Route First, Call Rarely

Track 1 scores submissions on Fireworks tokens spent above an accuracy bar, not on feature count. The naive path — route every task to the strongest model — is the most expensive way to pass. Most tasks don't need a frontier model, and a meaningful share don't need a model at all. Route First, Call Rarely is a tiered routing cascade. A zero-token classifier (regex/heuristics) sorts each task first. Tier 0 solves arithmetic, dates, strings, units, and extraction locally with deterministic solvers — no API call, ever — and only answers when it can self-validate. Everything else goes to Tier 1: the cheapest Fireworks model that can handle the task, minimal prompts, temperature 0, and per-model reasoning suppression so models like gpt-oss-20b don't burn tokens on hidden chain-of-thought. A confidence gate (format validation + Tier-0 cross-check, logprobs secondary) accepts the answer or escalates — exactly once — to the strongest allowed model. No retry loops, no self-verification. Measured on the full 200-task live eval against the real Fireworks API: 99.5% accuracy (199/200), 32.5% of tasks (65/200) resolved with zero Fireworks tokens, ~47-51% token reduction vs. an honest naive baseline, $0.003636 total price-weighted cost, 1.45% retry rate counted in every total above. What makes it different: the comparison baseline is deliberately fair — reasoning suppression explicitly disabled, not a strawman. Routing logic sits behind an isolated adapter contract, so swapping models never touches it. The model registry already prefers gemma* models at equal price tier — unit-tested and dormant, since Gemma wasn't reachable on our Fireworks account when probed July 7. Stack: Python, FastAPI demo dashboard, Fireworks AI on AMD infrastructure, Docker, pytest, CI. Repo: github.com/SebAustin/amd-routing-agent Demo: https://sebaustin-amd-routing-agent-demo.hf.space