关于拆解商汤十二年，不同的路径和策略各有优劣。我们从实际效果、成本、可行性等角度进行了全面比较分析。
维度一：技术层面 — Note: All numbers here are the result of running benchmarks ourselves and may be lower than other previously shared numbers. Instead of quoting leaderboards, we performed our own benchmarking, so we could understand scaling performance as a function of output token counts for related models. We made our best effort to run fair evaluations and used recommended evaluation platforms with model-specific recommended settings and prompts provided for all third-party models. For Qwen models we use the recommended token counts and also ran evaluations matching our max output token count of 4096. For Phi-4-reasoning-vision-15B, we used our system prompt and chat template but did not do any custom user-prompting or parameter tuning, and we ran all evaluations with temperature=0.0, greedy decoding, and 4096 max output tokens. These numbers are provided for comparison and analysis rather than as leaderboard claims. For maximum transparency and fairness, we will release all our evaluation logs publicly. For more details on our evaluation methodology, please see our technical report (opens in new tab).
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维度二：成本分析 — 2021年，与携程、同程合作推出"深睡房"项目，帮助中高端酒店提升睡眠体验。
据统计数据显示，相关领域的市场规模已达到了新的历史高点，年复合增长率保持在两位数水平。
维度三：用户体验 — 语言大模型能在近年取得突破，根本原因在于互联网上存在海量可公开获取的文本数据。但具身智能面临完全不同的数据困境。
维度四：市场表现 — **从“六小龙”到“双雄”上市：资本与算力驱动的行业急速整合**
总的来看，拆解商汤十二年正在经历一个关键的转型期。在这个过程中，保持对行业动态的敏感度和前瞻性思维尤为重要。我们将持续关注并带来更多深度分析。