深度｜ 大模型年终观察，如何定义2025年的"好模型"？

Core Insights - The AI industry is transitioning from a "score-based" evaluation to a "trust-based" framework, emphasizing the importance of open-source models as a default choice for businesses [1][2][3] Group 1: Industry Trends - The concept of "score fatigue" is prevalent in the AI sector, leading to a shift towards open-source models like DeepSeek, Qwen, and Kimi as essential tools [1] - The industry mindset is evolving from a "championship-style" competition to a "partnership-based" approach, where foundational capabilities are merely entry tickets, and trust is built through evaluation, deployment, and delivery [2] Group 2: Key Signals - The AI model landscape is showing a significant change, with open-source models capturing over one-third of the total token share by the end of 2025, indicating a stable demand post-launch [5] - The usage of reasoning models has surged, accounting for over 50% of token consumption, reflecting a growing complexity in tasks assigned to AI [8][12] Group 3: Evaluation Metrics - The evaluation of AI models is moving towards a multi-dimensional framework, incorporating both performance and cost metrics to assess value [20] - Kimi K2 Thinking exemplifies this trend by achieving top scores in key evaluations, gaining significant attention and trust from the community [14][18] Group 4: Deployment and Infrastructure - The deployability of models is becoming a critical factor, with advancements in hardware allowing for significant cost reductions and performance improvements [24] - Cloud platforms are enhancing transparency in deployment costs, shifting from estimation to clear pricing models for token usage [24] Group 5: Delivery and Governance - The final step in ensuring trust involves governance, observability, and reproducibility of AI models in enterprise settings [25] - Major cloud providers are integrating top models into their enterprise services, facilitating standardized API access and security measures [26] Group 6: Future Directions - The focus for 2026 will be on operational excellence, emphasizing task completion rates, stability, and alignment with real workloads [31] - Trust in AI models is increasingly seen as a product of engineering rather than belief, highlighting the importance of reliability in achieving productivity [32]