Core Viewpoint - The article introduces a new ranking method called Prompt-to-Leaderboard (P2L) that allows users to input any prompt and receive real-time rankings of large models, identifying the most suitable model for that prompt [1][10]. Group 1: P2L Ranking Mechanism - P2L ranks models based on their performance in response to specific prompts, enabling users to find the model that best addresses their needs [1][10]. - The ranking is dynamic, with models being evaluated in real-time as prompts are entered, showcasing their scores and relative performance [5][9]. - The system highlights the differences in model performance based on the nature of the prompt, such as the impact of content restrictions on rankings [7][10]. Group 2: Model Performance Examples - For a mathematical prompt, the model "03-mini-high" achieved the highest score of 1228, demonstrating its effectiveness in handling numerical tasks [5]. - In a prompt requiring HTML, CSS, and JS code for a 3D Earth, the model "Nous-Hermes-2-Mixtral-8x7B-DPO" scored 1257, indicating its proficiency in programming tasks [9]. - The rankings for prompts related to sensitive or inappropriate content showed that less restricted models performed better, while those with strict guidelines ranked lower [7][10]. Group 3: Additional Features and User Interaction - The platform offers a "P2L Router" feature that automatically selects the best model to respond to user prompts, enhancing user convenience [22][24]. - Users can explore various categories and subcategories to compare model performance across different tasks, providing a comprehensive view of model capabilities [18][20]. - The system also allows for user feedback and interaction, raising questions about the reliability and optimization of the ranking mechanism [25][26]. Group 4: Methodology and Evaluation - P2L utilizes a Bradley-Terry (BT) model to predict user preferences based on specific prompts, aiming to provide a more accurate ranking than traditional global rankings [29][30]. - The methodology focuses on the impact of prompts on model performance, allowing for tailored evaluations that reflect real-world usage scenarios [31][32]. - Experimental results indicate that P2L outperforms traditional ranking methods, particularly as the scale of models and datasets increases [35].