Cohere 论文：多智能体编码中让廉价模型只做规划最省成本

New Cohere paper finds that a team of coding agents gets the most from extra agents when they only research and plan, and cheap models can do that job for a strong lead at far lower cost.

A team of coding agents beats 1 agent working alone, but costs 3 to 7 times as much. More agents can also mean duplicate work and extra code to merge.

The researchers built a lead agent that hands subtasks to workers, then varied the workers' role, model and count. On ProgramBench, agents rebuild a whole program from just a working executable and its docs.

Workers that only plan matched the reward of workers used in every stage, at half the cost. A strong GPT-5.6 Sol lead with 16 cheap Luna planners came within 1.4 points of an all-Sol team at 70% lower cost.

On tasks like this, put your strongest model in the lead and use cheap models only for planning, since cheap coders lowered reward.