研究显示多智能体团队消耗大量 token 却几乎无质量提升
AI agent teams waste massive tokens for barely measurable quality gains, research finds
Vals AI 在 Vibe Code Bench 上测试 GPT-6 Sol 和 Claude Opus 5.5 的单智能体与团队表现，团队成本为单体的 1.8x 到 5.1x，四组对比中仅 GPT-6 Sol 在 medium reasoning 下高出 7.3 分且具统计显著性。
Oct 11, 2026
AI agent teams deliver almost no better results than single agents, research finds.
Evals company Vals AI tested GPT-6 Sol and Claude Opus 5.5 on the "Vibe Code Bench," both solo and as teams, at two reasoning levels: medium and maximum reasoning effort. The teams cost between 1.8x and 5.1x more than single agents.
Out of four comparisons between teams and solo agents, only one showed a statistically significant improvement: GPT-6 Sol at medium reasoning, where the team scored 7.3 points higher. At maximum reasoning, the team setup gave neither Sol nor Opus 5.5 any real advantage. The results suggest that the extra cost of agent teams isn't worth it in most cases, especially when models are already running at full compute.
Anthropic saw quality gains shrink as it added more agents in two of its own tests with Opus 5.5. Larger teams reached a given performance level faster, but going from ten to 100 agents only nudged scores up slightly after 24 hours. In separate ProgramBench tests, speed gains came with higher token usage.
| Task with Opus 5.5 | 1 agent | 10 agents | 30 agents | 100 agents |
|---|---|---|---|---|
| Knowledge base | 0.53 | 0.70 | 0.71 | 0.74 |
| Lean Theorem Proving | 0.39 | 0.66 | 0.66 | 0.68 |
Fable 5.1 showed stronger quality gains on the Lean theorem proving task above ten agents, but still scored below Opus 5.5 across all tests. On the knowledge base task, Fable's score actually dipped slightly when scaling from 30 to 100 agents.
More agents buy speed but not better output
OpenAI researcher Noam Brown confirmed in the Dwarkesh Podcast that multi-agent systems mainly buy speed, not better quality. Four agents solved tasks twice as fast but also cost twice as much. At 16 agents, the pattern held but grew slightly less efficient.
The effect depends heavily on the task. Web research and math parallelize well, but writing a novel doesn't, he said. Throwing 10,000 agents at a novel would be just as pointless as throwing 10,000 people at it. Brown acknowledged that scaling to very large numbers of agents remains largely unexplored because the costs are simply too high.
OpenAI developer Eric Provencher recently warned against using agent swarms for exactly this reason. They're most likely wasted money, he argued, because coordination between agents breaks down. He called it the coordination tax.
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