How cheap models changed multi-agent economics

Orchestrator-executor agent systems divide work between a capable, expensive model that plans, delegates, verifies, and synthesizes results and cheaper specialized models that perform bounded, token-intensive tasks such as research, coding, extraction, and tool use. Although the architecture has appeared in research since 2023, recent improvements in lower-cost models have made it more economically viable, with reported results from Anthropic and other vendors indicating that cheaper workers can retain much of an all-frontier-model system’s quality at substantially lower cost. The central measure for selecting executor models is argued to be cost per successfully completed task rather than token pricing, since models with lower per-token prices may consume more tokens or perform less reliably. Evidence cited suggests that strong orchestration remains necessary because weaker models often struggle to decompose tasks, evaluate evidence, and manage delegation effectively, while simple model-routing approaches have not consistently outperformed using the best single model. Major providers including Anthropic and OpenAI now offer tooling for multi-agent configurations, but the text emphasizes that organizations should evaluate architectures and model combinations against their own workloads, including quality, latency, reliability, and total task cost.

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