FrugalRouter

FrugalRouter treats API tokens as the scarcest resource. Instead of relying on a frontier model, we LoRA-fine-tuned Qwen3-1.7B on 2,708 machine-validated examples so it answers factual, math, sentiment, summary, NER, logic, and code tasks in a single generation — entirely inside the container on 2 CPU cores. Every local answer is verified deterministically, never trusted: math answers are Python programs we execute; code is behaviorally fingerprinted across samples; formats are programmatically enforced against each task's stated constraints; and purpose-trained ONNX models (23–126 MB) handle routing, NER, and sentiment for zero tokens. Only the categories a 1.7B provably can't self-verify escalate to Fireworks — batched, format-hinted, under a hard token budget. The result: 84.2% accuracy on the hidden evaluation set at 3,388 tokens, validated at 19/19 and 10/10 on grader-spec hardware, with a watchdog architecture that has never timed out or shipped invalid JSON. Even with zero API access it scores ~89% locally. Small model, big verification, surgical spending.