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Based on 20,869 traces from Agent Arena: Code, Arena.ai compared user criticism signals of five top models from Anthropic and OpenAI against other agents. The analysis found that frontier models differ most noticeably in three dimensions: 6.9 percentage points fewer complaints about failure-fix loops and 1.9 percentage points fewer about ignoring project context; 6.4 percentage points fewer design issues and 5.8 percentage points fewer issues with producing usable results; and 5.6, 4.9, and 4.5 percentage points fewer complaints about instruction following, task completion, and breaking existing code, respectively. There are currently no follow-up developments.
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Arena analyzed 20,869 records: three major differences among frontier coding agentsDevelopments
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Oct 28
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Arena analyzed 20,869 records: three major differences among frontier coding agents
Based on 20,869 traces from Agent Arena: Code, Arena compared the user complaint signals for five top-tier models from Anthropic and OpenAI against other agents, and found that frontier models differ most noticeably along three dimensions. Complaints about failure-repair loops were 6.9 percentage points lower, and complaints about ignoring project context were 1.9 percentage points lower; complaints about design issues were 6.4 percentage points lower, and complaints about producing usable results were 5.8 percentage points lower. Complaints about instruction following, task completion, and breaking existing code were 5.6, 4.9, and 4.5 percentage points lower, respectively.