Test an AI Model Router Before Switching Your Workflow

Make the router earn the switch

Test a candidate model router against a well-configured single-model baseline using the same tasks and acceptance checks. Count the full cost of verified work. This process is useful when a team is tempted to route requests based only on prompt length, model price or a successful demonstration.

This is a proposed evaluation workflow. DiscoverAI has not run the experiment described here. Start with the router pilot CSV; no account is required.

1. Define an accepted task before a cheap call

For extraction, acceptance might mean every required field matches the permitted source. For code work, it might mean passing relevant checks and human review of the changed behavior. Write down a concrete failure that matters, such as a missing field or an incorrect state transition.

Include review time in the comparison. An answer that looks complete but needs manual repair is unfinished work. Keep failed tasks in the record rather than dropping them from an average.

2. Keep development and held-out cases separate

Collect permitted examples of routine work, difficult work and short prompts with hidden complexity. Remove sensitive information while preserving what makes each case difficult. Mark which tasks may be used to tune routing and reserve others for evaluation.

Freeze the routing rules before running the reserved set. If you change a rule after seeing a failure, evaluate it on a fresh reserved set before calling the change an improvement. Otherwise you may only be measuring familiarity with your examples.

3. Compare the simplest credible baseline

Run the same inputs through the current configuration and candidate. Where feasible, include a lighter single-model setup too. Keep tool access, source material and acceptance criteria comparable. Record the date and configuration so later results have context.

Google’s November 3 experiment found that reducing an output ceiling did not produce the intended reasoning savings. Its small test also showed that caching and model switching complicate comparisons. Those findings motivate testing your own configuration; they do not establish your results.

4. Use a cost table that includes failures

Calculate total recorded workflow cost divided by accepted tasks, while also reporting failures and sample size. If there are zero accepted tasks, mark the ratio undefined rather than hiding the failure behind a low bill. Repeat borderline cases before interpreting a small difference.

5. Check fallback before enabling the candidate

Decide what happens when the classifier is unavailable or the selected model returns an unusable answer. Test that fallback with disposable inputs. Keep the original configuration available and define a stop condition before moving real work.

A candidate is ready for a wider pilot only when quality is acceptable, the cost difference survives complete accounting and someone owns the failure review. Avoid relying on one unusually favorable run.

Keep the decision reviewable

Save inputs, outputs and acceptance notes where authorized. The worksheet is a log, not a benchmark certification. Read the news analysis for the reported experiment’s boundaries, and use the buyer matrix to compare review effort alongside price.