AICodeKing compares five lead-worker model pairings across six projects, including emulation, a container tool, a room designer, a keyboard configurator, a small game and a desktop activity app. Each pair receives one high-effort run per task, without matched computation budgets, solo baselines or reversed roles. The results therefore compare delivered projects, not the isolated benefit of delegation.
AICodeKing shows how early planning omissions propagate into incomplete applications: some teams improve editors while leaving device communication or execution paths absent. More useful review loops reproduce disputed failures, assign specific repairs and check the rebuilt package. Working demonstrations still have compatibility, persistence or integration limits that prevent broad product-readiness claims.
AICodeKing favors Opus leading Sol for a starting setup, with Opus leading Sonnet close behind, but keeps task-specific strengths visible. Provider interruptions prevent a fair overall capability ranking across all five teams. Only eight of the thirty tasks have complete captured usage, so the recorded cost subtotals cannot establish the cheapest team or a score-per-dollar winner.
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