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.