CrewAI's multi-agent benchmark: 40–60% gains, but the task selection tells a story
CrewAI claims role-based multi-agent crews outperform single-agent approaches by 40–60% on complex tasks. We looked at which tasks they chose and what that selection reveals about where multi-agent actually helps.
The 12 task categories in CrewAI's benchmark are well-chosen — they're genuinely complex, require diverse tool access, and benefit from specialization. The cost-per-task comparison across model providers is the kind of practical data that's hard to find in academic multi-agent papers.
What's worth understanding: the 40–60% improvement is measured on tasks specifically designed for multi-agent approaches. On simpler, focused tasks, the overhead of agent coordination often makes a single agent faster and cheaper. CrewAI doesn't hide this — the benchmark methodology is transparent — but the headline number only applies to the complex end of the spectrum. If your use case is a straightforward extraction pipeline, don't expect these gains.