In the field of artificial intelligence, benchmark testing has always been regarded as the gold standard for measuring the capabilities of language models. Recently, Claude 3 Opus has emerged as a standout performer in multiple benchmark tests, surpassing previous models and even outperforming formidable competitors like OpenAI's GPT-4, showcasing its exceptional abilities in language understanding and generation. Claude 3 Opus and its family members, Claude 3 Opus Sonnet and Haiku, have garnered widespread attention since their release. This series of models has demonstrated outstanding performance in various language tasks, whether it be high school exam questions or logical reasoning tests, showcasing their superior capabilities compared to other models. However, for language models, the true test lies in their ability to flexibly handle various complex challenges in real-world scenarios. To comprehensively evaluate the capabilities of Claude 3 Opus, independent AI tester Ruben Hassid conducted a series of informal tests, directly comparing it with GPT-4. The results showed that Claude 3 Opus stood out with its refined processing capabilities in tasks such as summarizing PDF files and composing poetry. Although GPT-4 demonstrated certain advantages in internet browsing and parsing PDF charts, the overall performance of Claude 3 Opus remains remarkable. It is worth mentioning that in a test, Anthropic's instant engineer Alex Albert gave a brilliant demonstration of Claude 3 Opus' capabilities. He asked Opus to identify specific target sentences in a massive corpus of random documents, which is a highly challenging task for any generative AI. However, Claude 3 Opus not only successfully found these elusive sentences but also demonstrated metacognition by recognizing the artificial nature of the test, further proving its powerful adaptability in complex scenarios. This demonstration has also sparked deep thoughts in the industry: we need to go beyond traditional benchmark testing and develop evaluation methods that are closer to reality in order to have a more comprehensive understanding of the capabilities of language models. While benchmark tests provide valuable reference data, they often fail to capture the subtle functionalities and limitations of models in real-world environments. With the rapid development of AI technology, the industry urgently needs to explore more complex and comprehensive evaluation methods to address various challenges that may arise in practical applications. The rise of Claude 3 Opus marks the arrival of a new era in language benchmark testing. In this new era, we no longer rely solely on standardized tests to evaluate the quality of models, but instead place more emphasis on the adaptability, metacognition, and ability to handle real-world scenarios. As researchers and developers continue to push the technological boundaries of generative AI, the search for more comprehensive and accurate evaluation methods is of crucial significance in fully unleashing the potential of language models and driving the future development of AI technology. The outstanding performance of Claude 3 Opus has not only earned widespread acclaim for Anthropic, but also brought new hopes and expectations to the entire field of language models. We have reason to believe that in the near future, with the emergence of more advanced technologies and the improvement of evaluation methods, language models will play a significant role in various domains, making important contributions to the progress and development of human society.