Use model router for Microsoft Foundry

Note

This document refers to the Microsoft Foundry (classic) portal.

🔄 Switch to the Microsoft Foundry (new) documentation if you're using the new portal.

Model router for Microsoft Foundry is a deployable AI chat model that selects the best large language model (LLM) to respond to a prompt in real time. It uses different preexisting models to deliver high performance and save on compute costs, all in one model deployment. To learn more about how model router works, its advantages, and limitations, see the Model router concepts guide.

Use model router through the Chat Completions API like you'd use a single base model such as GPT-4. Follow the same steps as in the Chat completions guide.

Tip

The Microsoft Foundry (new) portal offers enhanced configuration options for model router. Switch to the Microsoft Foundry (new) documentation to see the latest features.

Deploy a model router model

Model router is packaged as a single Foundry model that you deploy. Start by following the steps in the resource deployment guide.

In the Create new deployment, find model-router in the Models list and select it.

Note

Your deployment settings apply to all underlying chat models that model router uses.

• Don't deploy the underlying chat models separately. Model router works independently of your other deployed models.
• Select a content filter when you deploy the model router model or apply a filter later. The content filter applies to all content passed to and from the model router; don't set content filters for each underlying chat model.
• Your tokens-per-minute rate limit setting applies to all activity to and from the model router; don't set rate limits for each underlying chat model.## Use model router in chats

Test model router with the Completions API

You can use model router through the chat completions API in the same way you'd use other OpenAI chat models. Set the model parameter to the name of our model router deployment, and set the messages parameter to the messages you want to send to the model.

Test model router in the playground

In the Foundry portal, go to your model router deployment on the Models + endpoints page and select it to open the model playground. In the playground, enter messages and see the model's responses. Each response shows which underlying model the router selected.

Important

You can set the Temperature and Top_P parameters to the values you prefer (see the concepts guide), but note that reasoning models (o-series) don't support these parameters. If model router selects a reasoning model for your prompt, it ignores the Temperature and Top_P input parameters.

The parameters stop, presence_penalty, frequency_penalty, logit_bias, and logprobs are similarly dropped for o-series models but used otherwise.

Important

Starting with the 2025-11-18 version, the reasoning_effort parameter (see the Reasoning models guide) is now supported in model router. If the model router selects a reasoning model for your prompt, it will use your reasoning_effort input value with the underlying model.

Output format

The JSON response you receive from a model router model is identical to the standard chat completions API response. Note that the "model" field reveals which underlying model was selected to respond to the prompt.

{
  "choices": [
    {
      "content_filter_results": {
        "hate": {
          "filtered": "False",
          "severity": "safe"
        },
        "protected_material_code": {
          "detected": "False",
          "filtered": "False"
        },
        "protected_material_text": {
          "detected": "False",
          "filtered": "False"
        },
        "self_harm": {
          "filtered": "False",
          "severity": "safe"
        },
        "sexual": {
          "filtered": "False",
          "severity": "safe"
        },
        "violence": {
          "filtered": "False",
          "severity": "safe"
        }
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": "None",
      "message": {
        "content": "I'm doing well, thank you! How can I assist you today?",
        "refusal": "None",
        "role": "assistant"
      }
    }
  ],
  "created": 1745308617,
  "id": "xxxx-yyyy-zzzz",
  "model": "gpt-4.1-nano-2025-04-14",
  "object": "chat.completion",
  "prompt_filter_results": [
    {
      "content_filter_results": {
        "hate": {
          "filtered": "False",
          "severity": "safe"
        },
        "jailbreak": {
          "detected": "False",
          "filtered": "False"
        },
        "self_harm": {
          "filtered": "False",
          "severity": "safe"
        },
        "sexual": {
          "filtered": "False",
          "severity": "safe"
        },
        "violence": {
          "filtered": "False",
          "severity": "safe"
        }
      },
      "prompt_index": 0
    }
  ],
  "system_fingerprint": "xxxx",
  "usage": {
    "completion_tokens": 15,
    "completion_tokens_details": {
      "accepted_prediction_tokens": 0,
      "audio_tokens": 0,
      "reasoning_tokens": 0,
      "rejected_prediction_tokens": 0
    },
    "prompt_tokens": 21,
    "prompt_tokens_details": {
      "audio_tokens": 0,
      "cached_tokens": 0
    },
    "total_tokens": 36
  }
}

Monitor model router metrics

Monitor performance

Monitor the performance of your model router deployment in Azure Monitor (AzMon) in the Azure portal.

1. Go to the Monitoring > Metrics page for your Azure OpenAI resource in the Azure portal.
2. Filter by the deployment name of your model router model.
3. Split the metrics by underlying models if needed.

Monitor costs

You can monitor the costs of model router, which is the sum of the costs incurred by the underlying models.

1. Visit the Resource Management -> Cost analysis page in the Azure portal.
2. If needed, filter by Azure resource.
3. Then, filter by deployment name: Filter by "Tag", select Deployment as the type of the tag, and then select your model router deployment name as the value.