雷峰网·2026-02-17 03:32Core Viewpoint - The article discusses the emergence of a new era in AI with the introduction of the Qwen3.5-Plus model by Alibaba, which is positioned as a leading open-source model with significant performance and efficiency improvements compared to existing models [2][19]. Group 1: Model Performance and Innovations - The Qwen3.5-Plus model has a total of 397 billion parameters, with only 17 billion activated during operation, achieving performance that surpasses models with over a trillion parameters [2][5]. - In various benchmark tests, Qwen3.5 achieved scores of 87.8 in MMLU-Pro, 88.4 in GPQA, and 76.5 in IFBench, outperforming competitors like GPT-5.2 and Claude 4.5 [3][12]. - The model introduces a mixed attention mechanism that reduces computational costs for long texts while maintaining processing accuracy [7][9]. - The architecture employs a sparse mixture of experts (MoE) approach, activating only a small fraction of parameters based on task requirements, significantly reducing computational waste [8][9]. - Qwen3.5-Plus features native multi-token prediction, allowing for faster response times in generating long texts and multi-turn dialogues [8][11]. Group 2: Cost Efficiency and Accessibility - The API pricing for Qwen3.5-Plus is set at 0.8 yuan per million tokens, making it 1/18 the cost of Gemini-3-pro, thus addressing affordability issues for small businesses and developers [5][14]. - The model's development benefits from Alibaba's integrated innovations in AI infrastructure, which enhance both training and inference efficiency [15][17]. - Qwen3.5 is open-sourced for global developers, with over 400 models already available, promoting widespread adoption across various industries [17][19]. Group 3: Market Impact and Future Outlook - The advancements in Qwen3.5 are expected to drive Alibaba Cloud's growth, with projections indicating a market share increase to 36% in the Chinese AI cloud market by mid-2025 [19]. - The article highlights the synergy between Alibaba's cloud services, chip technology, and model development as a key factor in achieving superior performance and cost-effectiveness [19].