@@ -8,7 +8,7 @@ ms.custom: devx-track-python, devx-track-dotnet, devx-track-extended-java, devx-
 ms.topic: quickstart
 author: aahill
 ms.author: aahi
-ms.date: 09/18/2024
+ms.date: 02/10/2025
 zone_pivot_groups: openai-quickstart-assistants
 recommendations: false
 ---

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the model deprecations and retirements in Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 02/06/2025
+ms.date: 02/12/2025
 ms.custom: 
 manager: nitinme
 author: mrbullwinkle
@@ -93,17 +93,15 @@ These models are currently available for use in Azure OpenAI Service.
 
 | Model | Version | Retirement date | Suggested replacements |
 | ---- | ---- | ---- | --- |
-| `babbage-002` | 1 | Retirement Date: January 27, 2025 | |
-| `davinci-002` | 1 | Retirement Date: January 27, 2025 | |
-| `dall-e-2`| 2 | January 27, 2025 | `dalle-3` |
 | `dall-e-3` | 3 | No earlier than April 30, 2025 | |
 | `gpt-35-turbo` | 0301 | February 13, 2025<br><br> Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on January 21, 2025.   | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini`  |
-| `gpt-35-turbo`<br>`gpt-35-turbo-16k` | 0613 | February 13, 2025 <br><br> Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on January 21, 2025.  | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini`|
-| `gpt-35-turbo` | 1106 | No earlier than March 31, 2025 <br><br> Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on January 21, 2025. | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini` |
+| `gpt-35-turbo` | 0613 | February 13, 2025 <br><br> Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on January 21, 2025.  | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini`|
+| `gpt-35-turbo-16k`| 0613 | April, 30, 2025 | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini`|
+| `gpt-35-turbo` | 1106 | No earlier than May 31, 2025 <br><br> Deployments set to [**Auto-update to default**](/azure/ai-services/openai/how-to/working-with-models?tabs=powershell#auto-update-to-default) will be automatically upgraded to version: `0125`, starting on January 21, 2025. | `gpt-35-turbo` (0125) <br><br> `gpt-4o-mini` |
 | `gpt-35-turbo` | 0125 | No earlier than May 31, 2025 | `gpt-4o-mini` |
 | `gpt-4`<br>`gpt-4-32k` | 0314 | June 6, 2025 | `gpt-4o` |
 | `gpt-4`<br>`gpt-4-32k` | 0613 | June 6, 2025 | `gpt-4o` |
-| `gpt-4` | turbo-2024-04-09 | No earlier than April 9, 2025 | `gpt-4o`|
+| `gpt-4` | turbo-2024-04-09 | No earlier than June 6, 2025 | `gpt-4o`|
 | `gpt-4` | 1106-preview | To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than February 10, 2025 **<sup>1</sup>** <br>Retirement date: February 28, 2025  | `gpt-4o`|
 | `gpt-4` | 0125-preview |To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than February 10, 2025 **<sup>1</sup>** <br>Retirement date: February 28, 2025  | `gpt-4o` |
 | `gpt-4` | vision-preview | To be upgraded to `gpt-4` version: `turbo-2024-04-09`, starting no sooner than February 10, 2025  **<sup>1</sup>** <br>Retirement date: February 28, 2025 | `gpt-4o`|
@@ -112,17 +110,17 @@ These models are currently available for use in Azure OpenAI Service.
 | `gpt-4o` | 2024-11-20 | No earlier than November 20, 2025  | |
 | `gpt-4o-mini` | 2024-07-18 | No earlier than July 18, 2025  | |
 | `gpt-4o-realtime-preview` | 2024-10-01 | No earlier than September 30, 2025  | `gpt-4o-realtime-preview` (version 2024-12-17) or `gpt-4o-mini-realtime-preview` (version 2024-12-17) |
-| `gpt-3.5-turbo-instruct` | 0914 | No earlier than April 1, 2025 |  |
+| `gpt-3.5-turbo-instruct` | 0914 | No earlier than May 31, 2025 |  |
 | `o1` | 2024-12-17 | No earlier than December 17, 2025 | |
 | `text-embedding-ada-002` | 2 | No earlier than October 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
 | `text-embedding-ada-002` | 1 | No earlier than October 3, 2025 | `text-embedding-3-small` or `text-embedding-3-large` |
 | `text-embedding-3-small` | | No earlier than October 3, 2025 | |
 | `text-embedding-3-large` | | No earlier than October 3, 2025 | |
 
- **<sup>1</sup>** We will notify all customers with these preview deployments at least 30 days before the start of the upgrades. We will publish an upgrade schedule detailing the order of regions and model versions that we will follow during the upgrades, and link to that schedule from here.
+ **<sup>1</sup>** We'll notify all customers with these preview deployments at least 30 days before the start of the upgrades. We'll publish an upgrade schedule detailing the order of regions and model versions that we'll follow during the upgrades, and link to that schedule from here.
 
 > [!IMPORTANT]
-> Vision enhancements preview features including Optical Character Recognition (OCR), object grounding, video prompts will be retired and no longer available once `gpt-4` Version: `vision-preview` is upgraded to `turbo-2024-04-09`. If you are currently relying on any of these preview features, this automatic model upgrade will be a breaking change.
+> Vision enhancements preview features including Optical Character Recognition (OCR), object grounding, video prompts will be retired and no longer available once `gpt-4` Version: `vision-preview` is upgraded to `turbo-2024-04-09`. If you're currently relying on any of these preview features, this automatic model upgrade will be a breaking change.
 
 ## Default model versions
 
@@ -133,16 +131,19 @@ These models are currently available for use in Azure OpenAI Service.
 
 ## Deprecated models
 
-These models were deprecated on July 6, 2023 and were retired on June 14, 2024. These models are no longer available for new deployments. Deployments created before July 6, 2023 remain available to customers until June 14, 2024. We recommend customers migrate their applications to deployments of replacement models before the June 14, 2024 retirement.
+ These models are no longer available for new deployments.
 
 If you're an existing customer looking for information about these models, see [Legacy models](./legacy-models.md).
 
 | Model | Deprecation date | Retirement date | Suggested replacement |
 | --------- | --------------------- | ------------------- | -------------------- |
-| ada | July 6, 2023 | June 14, 2024 | babbage-002 |
-| babbage | July 6, 2023 | June 14, 2024 | babbage-002 |
-| curie | July 6, 2023 | June 14, 2024 | davinci-002 |
-| davinci | July 6, 2023 | June 14, 2024 | davinci-002 |
+| babbage-002 | | January 27, 2025 |  |
+| davinci-002 | | January 27, 2025 | |
+| dall-e-2|  | January 27, 2025 | dalle-3 |
+| ada | July 6, 2023 | June 14, 2024 |  |
+| babbage | July 6, 2023 | June 14, 2024 |  |
+| curie | July 6, 2023 | June 14, 2024 | |
+| davinci | July 6, 2023 | June 14, 2024 |  |
 | text-ada-001 | July 6, 2023 | June 14, 2024 | gpt-35-turbo-instruct |
 | text-babbage-001 | July 6, 2023 | June 14, 2024 | gpt-35-turbo-instruct |
 | text-curie-001 | July 6, 2023 | June 14, 2024 | gpt-35-turbo-instruct |
@@ -169,6 +170,10 @@ If you're an existing customer looking for information about these models, see [
 
 ## Retirement and deprecation history
 
+## February 12, 2025
+
+- Updates to `gpt-35-turbo-16k` (0613), (1106), `gpt-35-turbo-instruct`, `gpt-4` (turbo-2024-04-09)
+
 ## February 6, 2025
 
 - Updates to `gpt-35-turbo`, `gpt-4` preview models, and `gpt-4o` models.

@@ -55,8 +55,8 @@ To learn more about the advanced `o-series` models see, [getting started with re
 |---|---|
 |`o3-mini` | East US2 (Global Standard) <br> Sweden Central (Global Standard) |
 |`o1` | East US2 (Global Standard) <br> Sweden Central (Global Standard) |
-| `o1-preview` | See the [models table](#global-standard-model-availability). |
-| `o1-mini` | See the [models table](#global-provisioned-managed-model-availability). |
+| `o1-preview` | See the [models table](#model-summary-table-and-region-availability). |
+| `o1-mini` | See the [models table](#model-summary-table-and-region-availability). |
 
 ## GPT-4o audio
 

@@ -13,18 +13,17 @@ ms.author: aahi
 recommendations: false
 ---
 
-# Azure OpenAI provisioned August 2024 update 
+# Azure OpenAI provisioned Managed offering updates 
 
-In mid-August, 2024, Microsoft launched improvements to its Provisioned Throughput offering that address customer feedback on usability and operational agility that open new payment options and deployment scenarios.
+Microsoft launched improvements to its Provisioned Throughput offering that address customer feedback on usability and operational agility that open new payment options and deployment scenarios.
 
 This article is intended for existing users of the provisioned throughput offering. New customers should refer to the [Azure OpenAI provisioned onboarding guide](../how-to/provisioned-throughput-onboarding.md).
 
 ## What's changing?
 
-The capabilities below are rolling out for the Provisioned Managed offering.
 
 > [!IMPORTANT]
-> The changes in this article do not apply to the older *"Provisioned Classic (PTU-C)"* offering. They only affect the Provisioned (also known as the Provisioned Managed) offering.
+> The changes in this article don't apply to the older *"Provisioned Classic (PTU-C)"* offering. They only affect the Provisioned (also known as the Provisioned Managed) offering.
 
 ### Usability improvements
 
@@ -39,11 +38,11 @@ The capabilities below are rolling out for the Provisioned Managed offering.
 
 |Feature | Benefit|
 |---|---|
-|Hourly, uncommitted usage | Hourly payment option without a required commitment enables short-term deployment scenarios. |
+|Non-binding, Hourly option | Hourly payment option without any binding enables short-term deployment scenarios. Ideal for testing new models and assessing benefits of Provisioned Throughput. |
 |Term discounts via Azure Reservations | Azure reservations provide substantial discounts over the hourly rate for one month and one year terms, and provide flexible scopes that minimize administration and associated with today’s resource-bound commitments.|
 | Default provisioned-managed quota in many regions | Get started quickly in new regions without having to first request quota. |
-| Flexible choice of payment model for existing provisioned customers | Customers with commitments can stay on the commitment model at least through the end of 2024, and can choose to migrate existing commitments to hourly/reservations via a self-service or managed process. |
-| Supports latest model generations | The hourly/reservation model is required to deploy models released after August 1, 2024. |
+| Flexible choice of payment model for existing provisioned customers | Customers with commitments can stay on the commitment model till the end of life of the currently supported models, and can choose to migrate existing commitments to hourly/reservations via managed process. We recommend migrating to hourly/ reservations to take advantage of term discounts and to work with the latest models. |
+| Supports latest model generations | The latest models are available only on hourly/ reservations in provisioned offering. |
 
 ## Usability improvement details
 
@@ -73,7 +72,7 @@ The following quota screenshot shows model-independent quota being used by deplo
 
 ## Quota as a limit
 
-Prior to the August update, Azure OpenAI Provisioned was only available to a few customers, and quota was allocated to maximize the ability for them to deploy and use it. With these changes, the process of acquiring quota is simplified for all users, and there is a greater likelihood of running into service capacity limitations when deployments are attempted. A new API and portal experience are available to help users find regions where the subscription has quota and the service has capacity to support deployments of a desired model.
+Prior to the August update, Azure OpenAI Provisioned was only available to a few customers, and quota was allocated to maximize the ability for them to deploy and use it. With these changes, the process of acquiring quota is simplified for all users, and there's a greater likelihood of running into service capacity limitations when deployments are attempted. A new API and portal experience are available to help users find regions where the subscription has quota and the service has capacity to support deployments of a desired model.
 
 We also recommend that customers using commitments now create their deployments before creating or expanding commitments to cover them. This guarantees that capacity is available before creating a commitment and prevents over-purchase of the commitment. To support this, the restriction that prevented deployments from being created larger than their commitments has been removed. This new approach to quota, capacity availability, and commitments matches what is provided under the hourly/reservation model, and the guidance to deploy before purchasing a commitment (or reservation, for the hourly model) is the same for both.
 
@@ -87,7 +86,7 @@ See the following links for more information. The guidance for reservations and
 > [!NOTE]
 > The following description of payment models doesn't apply to the older "Provisioned Classic (PTU-C)" offering. They only affect the Provisioned (also known as Provisioned Managed) offering. Provisioned Classic continues to be governed by the unchanged monthly commitment payment model.
 
-Microsoft has introduced a new "Hourly/reservation" payment model for provisioned deployments. This is in addition to the current **Commitment** payment model, which will continue to be supported at least through the end of 2024.
+Microsoft has introduced a new "Hourly/reservation" payment model for provisioned deployments. This is in addition to the current **Commitment** payment model, which will continue to be supported till end of life of the currently supported limited model list. Refer to the [supported models on **Commitment payment model**](./provisioned-migration.md#supported-models-on-commitment-payment-model) for the list of supported models on Commitment payment model.
 
 ### Commitment payment model
 
@@ -97,7 +96,18 @@ Microsoft has introduced a new "Hourly/reservation" payment model for provisione
 
 - Commitments can't be canceled or altered during their term, except to add new PTUs.
 
-- Supports models released prior to August 1, 2024.
+#### Supported models on Commitment payment model:
+  Only the following list of Azure OpenAI models are supported in Commitments. For onboarding any other models that aren't in the list below, or any newer models on provisioned throughput offering,  refer to the [Azure OpenAI provisioned onboarding guide](../how-to/provisioned-throughput-onboarding.md) and [Azure Reservations for Azure OpenAI provisioned deployments](../how-to/provisioned-throughput-onboarding.md#azure-reservations-for-azure-openai-provisioned-deployments)
+    
+|Supported models on Commitment plan |
+|-|
+|gpt-35-turbo|
+|gpt-4|
+|gpt-4-32k|
+|gpt-4o|
+
+
+
 
 ### Hourly reservation payment model
 
@@ -112,7 +122,7 @@ Microsoft has introduced a new "Hourly/reservation" payment model for provisione
 - Supports all models, both old and new.
 
 > [!IMPORTANT]
-> **Models released after August 1, 2024 require the use of the Hourly/Reservation payment model.** They are not deployable on Azure OpenAI resources that have active commitments. To deploy models released after August 1, existing customers must either:
+> More latest models are available in provisioned offering with Hourly/Reservation payment model. Check the list [**here**](https://learn.microsoft.com/azure/ai-services/openai/concepts/models?tabs=provisioned%2Cstandard-chat-completions#global-standard-model-availability) for the availabilityModels that aren't in the above [**list**](./provisioned-migration.md#supported-models-on-commitment-payment-model) aren't deployable on Azure OpenAI resources that have active commitments. To deploy models newer models you must either:
 > - Create deployments on Azure OpenAI resources without commitments.
 > - Migrate an existing resource off its commitments.
 
@@ -142,25 +152,25 @@ Steps 1 and 2 are the same in all cases. The difference is whether a commitment
     |Discount type  |Available Scopes (within a region)  |
     |---------|---------|
     |Commitment     |  Azure OpenAI resource        |
-    |Row2     | Resource group, single subscription, management group (group of subscriptions), shared (all subscriptions in a billing account)          |
+    |Reservation     | Resource group, single subscription, management group (group of subscriptions), shared (all subscriptions in a billing account)          |
 
 * The discounted price is applied to deployed PTUs up to the number of discounted PTUs in the discount. 
 * The number of deployed PTUs exceeding the discounted PTUs (or not covered by any discount) are charged the hourly rate. 
-* The best practice is to create deployments first, and then to apply discounts. This is to guarantee that service. capacity is available to support your deployments prior to creating a term commitment for PTUs you cannot use. 
+* The best practice is to create deployments first, and then to apply discounts. This is to guarantee that service. capacity is available to support your deployments prior to creating a term agreement for PTUs you can't use. 
 
 > [!NOTE] 
 > When you follow best practices, you might receive hourly charges between the time you create the deployment and increase your discount (commitment or reservation).   
 >
-> For this reason, we recommend that you be prepared to increase your discount immediately following the deployment. The prerequisites for purchasing an Azure reservations are different than for commitments, and we recommend you validate them prior to deployment if you intend to use them to discount your deployment. For more information, see [Permissions to view and manage Azure reservations](/azure/cost-management-billing/reservations/view-reservations) 
+> For this reason, we recommend that you're prepared to increase your discount immediately following the deployment. The prerequisites for purchasing an Azure reservations are different than for commitments, and we recommend you validate them prior to deployment if you intend to use them to discount your deployment. For more information, see [Permissions to view and manage Azure reservations](/azure/cost-management-billing/reservations/view-reservations) 
 
 ## Mapping deployments to discounting method 
 
-Customers using Azure OpenAI Provisioned prior to August 2024 can use either or both payment models simultaneously within a subscription. The payment model used for each deployment is determined based on its Azure OpenAI resource: 
+Customers using Azure OpenAI Provisioned offer prior to August 2024 can use either or both payment models simultaneously within a subscription. The payment model used for each deployment is determined based on its Azure OpenAI resource: 
 
 
 **Resource has an active Commitment** 
 
-* The commitment discounts all deployments on the resource up to the number of PTUs on the commitment. Any excess PTUs will be billed hourly. 
+* The commitment discounts all deployments on the resource up to the number of PTUs on the commitment. Any excess PTUs will be billed hourly unless the excess PTUs aren't in the scope of an active reservation. If the excess PTUs exist in the scope of an active reservation, will be discounted as a group up to the number of PTUs on the reservation and any excess spill still leftover will be billed hourly. 
 
 **Resource does not have an active commitment** 
 
@@ -169,12 +179,13 @@ Customers using Azure OpenAI Provisioned prior to August 2024 can use either or
 
 ### Changes to the existing payment mode
 
-Customers that have commitments today can continue to use them at least through the end of 2024. This includes purchasing new PTUs on new or existing commitments and managing commitment renewal behaviors. However, the August update has changed certain aspects of commitment operation.
+Customers that have commitments today can continue to use them at least till the supported model's retirement. This includes purchasing new PTUs on new or existing commitments and managing commitment renewals. However, the August update has changed certain aspects of commitments operation.
 
-- Only models released as provisioned prior to August 1, 2024 or before can be deployed on a resource with a commitment.
+- Azure OpenAI has stopped supporting enrollment on to new commitments, starting August 1, 2024
+- Only a limited set of models can be deployed on a resource with a commitment. Here's the [List of models](./provisioned-migration.md#supported-models-on-commitment-payment-model)
 
 - If the deployed PTUs under a commitment exceed the committed PTUs, the hourly overage charges will be emitted against the same hourly meter as used for the new hourly/reservation payment model. This allows the overage charges to be discounted via an Azure Reservation.
-- It is possible to deploy more PTUs than are committed on the resource. This supports the ability to guarantee capacity availability prior to increasing the commitment size to cover it.
+- It's possible to deploy more PTUs than are committed on the resource. This supports the ability to guarantee capacity availability prior to increasing the commitment size to cover it.
 
 ## Migrating existing resources off commitments
 
@@ -206,7 +217,7 @@ An alternative approach to self-service migration is to switch the reservation p
 * There will be a short period of double-billing or hourly charges during the switchover from committed to hourly/reservation billing.
 
 > [!IMPORTANT]
-> Both self-service approaches generate some additional charges as the payment mode is switched from Committed to Hourly/Reservation. These are characteristics of the migration approaches and customers aren't credited for these charges.  Customers can choose to use the managed migration approach described below to avoid them.
+> Self-service approach generates additional charges as the payment mode is switched from Committed to Hourly/Reservation. This is the characteristics of this migration approaches and customers aren't credited for these charges. Alternately, Customers can choose to use the managed migration approach described below to avoid additional charges.
 
 ### Managed migration
 
@@ -257,11 +268,11 @@ For each new commitment you need to create, follow these steps:
 
 2. Select **Purchase commitment**.
 
-3. Select the Azure OpenAI resource and purchase the commitment. You will see your resources divided into resources with existing commitments, which you can edit and resources that don't currently have a commitment.
+3. Select the Azure OpenAI resource and purchase the commitment. You'll see your resources divided into resources with existing commitments, which you can edit and resources that don't currently have a commitment.
 
 | Setting | Notes |
 |---------|-------|
-| **Select a resource** | Choose the resource where you'll create the provisioned deployment. Once you have purchased the commitment, you will be unable to use the PTUs on another resource until the current commitment expires. |
+| **Select a resource** | Choose the resource where you'll create the provisioned deployment. Once you have purchased the commitment, you'll be unable to use the PTUs on another resource until the current commitment expires. |
 | **Select a commitment type** | Select Provisioned. (Provisioned is equivalent to Provisioned Managed) |
 | **Current uncommitted provisioned quota** | The number of PTUs currently available for you to commit to this resource. | 
 | **Amount to commit (PTU)** | Choose the number of PTUs you're committing to. **This number can be increased during the commitment term, but can't be decreased**. Enter values in increments of 50 for the commitment type Provisioned. |
@@ -273,7 +284,7 @@ For each new commitment you need to create, follow these steps:
 :::image type="content" source="../media/how-to/provisioned-onboarding/commitment-tier.png" alt-text="Screenshot of commitment purchase UI." lightbox="../media/how-to/provisioned-onboarding/commitment-tier.png":::
 
 > [!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'll be billed again on each renewal date based on the renewal settings.
 
 ### Edit an existing Provisioned Throughput commitment
 
@@ -291,14 +302,14 @@ Adding PTUs to an existing commitment will allow you to create larger or more nu
 :::image type="content" source="../media/how-to/provisioned-onboarding/increase-commitment.png" alt-text="Screenshot of commitment purchase UI with an increase in the amount to commit value." lightbox="../media/how-to/provisioned-onboarding/increase-commitment.png":::
 
 > [!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 doesn't reset the commitment term.
+> When you add PTUs to a commitment, they'll be billed immediately, at a pro-rated amount from the current date to the end of the existing commitment term. Adding PTUs doesn't reset the commitment term.
 
 ### Changing renewal settings
 
 Commitment renewal settings can be changed at any time before the expiration date of your commitment. Reasons you might want to change the renewal settings include ending your use of provisioned throughput by setting the commitment to not autorenew, or to decrease usage of provisioned throughput by lowering the number of PTUs that will be committed in the next period.
 
 > [!IMPORTANT]
-> If you allow a commitment to expire or decrease in size such that the deployments under the resource require more PTUs than you have in your resource commitment, you will receive hourly overage charges for any excess PTUs. For example, a resource that has deployments that total 500 PTUs and a commitment for 300 PTUs will generate hourly overage charges for 200 PTUs.
+> If you allow a commitment to expire or decrease in size such that the deployments under the resource require more PTUs than you have in your resource commitment, you'll receive hourly overage charges for any excess PTUs. For example, a resource that has deployments that total 500 PTUs and a commitment for 300 PTUs will generate hourly overage charges for 200 PTUs.
 
 ## Monitor commitments and prevent unexpected billings
 

@@ -21,9 +21,13 @@ The provisioned throughput capability allows you to specify the amount of throug
 ## What do the provisioned deployment types provide?
 
 - **Predictable performance:** stable max latency and throughput for uniform workloads.
-- **Reserved processing capacity:** A deployment configures the amount of throughput. Once deployed, the throughput is available whether used or not.
+- **Allocated processing capacity:** A deployment configures the amount of throughput. Once deployed, the throughput is available whether used or not.
 - **Cost savings:** High throughput workloads might provide cost savings vs token-based consumption.
 
+> [!NOTE]
+> Customers can take advantage of additional cost savings on provisioned deployments when they buy [Microsoft Azure OpenAI Service reservations](/azure/cost-management-billing/reservations/azure-openai#buy-a-microsoft-azure-openai-service-reservation). 
+
+
 An Azure OpenAI Deployment is a unit of management for a specific OpenAI Model. A deployment provides customer access to a model for inference and integrates more features like Content Moderation ([See content moderation documentation](content-filter.md)). Global provisioned deployments are available in the same Azure OpenAI resources as all other deployment types but allow you to leverage Azure's global infrastructure to dynamically route traffic to the data center with the best availability for each request. Similarly, data zone provisioned deployments are also available in the same resources as all other deployment types but allow you to leverage Azure's global infrastructure to dynamically route traffic to the data center within the Microsoft specified data zone with the best availability for each request. 
 
 ## What do you get?
@@ -165,7 +169,7 @@ For provisioned deployments, we use a variation of the leaky bucket algorithm to
 
     a.    When the current utilization is above 100%, the service returns a 429 code with the `retry-after-ms` header set to the time until utilization is below 100%
    
-    b.    Otherwise, the service estimates the incremental change to utilization required to serve the request by combining the prompt tokens, less any cacehd tokens, and the specified `max_tokens` in the call. A customer can receive up to a 100% discount on their prompt tokens depending on the size of their cached tokens. If the `max_tokens` parameter is not specified, the service estimates a value. This estimation can lead to lower concurrency than expected when the number of actual generated tokens is small.  For highest concurrency, ensure that the `max_tokens` value is as close as possible to the true generation size.
+    b.    Otherwise, the service estimates the incremental change to utilization required to serve the request by combining the prompt tokens, less any cached tokens, and the specified `max_tokens` in the call. A customer can receive up to a 100% discount on their prompt tokens depending on the size of their cached tokens. If the `max_tokens` parameter is not specified, the service estimates a value. This estimation can lead to lower concurrency than expected when the number of actual generated tokens is small.  For highest concurrency, ensure that the `max_tokens` value is as close as possible to the true generation size.
    
 1. When a request finishes, we now know the actual compute cost for the call. To ensure an accurate accounting, we correct the utilization using the following logic:
 

@@ -22,9 +22,9 @@ Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to
 
 ## Assistants support
 
-### Region and model support
+### Region, model, and API support
 
-Code interpreter is available in all regions supported by Azure OpenAI Assistants. The [models page](../concepts/models.md#assistants-preview) contains the most up-to-date information on regions/models where Assistants are currently supported.
+The [models page](../concepts/models.md#assistants-preview) contains the most up-to-date information on regions/models where Assistants are currently supported. Assistants can be used in version `2024-02-15-preview` and later of the Azure OpenAI inference [preview API](../reference-preview.md#api-specs). A full list of previous API versions can be found on [GitHub](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview).
 
 ### Supported file types
 
@@ -96,7 +96,7 @@ from openai import AzureOpenAI
     
 client = AzureOpenAI(
     api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-    api_version="2024-05-01-preview",
+    api_version="2024-08-01-preview",
     azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
     )
 

@@ -8,7 +8,7 @@ ms.custom:
   - build-2024
   - ignite-2024
 ms.topic: include
-ms.date: 03/04/2024
+ms.date: 02/10/2025
 author: aahill
 ms.author: aahi
 ---
@@ -26,8 +26,14 @@ ms.author: aahi
 [Azure AI Foundry](https://ai.azure.com) lets you use Assistants v2 which provides several upgrades such as the [file search](../how-to/file-search.md) tool which is faster and supports more files.
 
 1. Sign in to [Azure AI Foundry](https://ai.azure.com).
-1. Go to your project or [create a new project](../../../ai-studio//how-to/create-projects.md) in Azure AI Foundry portal.
-1. From your project overview, select **Assistants**, located under **playgrounds**.
+1. If your screen doesn't look like the following screenshot, select **Azure AI Foundry** in the top left of the screen.
+1. Select **Let's go** in the Azure OpenAI Service card. 
+
+    :::image type="content" source="../media/assistants/foundry-openai-selectior.png" alt-text="A screenshot of the main page of the Azure AI Foundry." lightbox="../media/assistants/foundry-openai-selectior.png":::
+    
+1. In the navigation menu on the left, select **Assistants**, located under **playgrounds**.
+
+    :::image type="content" source="../media/quickstarts/assistants-ai-studio-playground.png" alt-text="Screenshot of the Assistant configuration screen without all the values filled in." lightbox="../media/quickstarts/assistants-ai-studio-playground.png":::
 
     The Assistants playground allows you to explore, prototype, and test AI Assistants without needing to run any code. From this page, you can quickly iterate and experiment with new ideas.
     
@@ -42,8 +48,6 @@ ms.author: aahi
     | **Code interpreter** | Code interpreter provides access to a sandboxed Python environment that can be used to allow the model to test and execute code. |
     | **Files** | You can upload up to 10,000 files, with a max file size of 512 MB to use with tools. Not used in this quickstart. |
     
-    :::image type="content" source="../media/quickstarts/assistants-ai-studio-playground.png" alt-text="Screenshot of the Assistant configuration screen without all the values filled in." lightbox="../media/quickstarts/assistants-ai-studio-playground.png":::
-
 ## Create your first Assistant
 
 1. Select your deployment from the **Deployments** dropdown.

@@ -5,7 +5,7 @@ description: Azure OpenAI model global batch limits
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 01/14/2025
+ms.date: 02/12/2025
 ---
 
 ## Batch limits
@@ -29,6 +29,7 @@ The table shows the batch quota limit. Quota values for global batch are represe
 | `gpt-4-turbo` | 300 M | 80 M | 40 M | 90 K | N/A |
 | `gpt-4` | 150 M | 30 M | 5 M | 100 K | N/A |
 | `gpt-35-turbo` | 10 B | 1 B | 100 M | 2 M | 50 K |
+| `o3-mini`| 15 B | 1 B | 50 M | 90 K | N/A |
 
 B = billion | M = million | K = thousand
 

@@ -68,6 +68,8 @@ AzureOpenAIClient azureClient = new(
 ChatClient chatClient = azureClient.GetChatClient("my-gpt-4o-mini-deployment");
 ```
 
+For more information about Azure OpenAI keyless authentication, see the "[Get started with the Azure OpenAI security building block](/azure/developer/ai/get-started-securing-your-ai-app?tabs=github-codespaces&pivots=dotnet)" QuickStart article. 
+
 # [API Key](#tab/dotnet-key)
 
 ```csharp

@@ -69,6 +69,7 @@ func main() {
 }
 ```
 
+For more information about Azure OpenAI keyless authentication, see [Use Azure OpenAI without keys](/azure/developer/ai/keyless-connections?tabs=go%2Cazure-cli). 
 
 # [API Key](#tab/api-key)
 

@@ -67,6 +67,8 @@ OpenAIClient client = new OpenAIClientBuilder()
     .buildClient();
 ```
 
+For more information about Azure OpenAI keyless authentication, see [Use Azure OpenAI without keys](/azure/developer/ai/keyless-connections?tabs=java%2Cazure-cli). 
+
 # [API Key](#tab/api-key)
 
 ```java

@@ -57,6 +57,8 @@ const client = new AzureOpenAI({
      });
 ```
 
+For more information about Azure OpenAI keyless authentication, see the "[Get started with the Azure OpenAI security building block](/azure/developer/ai/get-started-securing-your-ai-app?tabs=github-codespaces&pivots=typescript)" QuickStart article. 
+
 # [API Key](#tab/api-key)
 
 API Key

@@ -53,6 +53,8 @@ client = AzureOpenAI(
 )
 ```
 
+For more information about Azure OpenAI keyless authentication, see the "[Get started with the Azure OpenAI security building block](/azure/developer/ai/get-started-securing-your-ai-app?tabs=github-codespaces&pivots=python)" QuickStart article. 
+
 # [API Key](#tab/python-key)
 
 [!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]

@@ -14,7 +14,7 @@ recommendations: false
 
 # What is Azure OpenAI Service?
 
-Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o3-mini, o1, o1-mini, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or in the [Azure AI Foundry](https://ai.azure.com).
+Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o3-mini, o1, o1-mini, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, image understanding, semantic search, and natural language to code translation. Users can access the service through REST APIs, [Python/C#/JS/Java/Go SDKs](/azure/ai-services/openai/supported-languages).
 
 ### Features overview