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  "title": "What is model routing?",
  "hook": "Model routing selects an AI model for each request. A rule or a predictive router decides which of the available models will answer.",
  "summary": "Model routing selects an AI model for each request. A rule or a predictive router decides which of the available models will answer.",
  "pillar": "Hybrid Organisation",
  "author": {
    "name": "Marc Seefelder",
    "role": "Co-founder, MING Labs"
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  "datePublished": "2026-10-24",
  "dateModified": "2026-10-24",
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  "confidence": "B",
  "sources": [
    {
      "id": "S1",
      "title": "RouteLLM: Learning to Route LLMs with Preference Data",
      "publisher": "Ong et al., UC Berkeley / Anyscale / Canva",
      "date": "23 February 2025 (v4); read 24 October 2026",
      "url": "https://arxiv.org/html/2406.18665v4",
      "supports": [
        "Per-request model selection before generation; distinction from sequential cascades"
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      "method": "Research paper, sections 2–3 and 5.5. No benchmark saving or current model ranking transferred to this page."
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      "id": "S2",
      "title": "Understanding intelligent prompt routing in Amazon Bedrock",
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      "date": "Read 24 October 2026",
      "url": "https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html",
      "supports": [
        "Predicted response quality guides the model choice",
        "English-only optimization; application-specific performance data does not adapt routing"
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      "method": "Current provider documentation; limitations belong to this service, not every router."
    },
    {
      "id": "S3",
      "title": "Illustrative project-status routing policy",
      "publisher": "MING Labs",
      "date": "24 October 2026",
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        "Three proposed paths for ordinary work, more demanding synthesis and human clarification"
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      "method": "Original invented example. A and B are unnamed candidates, with no test results, client data or measured savings."
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      "q": "Is model routing the same as choosing a model?",
      "a": "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."
    },
    {
      "q": "Is trying a second model after a failed answer also routing?",
      "a": "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."
    },
    {
      "q": "Does routing always reduce costs?",
      "a": "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."
    },
    {
      "q": "Does the router decide whether an output is correct?",
      "a": "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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    "description": "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.",
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  "contentMarkdown": "---\ntitle: \"What is model routing? — MING Labs\"\ndescription: \"Model routing selects an AI model for each request. A rule or a predictive router decides which of the available models will answer.\"\ncanonical: https://www.minglabs.com/insights/concepts/what-is-model-routing\nlang: en\nlast-updated: 2026-10-24\n---\n\n# What is model routing?\n\n[Home](https://www.minglabs.com/)  [Insights](https://www.minglabs.com/insights)  [Concepts](https://www.minglabs.com/insights/concepts)  What is model routing?\n\n[English](https://www.minglabs.com/insights/concepts/what-is-model-routing)[Deutsch](https://www.minglabs.com/de/insights/concepts/was-ist-model-routing)\n\nConcept\n\n\n\nModel routing selects an AI model for each request. A rule or a predictive router decides which of the available models will answer.\n\nLast updated: 24 October 2026 | Next review: November 2026 Technical definition with an illustrative MING workflow [Machine-readable record ](https://www.minglabs.com/insights/concepts/what-is-model-routing.json)\n\nMarc Seefelder · Co-founder, MING Labs\n\nIllustrative · no measured results\n\nChoose a model only after the facts are clear.\n\nPrepare an internal project-status draft\n\nOne clear record\n\n**Model A**\n\nMust pass the status-extraction checks\n\nSeveral consistent records\n\n**Model B**\n\nMust pass the synthesis checks\n\nDates conflict or an owner is missing\n\n**Ask a person**\n\nResolve the source gap before routing\n\nA colleague reviews either model’s draft before use.\n\nInvented MING workflow. A and B are placeholders, not a ranking. The human clarification path is outside model routing; no results or savings were measured.\n\n##  The model can change from one request to the next.\n\nA 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\\]](https://www.minglabs.com/insights/concepts/what-is-model-routing#source-s1) Choosing the route does not establish that the resulting answer is correct.\n\n##  A missing project fact needs clarification.\n\nOur 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\\]](https://www.minglabs.com/insights/concepts/what-is-model-routing#source-s3)\n\nWhen 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.\n\n##  A router’s prediction still needs testing on your work.\n\nA 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\\]](https://www.minglabs.com/insights/concepts/what-is-model-routing#source-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.\n\nCompare 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.\n\nStart with our [model-selection cases](https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task) , then write which requests would take each route and who handles unresolved facts. That gives the router something concrete to be tested against.\n\n## Which requests in your team need different models?\n\nBring 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.\n\n[Discuss my model routing](https://www.minglabs.com/contact?type=project&topic=model-routing)\n\nTell us what you have in mind. We will respond personally.\n\n[Read the model-selection cases first ](https://www.minglabs.com/insights/answers/how-do-you-choose-an-ai-model-for-a-task)\n\n## Sources\n\n\\[S1\\]\n\n[RouteLLM: Learning to Route LLMs with Preference Data](https://arxiv.org/html/2406.18665v4) 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.\n\n\\[S2\\]\n\n[Understanding intelligent prompt routing in Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html) 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.\n\n\\[S3\\]\n\nIllustrative 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.\n\n## Frequently asked questions\n\nIs model routing the same as choosing a model?\n\nModel 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.\n\nIs trying a second model after a failed answer also routing?\n\nIt 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.\n\nDoes routing always reduce costs?\n\nNo. 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.\n\nDoes the router decide whether an output is correct?\n\nSelecting a model is not acceptance of its output. Keep source checks, permissions and any required human approval in the surrounding workflow.\n\n## Available actions\n\n- **Copy content for my AI**: copies the [AI briefing](https://www.minglabs.com/insights/concepts/what-is-model-routing.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/insights/concepts/what-is-model-routing\nMachine-readable index: https://www.minglabs.com/llms.txt\n"
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