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. 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.

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.

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.

The announcement follows Vals AI's benchmarking study. The study suggests that the extra cost of agent teams isn't worth it in most cases, especially when models are already running at full compute.

Vals AI did not say whether the results apply to all tasks or models, and the open question remains about whether larger agent teams could yield better results in specific scenarios. The study highlights the need for further research into the efficiency of multi-agent systems.

Source: thedecoder