The strongest model is not automatically the best model for every request.
A product may need fast classification, image understanding, long-context reasoning, and high-quality generation. Sending every task to the most capable option can waste money and make simple interactions feel slow.
Route on Observable Features
Useful routing signals include task type, input size, modality, user tier, latency budget, and risk. Start with explicit rules before adding a learned router. Rules are easier to inspect and evaluate.
A typical strategy might use a fast model for extraction, a stronger model for ambiguous reasoning, and deterministic code when no generation is needed.
Design Fallbacks Carefully
Fallback is not always “try a bigger model.” An unavailable model may require queueing, a partial response, or asking the user to retry. Re-running a side-effectful agent with another model can duplicate actions unless the workflow is idempotent.
Evaluate the Router as a System
Measure quality, latency, and cost by route and task category. Also measure escalation rate: how often does the cheap path fail and invoke the expensive one?
The meaningful metric is cost per successful outcome. A low-cost model that retries repeatedly may be the expensive choice.
Multi-model routing creates leverage when task boundaries are clear. It creates chaos when every model receives a different prompt and nobody can reproduce which path produced a response. Version routing rules, record decisions on traces, and keep the first implementation simple.