Concept

What is model routing?

Model routing selects an AI model for each request. A rule or a predictive router decides which of the available models will answer.

Last updated: 24 October 2026 | Next review: November 2026 Technical definition with an illustrative MING workflow Machine-readable record

Illustrative · no measured results

Choose a model only after the facts are clear.

Prepare an internal project-status draft

One clear record

Model A

Must pass the status-extraction checks

Several consistent records

Model B

Must pass the synthesis checks

Dates conflict or an owner is missing

Ask a person

Resolve the source gap before routing

A colleague reviews either model’s draft before use.

Invented MING workflow. A and B are placeholders, not a ranking. The human clarification path is outside model routing; no results or savings were measured.

The model can change from one request to the next.

A team preparing project updates need not send every draft to the same model. A routing step can choose a candidate for each request before it answers. RouteLLM studies this selection using learned preferences; a cascade instead tries models in sequence.[S1] Choosing the route does not establish that the resulting answer is correct.

A missing project fact needs clarification.

Our invented workflow distinguishes one clear status record from several consistent records that need combining. Model A is a candidate for the first task; model B for the second. Both must first meet the relevant task requirements. The graphic shows proposed branches, not tested performance.[S3]

When records disagree on the review date, or omit the owner, a person resolves that gap before model selection. A more capable model cannot supply an absent project decision. A colleague still checks either model’s draft before use. This human path surrounds model routing; it is not another language model.

A router’s prediction still needs testing on your work.

A predictive router estimates which model will suit the request. Amazon Bedrock’s documentation makes two relevant limitations explicit: its intelligent prompt routing is optimized only for English and cannot adapt decisions using application-specific performance data.[S2] These are service-specific limits. They make testing the intended languages and exceptions necessary; they do not prove that every router fails on German.

Compare the routed workflow with a fixed, eligible model on the same permitted cases. Keep accepted drafts, incorrect routes, response time, actual cost and human correction together. The extra routing step is worthwhile only if the resulting trade-off helps the task. We have measured no saving or reliability here.

Start with our model-selection cases , then write which requests would take each route and who handles unresolved facts. That gives the router something concrete to be tested against.

Which requests in your team need different models?

Bring one recurring task and its exceptions. We can discuss which model paths are worth testing and where a person needs to clarify or approve the work.

Discuss my model routing

Tell us what you have in mind. We will respond personally.

Sources

[S1]
RouteLLM: Learning to Route LLMs with Preference Data Ong et al., UC Berkeley / Anyscale / Canva · 23 February 2025 (v4); read 24 October 2026 Supports: Per-request model selection before generation; distinction from sequential cascades Research paper, sections 2–3 and 5.5. No benchmark saving or current model ranking transferred to this page.
[S2]
Understanding intelligent prompt routing in Amazon Bedrock Amazon Web Services · Read 24 October 2026 Supports: Predicted response quality guides the model choice, English-only optimization; application-specific performance data does not adapt routing Current provider documentation; limitations belong to this service, not every router.
[S3]
Illustrative project-status routing policy MING Labs · 24 October 2026 Supports: Three proposed paths for ordinary work, more demanding synthesis and human clarification Original invented example. A and B are unnamed candidates, with no test results, client data or measured savings.

Frequently asked questions

Is model routing the same as choosing a model?
Model selection establishes which configurations are eligible for a task. Routing then chooses among those candidates for each incoming request. The candidates and the routing decisions both need evaluation.
Is trying a second model after a failed answer also routing?
It is usually described as a cascade or fallback. The routing approach explained here chooses a model before it generates an answer. A cascade may make several model calls, so account for all of them.
Does routing always reduce costs?
No. The result depends on the workload, models, routing effort, errors and human correction. Compare accepted work and total effort against a fixed-model baseline; a provider’s saving does not establish yours.
Does the router decide whether an output is correct?
Selecting a model is not acceptance of its output. Keep source checks, permissions and any required human approval in the surrounding workflow.
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