The text highlights various LLM leaderboards that can help developers choose the right model for their AI applications. These leaderboards benchmark models using different evaluation harnesses, datasets, and metrics to provide a comprehensive comparison of their performance across various tasks such as reasoning, general knowledge, function calling, and more. The leaderboards include Open LLM Leaderboard, MTEB Leaderboard, Big Code Models Leaderboard, SEAL Leaderboards, Berkeley Function-Calling Leaderboard, Occiglot Euro LLM Leaderboard, LMSYS Chatbot Arena Leaderboard, Artificial Analysis LLM Performance Leaderboard, Open Medical LLM Leaderboard, Hughes Hallucination Evaluation Model (HHEM) Leaderboard, OpenVLM Leaderboard, and LLM-Perf Leaderboard. Each leaderboard provides a unique perspective on model performance, from general knowledge to specific tasks like function calling or hallucination detection. By using these leaderboards, developers can make informed decisions about which models to use for their projects, ensuring the best possible results.