研究显示 AI 达到同等基准性能的成本每年下降约 13 倍
AI performance costs are falling faster than those of any previous technology
Epoch 算出同性能成本一年降 13 倍，MIT 拆出算法本身贡献约 3 倍，但推理模型反而更烧钱，选型逻辑讲得挺清楚。
Epoch AI 的测算显示，AI 达到固定基准性能水平的价格每年下降约 13 倍，速度快于以往任何技术。MIT 研究在剔除硬件进步和竞争因素后，把算法层面的年度进步定在约 3 倍。
价格下降不等于最强模型更便宜，推理模型每个任务消耗的算力更多，成本可能反而更高。实际选模型时，质量、速度和错误率与价格同样重要。
AI performance costs are falling faster than those of any previous technology
AI is hitting a fixed benchmark performance level at a rapidly falling cost. Epoch AI measures a price decline of about 13x per year. After stripping out hardware gains and competition, MIT puts annual algorithmic progress at about 3x. That doesn't mean today's best models are cheaper, though. Reasoning models, for example, can cost more because they use far more compute per task. When picking a model for real-world use, quality, speed, and error rate matter just as much as price. The article AI performance costs are falling faster than those of any previous technology appeared first on The Decoder .
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Epoch AI、MIT、推理模型、基准测试，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「Decoder」（@Alan Truly），经 AITOP 多维评分管线结构化提炼。截至目前，全网已有 5 家独立信源对该事件进行交叉印证与跟进报道（含 Epoch AI、AI Will、Thomas Wolf 等），属于近期高关注度行业信号。
