Agent Arena 对比 20,869 条轨迹，揭示前沿编码智能体的用户反馈差异
Arena 基于 20,869 条 Agent Arena 轨迹，比较了 Anthropic 与 OpenAI 的五款顶尖模型与其他智能体的用户批评差异。结果显示，在出现抱怨的会话中，前沿模型的修复循环失败占比低 6.9 个百分点，设计和产出可用结果的问题分别低 6.4 和 5.8 个百分点，遵循指令、完成任务及破坏既有工作的批评分别低 5.6、4.9 和 4.5 个百分点。
Where do users actually feel the difference with a frontier coding agent?
Across 20,869 Agent Arena traces, we compared how users criticized the five top models (all from @AnthropicAI & @OpenAI) on Agent Arena: Code, versus how they criticized other agents.
Here are 3 areas where frontier models make the most difference:
1. Less getting stuck: Among sessions with complaints, failed repair loops come up 6.9 percentage points less often, and project context gets ignored less often too (-1.9 pp). You spend less time steering the agent out of dead ends.
2. Better taste and code that works: They are better at design (-6.4 pp) and at producing working results (-5.8 pp). What you get back is more likely to be what you wanted.
3. More diligence: They follow instructions better (-5.6 pp), finish the work more often (-4.9 pp) and break existing work less (-4.5 pp). There's less to clean up afterwards.
Curious for a deeper dive into Agent Arena's praise vs complaint signal? See the article below in thread.
来源：arena · x.com