Artificial Analysis@ArtificialAnlys · X人工智能分析发布编码代理基准指数，评估不同模型与执行框架组合在三大编码基准中的表现。Opus 4.7在Cursor CLI中以61分领先，GPT-5.5与Opus 4.7在其它框架中得分60紧随其后。开源模型GLM-5.1在Claude Code中获得53分，表现竞争但仍显著落后顶尖闭源模型。经济性差异悬殊：每任务成本从Composer 2的0.07美元到GLM-5.1的2.26美元不等，后者因任务循环令牌使用高达480万；任务耗时差异超7倍，Opus 4.7仅需6分钟而Kimi K2.6需40分钟。缓存命中率普遍较高，影响实际运行成本。
Announcing the Artificial Analysis Coding Agent Index！ Our new coding agent benchmarks measure how combinations of agent harnesses and models perform on 3 leading benchmarks， token usage， cost and more
When developers use AI to code they're choosing a model， but also pairing it with a specific harness. It makes sense to benchmark that combination to understand and compare performance.
The Artificial Analysis Coding Agent Index includes 3 leading benchmarks that represent a broad spectrum of coding agent use： ➤ SWE-Bench-Pro-Hard-AA， 150 realistic coding tasks that frontier models struggle with， sampled from Scale AI's SWE-Bench Pro ➤ Terminal-Bench v2， 84 agentic terminal tasks from the Laude Institute and that range from system administration and cryptography to machine learning. 5 tasks were filtered due to environment incompatibility ➤ SWE-Atlas-QnA， 124 technical questions developed by Scale AI about how code behaves， root causes of issues， and more， requiring agents to explore codebases and give text answers