Core Viewpoint - The article emphasizes the importance of conducting a fair, objective, and rigorous assessment of AI models' mathematical capabilities, particularly in the context of high school entrance examinations [1]. Testing Methodology - The testing utilized the 2025 National Mathematics Exam, focusing solely on objective questions and excluding subjective ones to ensure clarity in scoring [1]. - LaTeX was used to format the questions, ensuring accurate representation of mathematical symbols, thus avoiding potential misinterpretations from image recognition [1]. - The testing excluded a specific question that involved a chart to prevent ambiguity in understanding [1]. Scoring System - The scoring followed the principles of the actual high school entrance examination, with specific point allocations for different types of questions: single-choice questions (5 points each), multiple-choice questions (6 points each), and fill-in-the-blank questions (5 points each) [3]. - Each question was answered three times by the AI models to minimize errors, with the final score calculated based on the proportion of correct answers [3]. - The models were tested without external prompts, internet access, or coding capabilities to ensure a pure assessment of reasoning skills [3]. Model Performance - The models tested included OpenAI o3, Gemini 2.5 Pro, DeepSeek R1, and others, with results indicating varying levels of performance across the board [5]. - Gemini 2.5 Pro achieved the highest accuracy, while other models like DeepSeek and Qwen3 performed less favorably due to minor errors in specific questions [10]. - The overall results suggested that the differences in performance among the models were minimal, with most errors attributed to small misinterpretations rather than significant flaws in reasoning capabilities [10]. Conclusion - The article concludes that the rigorous testing process provided valuable insights into the mathematical abilities of AI models, highlighting the need for objective and fair evaluation methods in AI assessments [10].