sentinel

Cost-aware multimodal control gates perception and context windows to balance performance against expense. Sentinel monitors token usage, adjusts reasoning depth, and routes between Flash models for speed and Pro models for depth, optimizing ROI on expensive calls like large-context analyses and real-time monitoring.

Switching between models depends on ROI metrics and task requirements. Sentinel monitors thinking_level and media_resolution, routing to lightweight models for quick sweeps and deeper reasoning models only when analysis justifies the cost, particularly for batch processing and large-context vision tasks.

Token budgeting for multimodal tasks means monitoring and controlling token usage across vision and audio streams simultaneously. Sentinel gates expensive calls by deciding between high-context windows and lightweight alternatives, ensuring perception costs stay aligned with performance requirements.

Speed optimization uses Flash models and lightweight processing like 150ms audio sweeps for quick results; depth optimization uses Pro models and 2M-context windows for comprehensive analysis. Sentinel routes based on ROI metrics, applying each approach where cost-performance trade-offs justify the choice.

Cost-sensitive routing compares ROI metrics against the expense of high-resolution vision and extended reasoning. Sentinel cross-references thinking_level, media_resolution, and Pulse metrics to decide whether upgrading to Pro models or larger context windows delivers sufficient value relative to their cost impact.