Route Easy Tasks to Cheaper Models, Safely

Sending every request to your strongest model is simple, but it treats formatting a response like debugging a production incident. A better approach routes bounded tasks to a lower-cost model and reserves more expensive reasoning for work that needs it. The engineering challenge is deciding what counts as easy, then detecting when that decision was wrong.

1. Define tasks, not vague difficulty scores

Start with a small catalog of operations your application already performs. Good candidates include extracting explicit fields, assigning predefined labels, and rewriting text under narrow constraints. Keep architectural decisions, ambiguous troubleshooting, and consequential recommendations outside the initial rollout.

Give each task an explicit contract: required inputs, permitted outputs, validation rules, and conditions that require escalation. A short request is not necessarily easy. Missing context, conflicting instructions, and irreversible consequences matter more than character count.

2. Establish a baseline before changing routes

Collect representative requests and their accepted outputs from your existing workflow, removing sensitive data where necessary. Include ordinary cases, malformed inputs, ambiguous examples, and cases that previously needed human correction. Separate development examples from a held-out evaluation set.

Record baseline quality, latency, token usage, and actual billed cost. Define acceptance criteria by task: exact field matches for extraction, valid labels for classification, and a review rubric for rewriting. Avoid reducing everything to one aggregate score that hides an important failure category.

3. Select a cheaper candidate using actual rates

Compare the rates available to your account rather than assuming that a model name guarantees lower cost. For example, you might evaluate Claude Haiku 4.5 for bounded tasks while retaining Claude Sonnet 5.5 as an escalation candidate, if your measured quality and effective pricing support that pairing.

CodeRelay Hub provides one endpoint across model families, so your routing layer can sit above a shared API integration. Keep model identifiers and route settings in configuration. Verify each selected model's supported parameters, output constraints, and tool behavior instead of assuming complete interchangeability.

4. Begin with deterministic routing rules

Do not start by asking another model to judge every request. For an initial implementation, route using metadata your application already knows: operation name, input size, required tools, and risk category. This makes decisions inspectable and avoids adding a classification call to every request.

• Lower-cost route: supported task, complete inputs, bounded output, and no prohibited risk flags.
• Escalation route: ambiguous requirements, unsupported capabilities, or dependencies that need broader context.
• Human review: actions where model output alone is insufficient authorization.

Keep these rules server-side. Treat user content as task data, not as permission to override routing or safety controls.

5. Validate outputs and cap escalation

Check outputs before accepting them. Validate structure, required fields, allowed labels, and task-specific invariants. For extraction, confirm that quoted evidence appears in the source. A response that parses successfully can still be semantically wrong.

Escalate when a check fails, carrying forward the original input and a concise validation report. Do not rely on the cheaper model's self-reported confidence as your only gate. Limit escalation attempts and enforce a request budget so retries cannot quietly erase the expected savings.

6. Measure the complete request path

Count every attempt when calculating cost per accepted result, including failed calls, escalation, and any validation calls. Track end-to-end latency as well as individual model latency. A fast first attempt is not useful if frequent fallbacks make the overall experience slower.

Log the task type, route, model, configuration version, validation outcome, usage, and final disposition. Compare cohorts by task and input characteristics. When savings disappear, these records help distinguish a poor route boundary from excessive context, weak prompts, or repeated retries.

7. Roll out gradually and retain an override

Start with one low-risk task behind a feature flag. Shadow-test where appropriate, then enable a limited production cohort. Review sampled accepted outputs, not just rejected ones, because validators will miss some errors. Expand only when held-out evaluations and production review support the change.

Keep a server-side override that restores the baseline route without a deployment. Re-evaluate after model, prompt, or workload changes. Successful routing is a maintained policy, not a one-time choice of the cheapest model.

Open a free CodeRelay Hub workspace to try task-based routing.