The frontier isn’t a model. It’s a router.

This post introduces FireRouter, a system designed to dynamically select the optimal model for individual tasks within a larger AI workflow, moving beyond the concept of a single 'frontier' model. It presents empirical evidence from the DeepSWE benchmark, showing that a router can achieve 97.6% task completion at $1.88 per task by intelligently selecting from 18 models, a significant improvement over using a single best model like GPT-6 Astra (74.1% at $6.52). The analysis highlights that a few complementary models are sufficient to capture most of the collective capability, and that the challenge lies in predictable model selection. FireRouter is presented as a cache-aware system that routes at the task level, aiming to improve capability curves rather than just cost. Production traffic analysis shows a 53% cost reduction compared to using Opus 5 alone.