Better and cheaper together: Open models explore, frontier models patch

Executor-orchestrator architectures have gained renewed interest for their potential to reduce computational costs by utilizing a hierarchical model approach where smaller, cost-effective models handle initial exploration and context extraction before a more sophisticated frontier model finalizes the task. This setup, exemplified by a pipeline involving MiniMax-M3, GPT-5.2, and either Opus 4.8 or Fable 5 as the frontier model, achieves a state-of-the-art resolve rate of 80.8% on the SWE-Bench Pro benchmark while maintaining a cost of $5.99 per task. The architecture strategically assigns specific tasks to models based on their efficiency, with junior models conducting parallel rollouts to map problem areas, senior models refining context with deeper code insights, and the frontier model delivering the final solution. This method not only improves accuracy by ensuring the frontier model works with high-quality context but also reduces costs compared to single-model approaches, demonstrating the advantage of matching the model to the task stage and optimizing each step for efficiency.

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