Core Insights - Weibo has launched its first self-developed open-source large model, VibeThinker, which has only 1.5 billion parameters but outperformed the much larger DeepSeek R1 model with 671 billion parameters in benchmark tests [1][7] - The cost of a single post-training session for VibeThinker is only $7,800, significantly lower than competitors like DeepSeek and MiniMax, which have costs in the hundreds of thousands [2][10] - This breakthrough may shift the AI industry focus from a "scale competition" to an "efficiency revolution" [3][9] Industry Disruption - The AI industry has traditionally viewed parameter count as the primary measure of model capability, with a belief that complex reasoning requires over 100 billion parameters [5][6] - VibeThinker challenges this notion by demonstrating that a smaller model can achieve superior performance through optimized model structure and training methods, specifically the "Spectrum to Signal Principle" (SSP) [7][8] - The model's performance in high-difficulty mathematical tests has garnered significant attention, with endorsements from platforms like HuggingFace [7] Cost Revolution - VibeThinker's training cost is a fraction of what is typical in the industry, with the total cost being approximately $7,800 for the entire post-training process [10][13] - This cost efficiency allows for broader access to advanced AI capabilities, enabling smaller companies and research institutions to participate in AI innovation [13] Application and Ecosystem Development - Weibo is actively integrating AI technology across various business scenarios, enhancing user experience and content production efficiency [15][20] - The company plans to leverage its unique data assets to create a model that better understands public sentiment and social needs [17][18] - VibeThinker is expected to drive multiple AI applications within Weibo, enhancing user experience and potentially creating a new "social super-ecosystem" [19][20]
啊？微博7800美元训的大模型，数学能力超了DeepSeek-R1