@@ -5,7 +5,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 01/10/2024
+ms.date: 01/29/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -24,14 +24,12 @@ This article is to help you understand the support lifecycle for the Azure OpenA
 
 Azure OpenAI API latest release:
 
-- Inference: [2024-12-01-preview](reference-preview.md)
-- Authoring: [2024-10-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring/preview/2024-10-01-preview/azureopenai.json)
-
-> [!IMPORTANT]
-> For features that are part of the dataplane authoring API such as batch, fine-tuning, and assistants files, continue to use API version `2024-10-01-preview` to take advantage of the latest preview features.
+- Inference: [2025-01-01-preview](reference-preview.md)
+- Authoring: [2025-01-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring/preview/2025-01-01-preview/azureopenai.json)
 
 This version contains support for the latest Azure OpenAI features including:
 
+- [Predicted Outputs](./how-to/predicted-outputs.md) [**Added in 2025-01-01-preview**]
 - [Reasoning models](./how-to/reasoning.md) [**Added in 2024-12-01-preview**]
 - [Stored completions/distillation](./how-to/stored-completions.md) [**Added in 2024-12-01-preview**]
 - Assistants V2 [**Added in 2024-05-01-preview**]
@@ -44,6 +42,11 @@ This version contains support for the latest Azure OpenAI features including:
 - [Function calling](./how-to/function-calling.md)  [**Added in 2023-07-01-preview**]
 - [Retrieval augmented generation with your data feature](./use-your-data-quickstart.md).  [**Added in 2023-06-01-preview**]
 
+## Changes between 2025-01-01-preview and 2024-12_01_preview
+
+- `prediction` parameter added for [predicted outputs](./how-to/predicted-outputs.md) support.
+- `gpt-4o-audio-preview` [model support](./audio-completions-quickstart.md).
+
 ## Changes between 2024-12-01-preview and 2024-10-01-preview
 
 - `store`, and `metadata` parameters added for [stored completions support](./how-to/stored-completions.md).

@@ -6,20 +6,20 @@ author: challenp
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom: references_regions, azuregovernment
-ms.date: 12/11/2024
+ms.date: 1/29/2025
 recommendations: false
 ---
 
 # Azure OpenAI Service and features in Azure Government
 
-This article highlights the differences when using Azure OpenAI in Azure Government as compared to the commercial cloud offering. Learn more about the Azure OpenAI Service itself in [Azure OpenAI Service documentation](/azure/ai-services/openai/).
+This article highlights the differences when using Azure OpenAI in Azure Government as compared to the commercial cloud offering. If not specified, the Azure OpenAI model or feature should be assumed to be not available in the Azure Government environment. Learn more about the Azure OpenAI Service itself in [Azure OpenAI Service documentation](/azure/ai-services/openai/).
 <br><br>
 
 ## Azure OpenAI models
 
-Learn more about the different capabilities of each model in [Azure OpenAI Service models](./concepts/models.md). For customers with [Business Continuity and Disaster Recovery (BCDR) considerations](./how-to/business-continuity-disaster-recovery.md), please take careful note of the deployment types, regions, and model availability below as not all model/type combinations are available in both regions. 
+Learn more about the different capabilities of each model in [Azure OpenAI Service models](./concepts/models.md). For customers with [Business Continuity and Disaster Recovery (BCDR) considerations](./how-to/business-continuity-disaster-recovery.md), take careful note of the deployment types, regions, and model availability as not all model/type combinations are available in both regions. 
 
-The following sections show model availability by region and deployment type.
+The following sections show model availability by region and deployment type. Models and versions not listed are not currently available in Azure Government. 
 
 <br>
 
@@ -29,7 +29,7 @@ The following sections show model availability by region and deployment type.
 | usgovarizona  | ✅ | ✅ | ✅ | ✅ | -  | ✅ | ✅ |
 | usgovvirginia | ✅ | -  | ✅ | ✅ | ✅ |  - | ✅ |
 
-To request quota increases for these models, submit a request at [https://aka.ms/AOAIGovQuota](https://aka.ms/AOAIGovQuota). Please note the following maximum quota limits that will be granted via that form:
+To request quota increases for these models, submit a request at [https://aka.ms/AOAIGovQuota](https://aka.ms/AOAIGovQuota). Note the following maximum quota limits allowed via that form:
 
 | **gpt-4o** | **gpt-4o-mini** | **gpt-4** | **gpt-35-turbo** | **text-embedding-3-large** | **text-embedding-ada-002**|
 |:----------:|:---------------:|:---------:|:----------------:|:--------------------------:|:-------------------------:|
@@ -95,10 +95,10 @@ From the Manage Commitments view, you can do several things:
 | **Renewal settings** | Autorenew at current PTUs <br> Autorenew at lower PTUs <br> Do not autorenew |
 
 > [!IMPORTANT]
-> A new commitment is billed up-front for the entire term.  If the renewal settings are set to auto-renew, then you will be billed again on each renewal date based on the renewal settings.
+> A new commitment is billed up-front for the entire term. If the renewal settings are set to auto-renew, then you will be billed again on each renewal date based on the renewal settings.
 
 > [!IMPORTANT]
-> When you add PTUs to a commitment, they will be billed immediately, at a pro-rated amount from the current date to the end of the existing commitment term.  Adding PTUs does not reset the commitment term.
+> When you add PTUs to a commitment, they will be billed immediately, at a pro-rated amount from the current date to the end of the existing commitment term. Adding PTUs does not reset the commitment term.
 
 ### Changing renewal settings
 

@@ -80,6 +80,8 @@ For more information on the model evaluation process, see the [Getting started w
 
 For information on the model upgrade process, see [How to upgrade to a new model or version](./model-versions.md).
 
+For more information on how to manage model upgrades and migrations for provisioned deployments, see [Managing models on provisioned deployment types](../how-to/working-with-models.md#managing-models-on-provisioned-deployment-types)
+
 ## Current models
 
 > [!NOTE]

@@ -19,6 +19,9 @@ Azure OpenAI Service is committed to providing the best generative AI models for
 
 We want to make it easy for customers to stay up to date as models improve. Customers can choose to start with a particular version and to automatically update as new versions are released.
 
+> [!NOTE]
+> The following upgrade guidance only applies to Standard deployment types. For guidance on updating or migrating provisioned deployment types, review the [model management documentation](../how-to/working-with-models.md).
+
 When you deploy a model you can choose an update policy, which can include the following options:
 
 * Deployments set to **Auto-update to default** automatically update to use the new default version.

@@ -6,7 +6,7 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom: references_regions
-ms.date: 11/10/2024
+ms.date: 01/29/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -30,7 +30,11 @@ Azure OpenAI evaluation enables developers to create evaluation runs to test aga
 ### Supported deployment types
 
 - Standard
-- Provisioned
+- Global standard
+- Data zone standard
+- Provisioned-managed
+- Global provisioned-managed
+- Data zone provisioned-managed
 
 ## Evaluation pipeline
 
@@ -73,13 +77,13 @@ When you upload and select you evaluation file a preview of the first three line
 
 You can choose any existing previously uploaded datasets, or upload a new dataset.
 
-### Generate responses (optional)
+### Create responses (optional)
 
 The prompt you use in your evaluation should match the prompt you plan to use in production. These prompts provide the instructions for the model to follow. Similar to the playground experiences, you can create multiple inputs to include few-shot examples in your prompt. For more information, see [prompt engineering techniques](../concepts/advanced-prompt-engineering.md) for details on some advanced techniques in prompt design and prompt engineering.
 
 You can reference your input data within the prompts by using the `{{input.column_name}}` format, where column_name corresponds to the names of the columns in your input file.
 
-Outputs generated during the evaluation will be referenced in subsequent steps using the `{{sample.output_text}}` format. 
+Outputs generated during the evaluation will be referenced in subsequent steps using the `{{sample.output_text}}` format.
 
 > [!NOTE]
 > You need to use double curly braces to make sure you reference to your data correctly.
@@ -88,9 +92,9 @@ Outputs generated during the evaluation will be referenced in subsequent steps u
 
 As part of creating evaluations you'll pick which models to use when generating responses (optional) as well as which models to use when grading models with specific testing criteria.  
 
-In Azure OpenAI you'll be assigning specific model deployments to use as part of your evaluations. You can compare multiple deployments by creating a separate evaluation configuration for each model. This enables you to define specific prompts for each evaluation, providing better control over the variations required by different models.
+In Azure OpenAI you'll be assigning specific model deployments to use as part of your evaluations. You can compare multiple model deployments in single evaluation run.
 
-You can evaluate either a base or a fine-tuned model deployment. The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
+You can evaluate either base or fine-tuned model deployments. The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
 
 ### Testing criteria
 
@@ -105,7 +109,7 @@ Testing criteria is used to assess the effectiveness of each output generated by
 
     :::image type="content" source="../media/how-to/evaluations/new-evaluation.png" alt-text="Screenshot of the Azure OpenAI evaluation UX with new evaluation selected." lightbox="../media/how-to/evaluations/new-evaluation.png":::
 
-3. Enter a name of your evaluation. By default a random name is automatically generated unless you edit and replace it. > select **Upload new dataset**.
+3. Enter a name of your evaluation. By default a random name is automatically generated unless you edit and replace it. Select **Upload new dataset**.
 
     :::image type="content" source="../media/how-to/evaluations/upload.png" alt-text="Screenshot of the Azure OpenAI upload UX." lightbox="../media/how-to/evaluations/upload.png":::
 
@@ -128,7 +132,7 @@ Testing criteria is used to assess the effectiveness of each output generated by
 
     :::image type="content" source="../media/how-to/evaluations/preview.png" alt-text="Screenshot that shows a preview of an uploaded evaluation file." lightbox="../media/how-to/evaluations/preview.png":::
 
-5. Select the toggle for **Generate responses**. Select `{{item.input}}` from the dropdown. This will inject the input fields from our evaluation file into individual prompts for a new model run that we want to able to compare against our evaluation dataset. The model will take that input and generate its own unique outputs which in this case will be stored in a variable called `{{sample.output_text}}`. We'll then use that sample output text later as part of our testing criteria. Alternatively you could provide your own custom system message and individual message examples manually.
+5. Under **Responses** select the **Create** button. Select `{{item.input}}` from the **Create with a template** dropdown. This will inject the input fields from our evaluation file into individual prompts for a new model run that we want to able to compare against our evaluation dataset. The model will take that input and generate its own unique outputs which in this case will be stored in a variable called `{{sample.output_text}}`. We'll then use that sample output text later as part of our testing criteria. Alternatively you could provide your own custom system message and individual message examples manually.
 
 6. Select which model you want to generate responses based on your evaluation. If you don't have a model you can create one. For the purpose of this example we're using a standard deployment of `gpt-4o-mini`.
 

@@ -19,7 +19,6 @@ Predicted outputs can improve model response latency for chat completions calls
 ## Model support
 
 - `gpt-4o-mini` version: `2024-07-18`
-- `gpt-4o` version: `2024-05-13`
 - `gpt-4o` version: `2024-08-06`
 - `gpt-4o` version: `2024-11-20`
 

@@ -6,7 +6,7 @@ manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom: references_regions
-ms.date: 01/21/2025
+ms.date: 01/29/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -20,17 +20,15 @@ Stored completions allow you to capture the conversation history from chat compl
 
 ### API support
 
-- `2024-10-01-preview`
+Support first added in `2024-10-01-preview`
 
-### Model support
+### Model & region availability
 
-- `gpt-4o-2024-08-06`
-
-### Regional availability
-
-- Sweden Central
-- North Central US
-- East US2
+| **Region**     | **o1-preview**, **2024-09-12**   | **o1-mini**, **2024-09-12**   | **gpt-4o**, **2024-08-06**   | **gpt-4o**, **2024-05-13**  | **gpt-4o-mini**, **2024-07-18**   |
+|:---|:---:|:---:|:---:|:---:|:---:|
+| Sweden Central | ✅ | ✅  | ✅ | ✅ | ✅ |
+| North Central US | - | - | ✅ | - | - |
+| East US 2 | - | - | ✅ | - | - |
 
 ## Configure stored completions
 

@@ -6,18 +6,21 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 12/18/2024
+ms.date: 01/30/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
 ---
 
 # Structured outputs
 
-Structured outputs make a model follow a [JSON Schema](https://json-schema.org/overview/what-is-jsonschema) definition that you provide as part of your inference API call. This is in contrast to the older [JSON mode](./json-mode.md) feature, which guaranteed valid JSON would be generated, but was unable to ensure strict adherence to the supplied schema. Structured outputs is recommended for function calling, extracting structured data, and building complex multi-step workflows.
+Structured outputs make a model follow a [JSON Schema](https://json-schema.org/overview/what-is-jsonschema) definition that you provide as part of your inference API call. This is in contrast to the older [JSON mode](./json-mode.md) feature, which guaranteed valid JSON would be generated, but was unable to ensure strict adherence to the supplied schema. Structured outputs are recommended for function calling, extracting structured data, and building complex multi-step workflows.
 
 > [!NOTE]
-> * Currently structured outputs is not supported on [bring your own data](../concepts/use-your-data.md) scenario.
+> Currently structured outputs are not supported with:
+> - [Bring your own data](../concepts/use-your-data.md) scenarios.
+> - [Assistants](../how-to/assistant.md) or [Azure AI Agents Service](../../agents/overview.md).
+> - `gpt-4o-audio-preview` version: `2024-12-17`.
 
 ## Supported models
 
@@ -280,7 +283,7 @@ Output:
 Structured Outputs for function calling can be enabled with a single parameter, by supplying `strict: true`. 
 
 > [!NOTE]
-> Structured outputs is not supported with parallel function calls. When using structured outputs set `parallel_tool_calls` to `false`.
+> Structured outputs are not supported with parallel function calls. When using structured outputs set `parallel_tool_calls` to `false`.
 
 # [Python (Microsoft Entra ID)](#tab/python-secure)
 

@@ -26,6 +26,9 @@ Azure OpenAI now supports automatic updates for select model deployments. On mod
 
 You can learn more about Azure OpenAI model versions and how they work in the [Azure OpenAI model versions](../concepts/model-versions.md) article.
 
+> [!NOTE]
+> Automatic model updates are only supported for Standard deployment types. For more information on how to manage model updates and migrations on provisioned deployment types, refer to the section on [managing models on provisioned deployment types](./working-with-models.md#managing-models-on-provisioned-deployment-types)
+
 ### Auto update to default
 
 When you set your deployment to **Auto-update to default**, your model deployment is automatically updated within two weeks of a change in the default version.  For a preview version, it updates automatically when a new preview version is available starting two weeks after the new preview version is released.
@@ -280,6 +283,90 @@ curl -X PUT https://management.azure.com/subscriptions/00000000-0000-0000-0000-0
   "etag": "\"GUID\""
 }
 ```
+## Managing models on provisioned deployment types
+Provisioned deployments support distinct model management practices. Provisioned deployment model management practices are intended to give you the greatest control over when and how you migrate between model versions and model families. Currently, there are two approaches available to manage models on provisioned deployments: (1) in-place migrations and (2) multi-deployment migrations.
+
+### Prerequisites
+- Validate that the target model version or model family is supported for your existing deployment type. Migrations can only occur between provisioned deployments of the same deployment type. For more information on deployment types, review the [deployment type documentation](./deployment-types.md).
+- Validate capacity availability for your target model version or model family prior to attempting a migration. For more information on determining capacity availability, review the [capacity transparency documentation](../concepts/provisioned-throughput.md#capacity-transparency).
+- For multi-deployment migrations, validate that you have sufficient quota to support multiple deployments simultaneously. For more information on how to validate quota for each provisioned deployment type, review the [provisioned quota documentation](../concepts/provisioned-throughput.md#quota).
+
+### In-place migrations for provisioned deployments
+In-place migrations allow you to maintain the same provisioned deployment name and size while changing the model version or model family assigned to that deployment. With in-place migrations, Azure OpenAI Service takes care of migrating any existing traffic between model versions or model families throughout the migration over a 20-30 minute window. Throughout the migration window, your provisioned deployment will display an "updating" provisioned state. You can continue to use your provisioned deployment as you normally would. Once the in-place migration is complete, the provisioned state will be updated to "succeeded", indicating that all traffic has been migrated over to the target model version or model family. 
+
+#### In-place migration: model version update
+In-place migrations that target updating an existing provisioned deployment to a new model version within the same model family are supported through Azure AI Foundry, REST API, and Azure CLI. To perform an in-place migration targeting a model version update within Azure AI Foundry, select **Deployments** > under the deployment name column select the deployment name of the provisioned deployment you would like to migrate.
+
+Selecting a deployment name opens the **Properties** for the model deployment. From this view, select the **edit** button, which will show the **Update deployment** dialogue box. Select the model version dropdown to set a new model version for the provisioned deployment. As noted, the provisioning state will change to "updating" during the migration and will revert to "succeeded" once the migration is complete. 
+
+![Screenshot of update deployment dialogue box with the model version field selector opened to show model version options available for selection.](media/working-with-models/provisioned-deployment-model-version-update.png)
+
+#### In-place migration: model family change
+In-place migration that target updating an existing provisioned deployment to a new model family are supported through REST API and Azure CLI. To perform an in-place migration targeting a model family change, use the example request below as a guide. In the request, you will need to update the model name and model version for the target model you are migrating to. 
+
+```Bash
+curl -X PUT https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/resource-group-temp/providers/Microsoft.CognitiveServices/accounts/docs-openai-test-001/deployments/gpt-4o-ptu-deployment?api-version=2024-10-01 \
+  -H "Content-Type: application/json" \
+  -H 'Authorization: Bearer YOUR_AUTH_TOKEN' \
+  -d '{"sku":{"name":"GlobalProvisionedManaged","capacity":100},"properties": {"model": {"format": "OpenAI","name": "gpt-4o-mini","version": "2024-07-18"}}}'
+```
+#### Example response
+
+```json
+ {
+  "id": "/subscriptions/{subscription-id}/resourceGroups/resource-group-temp/providers/Microsoft.CognitiveServices/accounts/docs-openai-test-001/deployments/gpt-4o-ptu-deployment",
+  "type": "Microsoft.CognitiveServices/accounts/deployments",
+  "name": "gpt-4o-ptu-deployment",
+  "sku": {
+    "name": "GlobalProvisionedManaged",
+    "capacity": 100
+  },
+  "properties": {
+    "model": {
+      "format": "OpenAI",
+      "name": "gpt-4o-mini",
+      "version": "2024-07-18"
+    },
+    "versionUpgradeOption": "OnceCurrentVersionExpired",
+    "currentCapacity": 100
+    "capabilities": {
+      "area": "EUR",
+      "chatCompletion": "true"
+      "jsonObjectResponse": "true",
+      "maxContextToken": "128000",
+      "maxOutputToken": "16834",
+      "assistants": "true"
+    },
+    "provisioningState": "Updating",
+    "rateLimits": [
+      {
+        "key": "request",
+        "renewalPeriod": 10,
+        "count": 300
+      }
+    ]
+  },
+  "systemData": {
+    "createdBy": "docs@contoso.com",
+    "createdByType": "User",
+    "createdAt": "2025-01-28T02:57:15.8951706Z",
+    "lastModifiedBy": "docs@contoso.com",
+    "lastModifiedByType": "User",
+    "lastModifiedAt": "2025-01-29T15:35:53.082912Z"
+  },
+  "etag": "\"GUID\""
+}
+```
+
+> [!NOTE]
+> There are multiple ways to generate an authorization token. The easiest method for initial testing is to launch the Cloud Shell from the [Azure portal](https://portal.azure.com). Then run [`az account get-access-token`](/cli/azure/account?view=azure-cli-latest#az-account-get-access-token&preserve-view=true). You can use this token as your temporary authorization token for API testing.
+
+### Multi-deployment migrations for provisioned deployments
+Multi-deployment migrations allow you to have greater control over the model migration process. With multi-deployment migrations, you can dictate how quickly you would like to migrate your existing traffic to the target model version or model family on a new provisioned deployment. The process to migrate to a new model version or model family using the multi-deployment migration approach is as follows:
+- Create a new provisioned deployment. For this new deployment, you can choose to maintain the same provisioned deployment type as your existing deployment or select a new deployment type if desired.
+- Transition traffic from the existing provisioned deployment to the newly created provisioned deployment with your target model version or model family until all traffic is offloaded from the original deployment. 
+- Once traffic is migrated over to the new deployment, validate that there are no inference requests being processed on the previous provisioned deployment by ensuring the Azure OpenAI Requests metric does not show any API calls made within 5-10 minutes of the inference traffic being migrated over to the new deployment. For more information on this metric, [see the Monitor Azure OpenAI documentation](https://aka.ms/aoai/docs/monitor-azure-openai).
+- Once you confirm that no inference calls have been made, delete the original provisioned deployment.
 
 ## Next steps
 

@@ -6292,7 +6292,7 @@ Represents an `assistant` that can call the model and use tools.
 | created_at | integer | The Unix timestamp (in seconds) for when the assistant was created. | Yes |  |
 | name | string | The name of the assistant. The maximum length is 256 characters.<br> | Yes |  |
 | description | string | The description of the assistant. The maximum length is 512 characters.<br> | Yes |  |
-| model | string | ID of the model to use. You can use the List models API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.<br> | Yes |  |
+| model | string | ID of the model to use. You can use the List models API to see all of your available models, or see our [Model overview](../../concepts/models.md) for descriptions of them.<br> | Yes |  |
 | instructions | string | The system instructions that the assistant uses. The maximum length is 256,000 characters.<br> | Yes |  |
 | tools | array | A list of tool enabled on the assistant. There can be a maximum of 128 tools per assistant. Tools can be of types `code_interpreter`, `file_search`, or `function`.<br> | Yes | [] |
 | tool_resources | object | A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. For example, the `code_interpreter` tool requires a list of file IDs, while the `file_search` tool requires a list of vector store IDs.<br> | No |  |
@@ -7343,7 +7343,7 @@ Represents a step in execution of a run.
 | Name | Type | Description | Required | Default |
 |------|------|-------------|----------|---------|
 | id | string | The identifier of the run step, which can be referenced in API endpoints. | Yes |  |
-| object | string | The object type, which is always `assistant.run.step``. | Yes |  |
+| object | string | The object type, which is always `assistant.run.step`. | Yes |  |
 | created_at | integer | The Unix timestamp (in seconds) for when the run step was created. | Yes |  |
 | assistant_id | string | The ID of the assistant associated with the run step. | Yes |  |
 | thread_id | string | The ID of the thread that was run. | Yes |  |
@@ -7451,7 +7451,7 @@ Details of the message creation by the run step.
 
 | Name | Type | Description | Required | Default |
 |------|------|-------------|----------|---------|
-| type | string | Always `message_creation``. | Yes |  |
+| type | string | Always `message_creation`. | Yes |  |
 | message_creation | object |  | Yes |  |
 
 
@@ -7785,7 +7785,7 @@ A result instance of the file search.
 
 | Name | Type | Description | Default |
 |------|------|-------------|--------|
-| output | string | The output of the function. This will be `null` if the outputs have not been [submitted](/docs/api-reference/runs/submitToolOutputs) yet. |  |
+| output | string | The output of the function. This will be `null` if the outputs have not been submitted yet. |  |
 
 
 

@@ -17,7 +17,7 @@ Feature availability in Azure OpenAI is dependent on what version of the REST AP
 
 | Latest GA API | Latest Preview API|
 |:-----|:------|
-|`2024-10-21` |`2024-10-01-preview`|
+|`2024-10-21` |`2025-01-01-preview`|
 
 ## Installation
 

@@ -19,7 +19,7 @@ Feature availability in Azure OpenAI is dependent on what version of the REST AP
 
 | Latest GA API | Latest Preview API|
 |:-----|:------|
-|`2024-10-21` |`2024-10-01-preview`|
+|`2024-10-21` |`2025-01-01-preview`|
 
 ## Installation
 
@@ -616,7 +616,7 @@ except openai.APIStatusError as e:
 
 ### Request IDs
 
-To retrieve the ID of your request you can use the `_request_id` property which corresponds to the `x-request-id` responde header.
+To retrieve the ID of your request you can use the `_request_id` property which corresponds to the `x-request-id` response header.
 
 ```python
 print(completion._request_id) 

@@ -11,25 +11,25 @@ ms.date: 01/15/2025
 
 | **Region**     | **gpt-4o**, **2024-05-13**   | **gpt-4o**, **2024-08-06**   | **gpt-4o**, **2024-11-20**   | **gpt-4o-mini**, **2024-07-18**   |
 |:-------------------|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|
-| australiaeast      | ✅                       | ✅                       | -                      | ✅                            |
+| australiaeast      | ✅                       | ✅                       | ✅                       | ✅                            |
 | brazilsouth        | ✅                       | ✅                       | -                      | ✅                            |
 | canadacentral      | ✅                       | ✅                       | -                      | ✅                            |
 | canadaeast         | ✅                       | ✅                       | ✅                       | ✅                            |
-| eastus             | ✅                       | ✅                       | -                      | ✅                            |
-| eastus2            | ✅                       | ✅                       | -                      | ✅                            |
+| eastus             | ✅                       | ✅                       | ✅                       | ✅                            |
+| eastus2            | ✅                       | ✅                       | ✅                       | ✅                            |
 | francecentral      | ✅                       | ✅                       | -                      | ✅                            |
 | germanywestcentral | ✅                       | ✅                       | -                      | ✅                            |
 | japaneast          | ✅                       | ✅                       | -                      | ✅                            |
 | koreacentral       | ✅                       | ✅                       | -                      | ✅                            |
 | northcentralus     | ✅                       | ✅                       | -                      | ✅                            |
-| norwayeast         | ✅                       | ✅                       | -                      | ✅                            |
+| norwayeast         | ✅                       | ✅                       | ✅                       | ✅                            |
 | polandcentral      | ✅                       | ✅                       | -                      | ✅                            |
 | southafricanorth   | ✅                       | ✅                       | -                      | ✅                            |
 | southcentralus     | ✅                       | ✅                       | -                      | ✅                            |
 | southeastasia      | ✅                       | ✅                       | -                      | ✅                            |
 | southindia         | ✅                       | ✅                       | ✅                       | ✅                            |
 | spaincentral       | ✅                       | ✅                       | -                      | ✅                            |
-| swedencentral      | ✅                       | ✅                       | -                      | ✅                            |
+| swedencentral      | ✅                       | ✅                       | ✅                       | ✅                            |
 | switzerlandnorth   | ✅                       | ✅                       | -                      | ✅                            |
 | switzerlandwest    | ✅                       | ✅                       | -                      | ✅                            |
 | uaenorth           | ✅                       | ✅                       | -                      | ✅                            |