{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/83ecfb95ebe944b0a267235a94adcebd\" frameborder=\"0\" width=\"1108\" height=\"831\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":831,"width":1108,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":831,"thumbnail_width":1108,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/83ecfb95ebe944b0a267235a94adcebd-07e9a4399700c2a9.gif","duration":177.348,"title":"Cost-Aware Model Selection for Coding Agents","description":"This Loom explains a cost-aware coding agent approach to stop agents from routing every task to the same flagship model. It describes a pipeline with Planner and Schedule, adding a Debate Layer where multiple agents discuss and narrow each user prompt to two different models from a list of five. The system then runs the task on those models, returning generated code along with an exact cost breakdown and critic-based scoring on code quality and performance. The creator notes added latency because two models are used, and mentions Sonnet, Opus, GPT-Mini, and GPT-Nano, with plans to add more models later."}