Seek .(US:SKLTY) Mei Ri Jing Ji Xin Wen·2026-01-29 14:52Core Viewpoint - Recent updates from multiple domestic large model manufacturers indicate a shift from merely competing on parameters and dialogue performance to a deeper focus on engineering and system-level capabilities, aiming to transition large models from "research achievements" to "industrial products" [1] Group 1: Model Updates - Alibaba released the Qwen3-Max-Thinking flagship reasoning model, while DeepSeek and Kimi updated their models with DeepSeek-OCR 2 and Kimi K2.5 respectively [1] - MiniMax launched the Music2.5 music generation model, addressing two major AI music technology challenges, which significantly boosted stock prices in the Hong Kong market, with MiniMax's stock rising over 20% and Zhiyu's stock increasing over 10% [1] Group 2: Challenges in Engineering Phase - The first challenge is balancing cost and efficiency, as high-parameter models incur substantial training and inference costs, making it financially burdensome for most companies to adopt top models for full-scale business operations [2] - The second challenge involves meeting industrial-grade requirements for stability and interpretability, as current models still exhibit issues like "hallucinations" and output variability, which could pose significant risks in critical applications such as financial risk control and medical diagnosis [2] - The third challenge is the integration with existing systems, which requires complex API connections, data format conversions, workflow restructuring, and adaptation of security frameworks, yet many models remain at the "chat demonstration" level without deep integration capabilities [2] Group 3: Path to Overcoming Challenges - Breakthroughs in each challenge are technically demanding, necessitating a shift from "pursuing extreme parameters" to "optimizing unit computational efficiency" to ensure affordability and usability for enterprises [3] - Companies are increasingly seeking stable problem-solving capabilities rather than just technical specifications, prompting a shift from merely providing models to offering comprehensive services and solutions [3] - Implementing techniques like prompt engineering and retrieval-augmented generation can help build safeguards for key application scenarios, effectively controlling hallucinations and enhancing result reliability and interpretability [3]