Brick: New AI Router System Reduces LLM Costs While Maintaining Quality
Researchers have developed Brick, a routing system that directs queries to the most cost-effective AI model based on task difficulty rather than surface-level features. The system evaluates models across six capability dimensions and allows operators to balance quality and cost at deployment time. This addresses a significant challenge in production AI systems where frontier models cost 10-100 times more than open-weight alternatives.
Brick is a multimodal router designed to optimize the deployment of large language models by intelligently routing queries to appropriate models based on their actual capability requirements. Rather than relying on simple heuristics like domain labels or keyword matching, the system scores each model across six capability dimensions and combines this with per-query difficulty estimates to make routing decisions via a cost-penalized geometric rule. In benchmarks on 5,504 queries, Brick achieved 76.98% accuracy at maximum quality (exceeding the best single model at 75.02%), while at a neutral cost-quality setting it achieved 74.11% accuracy at 4.71x lower cost than always using the strongest model. The system also reduced median latency from 51.2 seconds to 22.8 seconds, and operators can adjust a preference knob to slide between maximum quality and maximum savings profiles depending on deployment needs.
What different sources said
- arXiv cs.AICenter
Brick: Spatial Capability Routing for the Mixture-of-Models (MoM) Paradigm
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