Core Viewpoint - The article discusses the perceived decline in the performance of AI models, particularly OpenAI's ChatGPT, highlighting a trend where AI models are designed to conserve resources by reducing their computational effort when possible [6][13][18]. Group 1: AI Model Performance - OpenAI's ChatGPT was found to struggle with basic arithmetic, raising concerns about its current capabilities compared to earlier versions [6][7]. - The introduction of models like LongCat and DeepSeek indicates a shift in the industry towards efficiency, with these models employing mechanisms to optimize token usage and processing [10][15][24]. Group 2: Cost Efficiency and Token Management - AI companies are implementing strategies to reduce token consumption, with OpenAI's GPT-5 reportedly saving 50%-80% in output tokens, which translates to significant cost savings for large organizations [13][18]. - The concept of a "perceptual router" has been introduced, allowing models to determine when to engage in complex processing versus simpler tasks, thereby enhancing efficiency [22][24]. Group 3: User Experience and Model Limitations - The new routing mechanisms have led to instances where models fail to engage deeply with user prompts, resulting in a lack of nuanced responses [30][34]. - Users have expressed frustration over the perceived loss of control and depth in interactions with AI models, particularly with the introduction of a one-size-fits-all approach [29][30].

Core Insights - The article discusses the perceived decline in the performance of AI models, particularly OpenAI's ChatGPT, as users report issues with basic arithmetic and reasoning tasks [1][2][4]. - There is a trend among AI companies to implement models that can decide when to engage in complex reasoning versus when to simplify tasks, primarily to reduce operational costs [7][12][19]. Group 1: AI Model Performance - Users have noted that the latest version of ChatGPT struggles with simple arithmetic, raising concerns about the model's capabilities compared to earlier versions [1][2]. - The introduction of models like LongCat by Meituan and Gemini by Google reflects a broader industry trend towards efficiency, allowing models to optimize their processing based on task complexity [4][6]. Group 2: Cost Efficiency Strategies - AI companies are adopting strategies that allow models to conserve resources by reducing the number of tokens used during processing, with OpenAI's GPT-5 reportedly cutting token usage by 50%-80% [7][12]. - The implementation of "perceptual routers" in AI models enables them to assess the complexity of tasks and allocate resources accordingly, which can lead to significant cost savings for companies [16][19]. Group 3: User Experience and Feedback - Users have expressed dissatisfaction with the new models, feeling that they lack the personality and engagement of previous versions, leading to calls for the return of older models [24][27]. - The article highlights that while efficiency improvements are beneficial for companies, they may negatively impact user experience if not managed properly [23][31].