Do Agent Teams Pay Off? A Case Study on Vibe Code Bench

Both OpenAI and Anthropic now ship native multi-agent support: a lead agent can spawn subagents, hand each of them part of a task, and assemble what they return. We wanted to measure what that buys, what it costs, and which kinds of work it helps, on tasks that resemble real software projects. We used Vibe Code Bench, our benchmark for building complete web applications from a product spec, as a test bed. Each app is deployed with Docker Compose and graded by browser agents that click through its workflows. The work splits naturally into a database, an API, an interface, and deployment, and bugs at the seams between those pieces show up directly in the score. We ran GPT 6 Sol and Claude Opus 5.5 on 50 apps, each as a single agent and as a team, at medium and max reasoning effort. In a team, the lead agent can hand parts of the build to subagents, up to five at once, and subagents cannot spawn their own. Both leads received the same short instruction: split the work into independent parts, delegate each one, then integrate and verify the result; the task prompt, sandbox, and grader were otherwise identical. Score…