Orchestration Sets Token Economics

Writer's controlled harness swap — same 22 tasks, same six models, same judges and price table, only the orchestration layer changes — moves cost per task −41%, tokens −38% and wall-clock −44% at quality parity, with every model cheaper by 33–61%; efficiency gains are model-invariant while quality gains scale almost perfectly with baseline capability (harness leverage, r = 0.99), and one net-new feature carries a capability floor below which exposing it produces failures; plus 'token maxing' as a named trajectory, the effective-input-price model under caching, the vendor-measures-own-product caveat that qualifies all of it, and Databricks' production counterpart on a multi-million-line codebase — three shipped third-party harnesses, same success rate at 2× less cost, ~3.1× per-task context spread