K2 Thinking再炸场，杨植麟凌晨回答了21个问题

Core Insights - The article discusses the recent release of K2 Thinking, a large AI model developed by Kimi, highlighting its significant advancements and the implications for the AI industry [5][14][15]. Group 1: Model Release and Features - K2 Thinking is a model with 1 trillion parameters, utilizing a sparse mixture of experts (MoE) architecture, making it one of the largest open-source models available [14]. - The model has shown impressive performance in various benchmark tests, particularly in reasoning and task execution, outperforming GPT-5 in certain assessments [15][16]. - K2 Thinking's operational cost is significantly lower than that of GPT-5, with a token output price of $2.5 per million tokens, which is one-fourth of GPT-5's cost [16]. Group 2: Development and Training Insights - The Kimi team has adopted an open-source approach, engaging with communities like Reddit and Zhihu to discuss the model and gather feedback [7][8]. - The training of K2 Thinking was conducted under constrained conditions, utilizing H800 GPUs with Infiniband, and the team emphasized maximizing the performance of each GPU [29]. - The training cost of K2 Thinking is not officially quantified, as it includes significant research and experimental components that are difficult to measure [29][34]. Group 3: Market Trends and Competitive Landscape - The release of K2 Thinking, along with other models like GLM-4.6 and MiniMax M2, indicates a trend of accelerated innovation in domestic AI models, particularly in the context of supply chain disruptions [28][30]. - Different companies are adopting varied strategies in model development, with Kimi focusing on maximizing performance and capabilities, while others like MiniMax prioritize cost-effectiveness and stability [32][33]. - The article notes that the open-source model ecosystem in China is gaining traction, with international developers increasingly building applications on these models [33].