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  "path": "/t/model-aware-task-delegation-for-subagents/1381671#post_1",
  "publishedAt": "2026-05-24T06:21:54.000Z",
  "site": "https://community.openai.com",
  "textContent": "Right now, when I create implementation plans, I often annotate each task with the recommended model and reasoning level, for example:\n\n\n    - [ ] T005 Add the SwiftUI app entry point and SwiftData model container wiring in DriftApp/App/DriftApp.swift\n      [model: gpt-5.4-mini | reasoning: medium]\n\n\nThis works well for planning, but when the orchestrator spawns subagents, they currently inherit the orchestrator’s own model instead of using the task-level configuration.\n\nThat creates friction because I have to manually switch models for nearly every task.\n\nIt would be extremely useful if the orchestrator could:\n\n  * Parse task metadata automatically\n\n  * Spawn subagents with the requested model\n\n  * Apply the specified reasoning level (`low`, `medium`, `high`, etc.)\n\n  * Optionally fall back to the orchestrator defaults if no task-level config is provided\n\n\n\n\nExample:\n\n\n    - [ ] T001 Create the SwiftUI iOS app project\n      [model: gpt-5.4-mini | reasoning: medium]\n\n    - [ ] T020 Design subscription state machine\n      [model: gpt-5.5 | reasoning: high]\n\n\nThis would enable much better cost/performance optimization:\n\n  * cheap models for scaffolding and boilerplate\n\n  * stronger models for architecture and critical logic\n\n  * more predictable execution across large implementation plans\n\n\n\n\nFor agentic workflows and spec-driven development, this would be a huge productivity improvement.",
  "title": "Model-aware task delegation for subagents"
}