Models supported by Azure AI Agent Service
Agents are powered by a diverse set of models with different capabilities and price points. Model availability varies by region and cloud. Certain tools and capabilities require the latest models. The following models are available in the available SDKs. The following table is for pay-as-you-go. For information on Provisioned Throughput Unit (PTU) availability, see provisioned throughput in the Azure OpenAI documentation. You can use global standard models if they're supported in the regions listed here.
Azure OpenAI models
Azure AI Agent Service supports the same models as the chat completions API in Azure OpenAI, in the following regions.
| Region | gpt-4o, 2024-05-13 | gpt-4o, 2024-08-06 | gpt-4o-mini, 2024-07-18 | gpt-4, 0613 | gpt-4, 1106-Preview | gpt-4, 0125-Preview | gpt-4, turbo-2024-04-09 | gpt-4-32k, 0613 | gpt-35-turbo, 0613 | gpt-35-turbo, 1106 | gpt-35-turbo, 0125 | gpt-35-turbo-16k, 0613 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| australiaeast | - | - | - | ✅ | ✅ | - | - | ✅ | ✅ | ✅ | ✅ | ✅ |
| eastus | ✅ | ✅ | ✅ | - | - | ✅ | ✅ | - | ✅ | - | ✅ | ✅ |
| eastus2 | ✅ | ✅ | ✅ | - | ✅ | - | ✅ | - | ✅ | - | ✅ | ✅ |
| francecentral | - | - | - | ✅ | ✅ | - | - | ✅ | ✅ | ✅ | - | ✅ |
| japaneast | - | - | - | - | - | - | - | - | ✅ | - | ✅ | ✅ |
| norwayeast | - | - | - | - | ✅ | - | - | - | - | - | - | - |
| southindia | - | - | - | - | ✅ | - | - | - | - | ✅ | ✅ | - |
| swedencentral | ✅ | ✅ | ✅ | ✅ | ✅ | - | ✅ | ✅ | ✅ | ✅ | - | ✅ |
| uksouth | - | - | - | - | ✅ | ✅ | - | - | ✅ | ✅ | ✅ | ✅ |
| westus | ✅ | ✅ | ✅ | - | ✅ | - | ✅ | - | - | ✅ | ✅ | - |
| westus3 | ✅ | ✅ | ✅ | - | ✅ | - | ✅ | - | - | - | ✅ | - |
Non-Microsoft models
The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
- Meta-Llama-405B-Instruct
- Mistral-large-2407
- Cohere-command-r-plus
- Cohere-command-r
To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference the deployment name in your agent. For example:
agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" )