Learned compact router
Route locally.
Spend only when needed.
Watch a learned router make a zero-token decision in this browser, compare it with a paid prompt-based decision, and inspect provider-reported Fireworks usage in live mode.
See the decision cost.
Each run reveals the local route first, then performs the paid baseline classification, and finally returns an answer with exact provider usage when live.
Prompt-based baseline
Waiting
The paid comparison starts after the local decision.
Answer stage
WAITING
The answer appears after both routing decisions are visible.
Run a query to compare
Both bars share the same answer usage, isolating the cost of paying an LLM to route.
Learn once. Route free.
The router is trained on empirical local-model outcomes. At judge time it spends zero Fireworks tokens and keeps deterministic verification as a second quality gate.
Score locally
The learned classifier estimates whether the local pipeline will succeed.
Generate locally
A quantized Qwen model handles the zero-token path inside the container.
Verify
Arithmetic, code, logic, and output constraints are checked deterministically.
Escalate
An allowed Fireworks model repairs the small subset that needs more capability.
Measured under the real limits.
The new image is accepted only after a disjoint accuracy check, 2-vCPU timing rehearsal, router class metrics, and provider-reported token comparison against the previous 9,685-token run.
Inspect the benchmark code →