The short answer

A routing system is valuable when its selection cost and occasional wrong choice are outweighed by savings on acceptable answers. Average token price alone does not describe that tradeoff.

The question and scope

This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate llm model routing in a documented setting. No measurements, participants or client outcomes are claimed here.

An illustrative test case

Most tasks are simple, but a small subset requires checking contradictory sources. A router needs to recognise when a cheap answer should be escalated and include that decision's own cost.

This scenario defines a case to examine; it is not an observed result.

A proposed investigation

Label a task set by difficulty and acceptance criteria. Compare one small model, one larger model and a predetermined router. Include routing latency, escalation and retries in total cost.

Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.

What to measure

  • Acceptable-answer rate.
  • cost per accepted task.
  • latency.
  • routing mistakes.

Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.

Interpretation and limitations

Do not choose the routing threshold on the final test set. Separate calibration tasks from held-out evaluation tasks.

Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.

A practical review checklist

  • Is the question narrow enough for the available system or dataset?
  • Are the comparison conditions genuinely comparable?
  • Can a reader trace each conclusion to an observation?
  • Are the boundaries and unresolved assumptions visible?
  • Does the decision change if the result is negative or inconclusive?

The publication artifact

The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.

Sources and further reading

These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.

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