand beyond set benchmarks to ensure diverse and comprehensive evaluation of LLMs. It highlights the importance of community efforts in improving the leaderboard, including adherence to model card guidelines and avoiding merged models. The Open Ko-LLM Leaderboard is designed to be a standardized evaluation platform for Korean LLMs, with a focus on fairness, transparency, and ethical considerations. The leaderboard is continuously evolving, with new tasks being added to enhance its scope and utility. The paper concludes that the Open Ko-LLM Leaderboard and Ko-H5 Benchmark are valuable tools for advancing the evaluation of Korean LLMs, fostering linguistic diversity and promoting the development of more robust and inclusive AI systems.