Route Anything: model routing delivers 8.82X more quality per cost with <0.5-point loss
rohanpaul_ai · x · 2026-11-11
A UC Berkeley-led paper, Route Anything, studies model and policy routing for general-purpose AI systems. Key finding: routing inputs to the right model yields on average 8.82X more quality per unit cost while losing under 0.5 points.
- Built an omni-modal trajectory matrix spanning 25 benchmarks: language understanding, image/video/speech generation, coding and workplace agents, robotic manipulation
- 374,401 scored runs from 452 candidate configurations
- Establishes a unified theory of optimal routing under quality-cost trade-offs, showing that a router's attainable performance is determined by the information available to it
The paper frames orchestration of specialized models, tools, and policies as a practical path toward AGI rather than relying on a single model.
Related event: Route Anything: Model Routing Cuts Costs 8.82x on Average(2 posts)→
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