Core 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]

第三道坎，是与现有系统的融合之困。大模型能力如何融入建设多年的现有系统，这涉及复杂的API （应用程序编程接口）对接、数据格式转换、工作流重组以及权限与安全体系的适配。然而，许多模型 当前仍停留在"聊天演示"层面，缺乏与企业核心业务系统深度、无缝集成的"中间件"与标准接口。 从此次国内各大模型厂商更新方向来看，大模型的能力进阶以及市场竞争的焦点，已然跳出了单纯比拼 参数与对话表现的阶段，迈向了更深一层的工程化、系统级能力重塑。 简言之，就是让大模型完成从"科研成果"向"工业产品"的跨越，特别是要让非AI专业的业务团队也能稳 定安全、高可用、低成本地消费大模型。与一味堆算力的前期阶段不同，这无疑更加考验国内大模型的 综合能力。 笔者认为，目前国内大模型迈入工程化阶段仍面临三道坎。 第一道坎，是成本与效能的平衡难题。大模型尤其是高参数模型的训练与推理成本高昂，堪称"算力吞 金兽"。对于多数企业而言，自建或频繁调用顶尖模型进行全量业务处理，财务压力巨大。如何在保持 核心性能的同时，大幅降低部署与使用门槛，是规模化应用必须跨越的第一道坎。 第二道坎，是稳定性与可解释性的工业级要求。大模型在实验室的优异表现，不能与其在 ...