Agent teams cost up to 5.1x more than one agent and rarely scored better. Start your automations with one

Splitting a job across a team of AI agents usually bought speed or nothing, at up to five times the price.

What happened

Vals AI ran GPT-6 Sol and Claude Opus 5.5 on its Vibe Code Bench, building 50 complete web apps from a spec, once as a single agent and once as a team where a lead agent could hand work to up to five subagents. Each setup ran at medium and max reasoning effort.

Teams cost 1.8 to 5.1 times as much. Only one of the four comparisons was statistically significant: Sol's medium effort team scored 7.3 points higher than its single agent. Opus at medium effort as a single agent scored 91.5% for about $4.08 per app. Its max effort team scored 93.2% at thirty times that cost, a gap Vals did not find significant.

Most of the extra spend was cached input. Every subagent carries its own context with the spec and its working state, and resends it on every model call. The Decoder also points to Anthropic's own testing, where going from ten to 100 agents only nudged scores up.

The useful detail: where Sol's team gained 20 points or more, the single agents had reported tests passing while some features were never built. The team's lead caught it by checking the separately built parts against each other.

My take

For business automations this is a clear default. Start with one agent and one well scoped job. If quality is the problem, the win in this study came from checking, not from more workers. A separate verification step that compares the output against the source record is cheaper than a swarm.

Split work only when it is truly parallel and independent, like enriching 500 leads where no record depends on another. Even then, measure cost per finished job, not cost per token, because each extra agent pays to reread the same context.

Before you approve an agent team in a client build, ask two questions. What does the second agent check that the first cannot? And what does it cost per completed task? If nobody can answer, it is overhead.

I design and build AI automations for real jobs: lead routing, CRM data entry, reporting, content pipelines and agent workflows, sized to the job instead of the hype. See what I build at romielwillautomate.dev.