AGI Ranker发布了对其AI模型排名系统的全面审计报告，在v2.0.0版本中实现了方法论的重大调整[1]。该更新于2026年7月29日发布，涉及人类基准线重新测量和算法优化，导致所有模型的评分均出现下降，幅度介于6至15分之间，但模型间的相对排名顺序基本保持不变[1]。
此次调整的核心改进包括三个方面[1]。首先是人类天花板的重新验证，系统从原有的11个基准中精简至仅4个具有真实人类测量结果的基准，其余11个改为采用1.00的标准基准值[1]。其次是Agency组件的重建，采用了4个独立评估器——包括SWE-bench Verified、Terminal-Bench 2.1、τ³-Banking和LiveBench Agentic Coding——以减少对单一评估机构的依赖[1]。此外，评分权重也进行了重新分配，Artificial Analysis的占比从45%降至26%，而Knowledge组件从此前的97%单一来源改为52/48的双源分布[1]。
为提升透明度，系统现已将所有156个评分单元都与记录来源关联，使来源信息成为评分的必要条件而非可选项[1]。报告指出，评分下降约6分源于天花板调整，而进一步的下降则来自Agency组件的重建[1]。此外，AGI Ranker还修复了社交媒体预览卡片的缓存问题，改采版本化URL方案来强制重新获取数据，解决了旧文件缓存导致预发布数据被分享的问题[1]。
AGI Ranker has released a comprehensive audit of its AI model ranking system, revealing significant methodological changes in the v2.0 update released on July 29, 2026 [1]. The overhaul resulted in all model scores declining by 6 to 15 points, though the relative ranking order of models remained largely unchanged [1].
The primary driver of score adjustments was a recalibration of human baseline measurements [1]. The system reduced its human benchmark reference from 11 bases to only 4 that contained actual human measurement data, while the remaining 11 benchmarks were converted to a standardized 1.00 baseline value [1]. This recalibration alone accounted for approximately 6 points of the score decline [1]. Additional reductions stemmed from the reconstruction of the Agency component, which now draws from four independent evaluators—SWE-bench Verified, Terminal-Bench 2.1, τ³-Banking, and LiveBench Agentic Coding—to reduce reliance on any single evaluation source [1].
The update also rebalanced data source weighting across multiple dimensions [1]. Artificial Analysis, which previously accounted for 45 percent of scores, was reduced to 26 percent, while the Knowledge component shifted from a single-source structure at 97 percent to a dual-source distribution split 52-48 [1]. All 156 scoring units now require documented sources as a mandatory condition, rather than being optional [1]. The company also resolved a technical issue where cached social media preview cards were sharing outdated pre-release data by implementing versioned URLs to force fresh data retrieval [1].