@@ -1,7 +1,7 @@
 ---
 title: Image prompt engineering techniques
 titleSuffix: Azure OpenAI
-description: Learn how to better engineer image prompts for GPT-4 Turbo with Vision.
+description: Learn how to better engineer image prompts for vision-enabled chat models.
 author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
@@ -12,7 +12,7 @@ manager: nitinme
 
 # Image prompt engineering techniques
 
-To unlock the full potential of GPT-4 Turbo with Vision, it's essential to tailor the prompts to your specific needs. Here are some guidelines to enhance the accuracy and efficiency of your prompts.
+To unlock the full potential of vision-enabled chat models like GPT-4 Turbo with Vision and GPT-4o, it's essential to tailor the prompts to your specific needs. Here are some guidelines to enhance the accuracy and efficiency of your prompts.
 
 ## Fundamentals of crafting your image prompt
 - **Contextual specificity:** Adding context to the scenario at hand gives the model a better understanding of an appropriate output. This level of specificity aids in focusing on relevant aspects and avoiding extraneous details.
@@ -28,57 +28,57 @@ To unlock the full potential of GPT-4 Turbo with Vision, it's essential to tailo
 - **Define output format:** Clearly mention the desired format for the output, such as markdown, JSON, HTML, etc. You can also suggest a specific structure, length, or specific attributes about the response.
 
 ## Example prompt inputs & outputs
-There are many ways to craft system prompts to tailor the output specifically to your needs. The following sample inputs and outputs showcase how adjusting your prompts can give you different results. Try the model out for yourself using these images and adjusting the system prompt in the [Azure AI Foundry playground](https://ai.azure.com/).
+There are many ways to craft system prompts to tailor the output specifically to your needs. The following sample inputs and outputs showcase how adjusting your prompts can give you different results. Try out the model for yourself using these images and adjusting the system prompt in the [Azure AI Foundry playground](https://ai.azure.com/).
 
 ### Contextual specificity  
-Context can help improve feedback from the model. For example, if you're working on image descriptions for a product catalog, ensure your prompt reflects this in a clear and concise way. A prompt like “Describe images for an outdoor hiking product catalog, focusing on enthusiasm and professionalism” guides the model to generate responses that are both accurate and contextually rich.
+Context can help improve feedback from the model. For example, if you're working on image descriptions for a product catalog, ensure your prompt reflects that in a clear and concise way. A prompt like “Describe images for an outdoor hiking product catalog, focusing on enthusiasm and professionalism” guides the model to generate responses that are both accurate and contextually rich.
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/tent-chairs.jpg" lightbox="../media/concepts/gpt-v/tent-chairs.jpg" alt-text="Photograph of a camping tent and chairs in a forest."::: What is in this image? | The image shows a camping setup in a forest area. There is a yellow tent, two folding camping chairs—one red and one blue—and a portable camping table. The surrounding area is densely populated with trees, indicating that the campsite is in a wooded region. |
+| :::image type="content" source="../media/concepts/gpt-v/tent-chairs.jpg" lightbox="../media/concepts/gpt-v/tent-chairs.jpg" alt-text="Photograph of a camping tent and chairs in a forest."::: What is in this image? | `The image shows a camping setup in a forest area. There is a yellow tent, two folding camping chairs—one red and one blue—and a portable camping table. The surrounding area is densely populated with trees, indicating that the campsite is in a wooded region.` |
 
 |Updated prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/tent-chairs.jpg" lightbox="../media/concepts/gpt-v/tent-chairs.jpg" alt-text="Photograph of a camping tent and chairs in a forest."::: Describe images for an outdoor hiking product catalog, focusing on enthusiasm and professionalism. | "Embark on your next outdoor adventure with our premium hiking and camping essentials! Featured here is our vibrant, spacious tent, designed to be your home away from home amidst the serenity of nature. Crafted with top-notch materials, it promises durability and comfort in any weather. Complementing the tent are our ultra-comfortable, lightweight folding chairs, available in eye-catching red and serene blue, perfect for those moments of relaxation after a day of exploration. Don't forget our sturdy, portable table, ideal for a delightful outdoor meal or a game of cards under the stars. Elevate your camping experience with our reliable products, where quality meets the great outdoors!" |
+| :::image type="content" source="../media/concepts/gpt-v/tent-chairs.jpg" lightbox="../media/concepts/gpt-v/tent-chairs.jpg" alt-text="Photograph of a camping tent and chairs in a forest."::: Describe images for an outdoor hiking product catalog, focusing on enthusiasm and professionalism. | `"Embark on your next outdoor adventure with our premium hiking and camping essentials! Featured here is our vibrant, spacious tent, designed to be your home away from home amidst the serenity of nature. Crafted with top-notch materials, it promises durability and comfort in any weather. Complementing the tent are our ultra-comfortable, lightweight folding chairs, available in eye-catching red and serene blue, perfect for those moments of relaxation after a day of exploration. Don't forget our sturdy, portable table, ideal for a delightful outdoor meal or a game of cards under the stars. Elevate your camping experience with our reliable products, where quality meets the great outdoors!"` |
 
 
 ### Task-oriented prompts
 Giving the model a specific task to follow is one way of sharpening the response. In this example, this prompt steers the model to concentrate on elements crucial for insurance assessments, thereby improving accuracy and relevancy.
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/car-crash.jpg" lightbox="../media/concepts/gpt-v/car-crash.jpg" alt-text="Photograph of a car crashed into a pole."::: What is in this image? | The image shows a silver sedan that has been in an accident. The front end of the car is damaged, with the hood crumpled, and the bumper partially detached. The car is resting against a utility pole on the sidewalk, and there is visible smoke coming from the engine area, indicating a recent crash. The setting appears to be a residential street, as indicated by the presence of houses in the background, a neatly trimmed hedge, and a sidewalk. |
+| :::image type="content" source="../media/concepts/gpt-v/car-crash.jpg" lightbox="../media/concepts/gpt-v/car-crash.jpg" alt-text="Photograph of a car crashed into a pole."::: What is in this image? | `The image shows a silver sedan that has been in an accident. The front end of the car is damaged, with the hood crumpled, and the bumper partially detached. The car is resting against a utility pole on the sidewalk, and there is visible smoke coming from the engine area, indicating a recent crash. The setting appears to be a residential street, as indicated by the presence of houses in the background, a neatly trimmed hedge, and a sidewalk.` |
 
 |Updated prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/car-crash.jpg" lightbox="../media/concepts/gpt-v/car-crash.jpg" alt-text="Photograph of a car crashed into a pole."::: Analyze this car damage video for an auto insurance report, focusing on identifying and detailing damage | In the image provided, we observe a silver sedan that has sustained significant front-end damage. The vehicle's hood is notably crumpled, and the front bumper has suffered substantial deformation, partially detaching from the chassis. The right headlamp assembly appears to be damaged or dislodged. There is also visible damage to the front grille area. The car is in contact with a utility pole, suggesting a frontal collision with the pole at a relatively low or moderate speed. Smoke is emanating from the engine compartment, suggesting potential mechanical or electrical damage that may need further inspection for a comprehensive assessment. The airbags do not appear to be deployed in this image. The vehicle will require a detailed inspection to evaluate the full extent of structural and mechanical damage for a complete insurance report. |
+| :::image type="content" source="../media/concepts/gpt-v/car-crash.jpg" lightbox="../media/concepts/gpt-v/car-crash.jpg" alt-text="Photograph of a car crashed into a pole."::: Analyze this car damage video for an auto insurance report, focusing on identifying and detailing damage | `In the image provided, we observe a silver sedan that has sustained significant front-end damage. The vehicle's hood is notably crumpled, and the front bumper has suffered substantial deformation, partially detaching from the chassis. The right headlamp assembly appears to be damaged or dislodged. There is also visible damage to the front grille area. The car is in contact with a utility pole, suggesting a frontal collision with the pole at a relatively low or moderate speed. Smoke is emanating from the engine compartment, suggesting potential mechanical or electrical damage that may need further inspection for a comprehensive assessment. The airbags do not appear to be deployed in this image. The vehicle will require a detailed inspection to evaluate the full extent of structural and mechanical damage for a complete insurance report.` |
 
 ### Handling refusals
 When you receive a refusal output from a model, there are several ways you can tweak input to generate a more appropriate response. Below is one example of how to handle a refusal and progress. 
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/dinner-spread.jpg" lightbox="../media/concepts/gpt-v/dinner-spread.jpg" alt-text="Photograph of a dinner table with several plates of food."::: Plan this meal | Sorry, I can't provide that information. |
+| :::image type="content" source="../media/concepts/gpt-v/dinner-spread.jpg" lightbox="../media/concepts/gpt-v/dinner-spread.jpg" alt-text="Photograph of a dinner table with several plates of food."::: Plan this meal | `Sorry, I can't provide that information.` |
 
 |Follow up prompt|Model output|
 |--------|-----------|
-| What information do you need? | I apologize for the confusion earlier. To plan a meal based on the image provided, I would need to know the following: <br> The number of people dining to ensure there's enough food for everyone.<br>Any dietary restrictions or preferences to accommodate (e.g., vegetarian, gluten-free, allergies).<br>The occasion or type of meal being planned (e.g., casual lunch, dinner party).<br>Desired balance of the meal in terms of food groups and nutrition. <br>With this information, I could help suggest how to portion the dishes, create a balanced meal, and ensure the meal meets the needs of all diners.|
+| What information do you need? | `I apologize for the confusion earlier. To plan a meal based on the image provided, I would need to know the following: <br> The number of people dining to ensure there's enough food for everyone.<br>Any dietary restrictions or preferences to accommodate (e.g., vegetarian, gluten-free, allergies).<br>The occasion or type of meal being planned (e.g., casual lunch, dinner party).<br>Desired balance of the meal in terms of food groups and nutrition. <br>With this information, I could help suggest how to portion the dishes, create a balanced meal, and ensure the meal meets the needs of all diners.`|
 
 ### Adding examples
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: What is this dog breed and what is its average weight and height? | The dog in the image appears to be a Border Collie. Border Collies are known for their intelligence, agility, and herding abilities.The average weight for a Border Collie is typically around 30-45 pounds (14-20 kilograms). As for height, Border Collies generally stand about 18-22 inches (46-56 centimeters) tall at the shoulder. These figures can vary based on the individual dog's genetics, diet, and level of activity. Border Collies are medium-sized dogs with a well-proportioned body and a high level of energy. |
+| :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: What is this dog breed and what is its average weight and height? | `The dog in the image appears to be a Border Collie. Border Collies are known for their intelligence, agility, and herding abilities.The average weight for a Border Collie is typically around 30-45 pounds (14-20 kilograms). As for height, Border Collies generally stand about 18-22 inches (46-56 centimeters) tall at the shoulder. These figures can vary based on the individual dog's genetics, diet, and level of activity. Border Collies are medium-sized dogs with a well-proportioned body and a high level of energy.` |
 
 |Updated prompt|Model output|
 |--------|-----------|
-| Q: What is this dog breed and what is its average weight & height? :::image type="content" source="../media/concepts/gpt-v/pomeranian.jpg" lightbox="../media/concepts/gpt-v/pomeranian.jpg" alt-text="Photograph of a pomeranian dog sitting on a pool diving board above the water."::: <br> A: Breed: pomeranian; average weight: 3-7 lbs; average height: 8-14 inches <br> :::image type="content" source="../media/concepts/gpt-v/bulldog.jpg" lightbox="../media/concepts/gpt-v/bulldog.jpg" alt-text="Photograph of a bulldog laying in a grass field."::: A: Breed: English bulldog; average weight: 50-55 lbs; average height: 14-16 inches :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: | A: Breed: Border Collie; average weight: 30-45 lbs; average height: 18-22 inches |
+| Q: What is this dog breed and what is its average weight & height? :::image type="content" source="../media/concepts/gpt-v/pomeranian.jpg" lightbox="../media/concepts/gpt-v/pomeranian.jpg" alt-text="Photograph of a pomeranian dog sitting on a pool diving board above the water."::: <br> A: Breed: pomeranian; average weight: 3-7 lbs; average height: 8-14 inches <br> :::image type="content" source="../media/concepts/gpt-v/bulldog.jpg" lightbox="../media/concepts/gpt-v/bulldog.jpg" alt-text="Photograph of a bulldog laying in a grass field."::: A: Breed: English bulldog; average weight: 50-55 lbs; average height: 14-16 inches :::image type="content" source="../media/concepts/gpt-v/border-collie.jpg" lightbox="../media/concepts/gpt-v/border-collie.jpg" alt-text="Photograph of a border collie dog laying in a grass field."::: | `A: Breed: Border Collie; average weight: 30-45 lbs; average height: 18-22 inches` |
 
 ### Break down requests
 
 |Basic prompt|Model output|
 |--------|-----------|
-| :::image type="content" source="../media/concepts/gpt-v/grandmas-recipe.jpg" lightbox="../media/concepts/gpt-v/grandmas-recipe.jpg" alt-text="Photograph of a recipe card detailing a recipe for Grandma's Apple Crumb Pie."::: What's in this image? | The image contains a recipe card titled Grandma's Apple Crumb Pie. It is noted as a Thanksgiving tradition. The ingredients listed are:<br><ul><li>5-7 apples, peeled </li> <li>1 9-inch pie crust</li> <li>	3/4 cups sugar </li> <li>1/2 teaspoon cinnamon </li> <li>A dash of salt </li><li>	3 tablespoons butter</li> </ul><br>Please note that the measurements for sugar, flour, and butter are not fully visible in the image. If needed, the standard measures for a crumb topping typically involve a combination of sugar, flour, and butter mixed together until crumbly|
+| :::image type="content" source="../media/concepts/gpt-v/grandmas-recipe.jpg" lightbox="../media/concepts/gpt-v/grandmas-recipe.jpg" alt-text="Photograph of a recipe card detailing a recipe for Grandma's Apple Crumb Pie."::: What's in this image? | `The image contains a recipe card titled Grandma's Apple Crumb Pie. It is noted as a Thanksgiving tradition. The ingredients listed are:<br><ul><li>5-7 apples, peeled </li> <li>1 9-inch pie crust</li> <li>	3/4 cups sugar </li> <li>1/2 teaspoon cinnamon </li> <li>A dash of salt </li><li>	3 tablespoons butter</li> </ul><br>Please note that the measurements for sugar, flour, and butter are not fully visible in the image. If needed, the standard measures for a crumb topping typically involve a combination of sugar, flour, and butter mixed together until crumbly`|
 
 |Updated prompt|Model output|
 |----------|-----------|
@@ -88,11 +88,11 @@ When you receive a refusal output from a model, there are several ways you can t
 
 |Basic prompt|Model output|
 |----------|-----------|
-|:::image type="content" source="../media/concepts/gpt-v/rental-listing.jpg" lightbox="../media/concepts/gpt-v/rental-listing.jpg" alt-text="Photograph of a white villa in a tropical region with a pool."::: Describe this image | The image shows a luxurious white villa with a traditional thatched roof on one section and a clear blue swimming pool in the foreground. There are sun loungers lined up along the poolside, inviting relaxation. The architecture includes elegant archways, a balcony with wooden railings, and large windows and doors that suggest a bright and airy interior. The villa is surrounded by lush tropical vegetation, including tall palm trees, which adds to the serene and exotic ambiance of the setting. |
+|:::image type="content" source="../media/concepts/gpt-v/rental-listing.jpg" lightbox="../media/concepts/gpt-v/rental-listing.jpg" alt-text="Photograph of a white villa in a tropical region with a pool."::: Describe this image | `The image shows a luxurious white villa with a traditional thatched roof on one section and a clear blue swimming pool in the foreground. There are sun loungers lined up along the poolside, inviting relaxation. The architecture includes elegant archways, a balcony with wooden railings, and large windows and doors that suggest a bright and airy interior. The villa is surrounded by lush tropical vegetation, including tall palm trees, which adds to the serene and exotic ambiance of the setting.` |
 
 |Updated prompt|Model output|
 |----------|-----------|
-|:::image type="content" source="../media/concepts/gpt-v/rental-listing.jpg" lightbox="../media/concepts/gpt-v/rental-listing.jpg" alt-text="Photograph of a white villa in a tropical region with a pool."::: You are an AI assistant that writes detailed listings for vacation rental properties. Summarize in three sentences the optimal features of the listing, based on only what is visible in the image. Conclude with one sentence on why the customer should book today. | This idyllic villa showcases an inviting private pool with steps for easy access, framed by a beautifully tiled sun deck and comfortable loungers ideal for soaking up the sun or enjoying a good book under the shade of tropical palms. Architectural charm is evident in the villa's design, featuring elegant archways, a thatched-roof gazebo, and balconies that promise serene views and a perfect spot to savor your morning or evening beverages. The lush greenery surrounding the property not only provides privacy but also enhances the sense of an exclusive paradise retreat. Don't miss the opportunity to secure this perfect blend of luxury and tranquility – book today for a tropical escape like no other! |
+|:::image type="content" source="../media/concepts/gpt-v/rental-listing.jpg" lightbox="../media/concepts/gpt-v/rental-listing.jpg" alt-text="Photograph of a white villa in a tropical region with a pool."::: You are an AI assistant that writes detailed listings for vacation rental properties. Summarize in three sentences the optimal features of the listing, based on only what is visible in the image. Conclude with one sentence on why the customer should book today. | `This idyllic villa showcases an inviting private pool with steps for easy access, framed by a beautifully tiled sun deck and comfortable loungers ideal for soaking up the sun or enjoying a good book under the shade of tropical palms. Architectural charm is evident in the villa's design, featuring elegant archways, a thatched-roof gazebo, and balconies that promise serene views and a perfect spot to savor your morning or evening beverages. The lush greenery surrounding the property not only provides privacy but also enhances the sense of an exclusive paradise retreat. Don't miss the opportunity to secure this perfect blend of luxury and tranquility – book today for a tropical escape like no other!` |
 
 
-These guidelines and examples demonstrate how tailored system prompts can significantly enhance the performance of GPT-4 Turbo with Vision, ensuring that the responses are not only accurate but also perfectly suited to the specific context of the task at hand.
+These guidelines and examples demonstrate how tailored system prompts can significantly enhance the performance of vision-enabled models, ensuring that the responses are not only accurate but also perfectly suited to the specific context of the task at hand.

@@ -1,5 +1,5 @@
 ---
-title: GPT-4 Turbo with Vision concepts
+title: Vision-enabled chat model concepts
 titleSuffix: Azure OpenAI
 description: Learn concepts related to using images in your AI model chats, enabled through GPT-4 Turbo with Vision and other models.
 author: PatrickFarley
@@ -10,23 +10,23 @@ ms.date: 09/24/2024
 manager: nitinme
 ---
 
-# GPT-4 Turbo with Vision concepts
+# Vision-enabled chat model concepts
 
-GPT-4 Turbo with Vision is a large multimodal model (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. It incorporates both natural language processing and visual understanding. This guide provides details on the capabilities and limitations of GPT-4 Turbo with Vision.
+Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are GPT-4 Turbo with Vision, GPT-4o, and GPT-4o-mini. This guide provides details on their capabilities and limitations.
 
-To try out GPT-4 Turbo with Vision, see the [quickstart](/azure/ai-services/openai/gpt-v-quickstart).
+To try out vision-enabled chat models, see the [quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
-## Chats with vision
+## Vision-enabled chats
 
-The GPT-4 Turbo with Vision model answers general questions about what's present in the images or videos you upload.
+The vision-enabled models answer general questions about what's present in the images or videos you upload.
 
 
 ## Special pricing information
 
 > [!IMPORTANT]
 > Pricing details are subject to change in the future.
 
-GPT-4 Turbo with Vision accrues charges like other Azure OpenAI chat models. You pay a per-token rate for the prompts and completions, detailed on the [Pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/). The base charges and additional features are outlined here:
+Vision-enabled models accrue charges like other Azure OpenAI chat models. You pay a per-token rate for the prompts and completions, detailed on the [Pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/). The base charges and additional features are outlined here:
 
 Base Pricing for GPT-4 Turbo with Vision is:
 - Input: $0.01 per 1000 tokens
@@ -71,7 +71,7 @@ Additionally, there's a one-time indexing cost of $0.15 to generate the Video Re
 
 ## Input limitations
 
-This section describes the limitations of GPT-4 Turbo with Vision.
+This section describes the limitations of vision-enabled chat models.
 
 ### Image support
 
@@ -89,6 +89,6 @@ This section describes the limitations of GPT-4 Turbo with Vision.
 
 ## Next steps
 
-- Get started using GPT-4 Turbo with Vision by following the [quickstart](/azure/ai-services/openai/gpt-v-quickstart).
+- Get started using vision-enabled models by following the [quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 - For a more in-depth look at the APIs, and to use video prompts in chat, follow the [how-to guide](../how-to/gpt-with-vision.md).
 - See the [completions and embeddings API reference](../reference.md)

@@ -4,7 +4,7 @@ titleSuffix: Azure OpenAI
 description: Learn about the different model capabilities that are available with Azure OpenAI.
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 12/05/2024
+ms.date: 12/16/2024
 ms.custom: references_regions, build-2023, build-2023-dataai, refefences_regions
 manager: nitinme
 author: mrbullwinkle #ChrisHMSFT
@@ -249,8 +249,9 @@ You need to [create](../how-to/create-resource.md) or use an existing resource i
 
 When your resource is created, you can [deploy](../how-to/create-resource.md#deploy-a-model) the GPT-4o models. If you are performing a programmatic deployment, the **model** names are:
 
+- `gpt-4o` **Version** `2024-11-20`
 - `gpt-4o` **Version** `2024-08-06`
-- `gpt-4o`, **Version**  `2024-05-13`
+- `gpt-4o` **Version** `2024-05-13`
 - `gpt-4o-mini` **Version** `2024-07-18`
 
 ### GPT-4 Turbo
@@ -277,7 +278,8 @@ See [model versions](../concepts/model-versions.md) to learn about how Azure Ope
 
 |  Model ID  | Description | Max Request (tokens) | Training Data (up to)  |
 |  --- |  :--- |:--- |:---: |
-|`gpt-4o` (2024-08-06) <br> **GPT-4o (Omni)** | **Latest large GA model** <br> - Structured outputs<br> - Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks |Input: 128,000  <br> Output: 16,384 | Oct 2023 |
+| `gpt-4o` (2024-11-20) <br> **GPT-4o (Omni)**  | **Latest large GA model** <br> - Structured outputs<br> - Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks. <br> - **Enhanced creative writing ability** | Input: 128,000  <br> Output: 16,384 | Oct 2023 |
+|`gpt-4o` (2024-08-06) <br> **GPT-4o (Omni)** | - Structured outputs<br> - Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks |Input: 128,000  <br> Output: 16,384 | Oct 2023 |
 |`gpt-4o-mini` (2024-07-18) <br> **GPT-4o mini** | **Latest small GA model** <br> - Fast, inexpensive, capable model ideal for replacing GPT-3.5 Turbo series models. <br> - Text, image processing <br>- JSON Mode <br> - parallel function calling | Input: 128,000 <br> Output: 16,384  | Oct 2023 |
 |`gpt-4o` (2024-05-13) <br> **GPT-4o (Omni)** | Text, image processing <br> - JSON Mode <br> - parallel function calling <br> - Enhanced accuracy and responsiveness <br> - Parity with English text and coding tasks compared to GPT-4 Turbo with Vision <br> - Superior performance in non-English languages and in vision tasks |Input: 128,000  <br> Output: 4,096| Oct 2023 |
 | `gpt-4` (turbo-2024-04-09) <br>**GPT-4 Turbo with Vision** | **New GA model** <br> - Replacement for all previous GPT-4 preview models (`vision-preview`, `1106-Preview`, `0125-Preview`). <br> - [**Feature availability**](#gpt-4o-and-gpt-4-turbo) is currently different depending on method of input, and deployment type. | Input: 128,000  <br> Output: 4,096  | Dec 2023 |

@@ -1,7 +1,7 @@
 ---
-title: 'Quickstart: Use GPT-4 Turbo with Vision on your images and videos with the Azure OpenAI Service'
+title: 'Quickstart: Use vision-enabled models with the Azure OpenAI Service'
 titleSuffix: Azure OpenAI
-description: Use this article to get started using Azure OpenAI to deploy and use the GPT-4 Turbo with Vision model. 
+description: Use this article to get started using Azure OpenAI to deploy and use the GPT-4 Turbo with Vision model or other vision-enabled models. 
 services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -60,6 +60,6 @@ Get started using GPT-4 Turbo with images with the Azure OpenAI Service.
 
 ## Next steps
 
-* Learn more about these APIs in the [GPT-4 Turbo with Vision how-to guide](./gpt-v-quickstart.md)
+* Learn more about these APIs in the [Vision-enabled models how-to guide](./gpt-v-quickstart.md)
 * [GPT-4 Turbo with Vision frequently asked questions](./faq.yml#gpt-4-turbo-with-vision)
 * [GPT-4 Turbo with Vision API reference](https://aka.ms/gpt-v-api-ref)

@@ -5,9 +5,9 @@ description: Learn how to create your own customized model with Azure OpenAI Ser
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
-ms.custom: build-2023, build-2023-dataai, devx-track-python
+ms.custom: build-2023, build-2023-dataai, devx-track-python, references_regions
 ms.topic: how-to
-ms.date: 11/11/2024
+ms.date: 12/13/2024
 author: mrbullwinkle
 ms.author: mbullwin
 zone_pivot_groups: openai-fine-tuning
@@ -44,6 +44,20 @@ We use LoRA, or low rank approximation, to fine-tune models in a way that reduce
 
 ::: zone-end
 
+## Global Standard
+
+Azure OpenAI fine-tuning supports [global standard deployments](./deployment-types.md#global-standard) in East US2, North Central US, and Sweden Central for:
+
+- `gpt-4o-2024-08-06`
+- `gpt-4o-mini-2024-07-18`
+
+Global standard fine-tuned deployments offer [cost savings](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/), but custom model weights may temporarily be stored outside the geography of your Azure OpenAI resource.
+
+:::image type="content" source="../media/fine-tuning/global-standard.png" alt-text="Screenshot of the global standard deployment user experience with a fine-tuned model." lightbox="../media/fine-tuning/global-standard.png":::
+
+Global Standard fine-tuning deployments currently do not support vision and structured outputs.
+
+
 ## Vision fine-tuning
 
 Fine-tuning is also possible with images in your JSONL files. Just as you can send one or many image inputs to chat completions, you can include those same message types within your training data. Images can be provided either as publicly accessible URLs or data URIs containing [base64 encoded images](/azure/ai-services/openai/how-to/gpt-with-vision?tabs=rest#call-the-chat-completion-apis). 
@@ -79,6 +93,10 @@ Images containing the following will be excluded from your dataset and not used
 > [!IMPORTANT]
 >For  vision fine tuning face screening process: We screen for faces/people to skip those images from training the model. The screening capability leverages face detection **WITHOUT** Face identification which means we don't create facial templates or measure specific facial geometry, and the technology used to screen for faces is incapable of uniquely identifying the individuals. To know more about data and Privacy for face refer to - [Data and privacy for Face - Azure AI services | Microsoft Learn](/legal/cognitive-services/computer-vision/imageanalysis-data-privacy-security?context=%2Fazure%2Fai-services%2Fcomputer-vision%2Fcontext%2Fcontext).
 
+## Prompt caching
+
+Azure OpenAI fine-tuning supports prompt caching with select models. Prompt caching allows you to reduce overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. To learn more about prompt caching, see [getting started with prompt caching](./prompt-caching.md).
+
 ## Troubleshooting
 
 ### How do I enable fine-tuning?

@@ -1,7 +1,7 @@
 ---
-title: How to use the GPT-4 Turbo with Vision model
+title: How to use vision-enabled chat models
 titleSuffix: Azure OpenAI Service
-description: Learn about the options for using GPT-4 Turbo with Vision
+description: Learn about the options for using vision-enabled chat models
 author: PatrickFarley #dereklegenzoff
 ms.author: pafarley #delegenz
 ms.service: azure-ai-openai
@@ -10,15 +10,15 @@ ms.date: 08/21/2024
 manager: nitinme
 ---
 
-# Use GPT-4 Turbo with Vision
+# Use vision-enabled chat models
 
 
-GPT-4 Turbo with Vision is a large multimodal model (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. It incorporates both natural language processing and visual understanding.
+Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are GPT-4 Turbo with Vision, GPT-4o, and GPT-4o-mini.
 
-The GPT-4 Turbo with Vision model answers general questions about what's present in images.
+The vision-enabled models answer general questions about what's present in the images or videos you upload.
 
 > [!TIP]
-> To use GPT-4 Turbo with Vision, you call the Chat Completion API on a GPT-4 Turbo with Vision model that you have deployed. If you're not familiar with the Chat Completion API, see the [GPT-4 Turbo & GPT-4 how-to guide](/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions).
+> To use vision-enabled models, you call the Chat Completion API on a supported model that you have deployed. If you're not familiar with the Chat Completion API, see the [Vision-enabled chat how-to guide](/azure/ai-services/openai/how-to/chatgpt?tabs=python&pivots=programming-language-chat-completions).
 
 ## GPT-4 Turbo model upgrade
 
@@ -371,6 +371,6 @@ Every response includes a `"finish_reason"` field. It has the following possible
 ## Next steps
 
 * [Learn more about Azure OpenAI](../overview.md).
-* [GPT-4 Turbo with Vision quickstart](../gpt-v-quickstart.md)
+* [Vision-enabled chats quickstart](../gpt-v-quickstart.md)
 * [GPT-4 Turbo with Vision frequently asked questions](../faq.yml#gpt-4-turbo-with-vision)
 * [GPT-4 Turbo with Vision API reference](https://aka.ms/gpt-v-api-ref)

@@ -6,15 +6,15 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 10/18/2024
+ms.date: 12/15/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
 ---
 
 # Prompt caching
 
-Prompt caching allows you to reduce overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. *"Prompt"* in this context is referring to the input you send to the model as part of your chat completions request. Rather than reprocess the same input tokens over and over again, the service is able to retain a temporary cache of processed input token computations to improve overall performance. Prompt caching has no impact on the output content returned in the model response beyond a reduction in latency and cost. For supported models, cached tokens are billed at a [50% discount on input token pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) for Standard deployment types and up to [100% discount on input tokens](/azure/ai-services/openai/concepts/provisioned-throughput) for Provisioned deployment types. 
+Prompt caching allows you to reduce overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. *"Prompt"* in this context is referring to the input you send to the model as part of your chat completions request. Rather than reprocess the same input tokens over and over again, the service is able to retain a temporary cache of processed input token computations to improve overall performance. Prompt caching has no impact on the output content returned in the model response beyond a reduction in latency and cost. For supported models, cached tokens are billed at a [discount on input token pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) for Standard deployment types and up to [100% discount on input tokens](/azure/ai-services/openai/concepts/provisioned-throughput) for Provisioned deployment types.
 
 Caches are typically cleared within 5-10 minutes of inactivity and are always removed within one hour of the cache's last use. Prompt caches are not shared between Azure subscriptions. 
 
@@ -28,6 +28,9 @@ Currently only the following models support prompt caching with Azure OpenAI:
 - `gpt-4o-2024-08-06`
 - `gpt-4o-mini-2024-07-18`
 
+> [!NOTE]
+> Prompt caching is now also available as part of model fine-tuning for `gpt-4o` and `gpt-4o-mini`. Refer to the fine-tuning section of the [pricing page](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) for details.
+
 ## API support
 
 Official support for prompt caching was first added in API version `2024-10-01-preview`. At this time, only the o1 model family supports the `cached_tokens` API response parameter.

@@ -0,0 +1,188 @@
+---
+title: 'How to use Azure OpenAI Service stored completions & distillation'
+titleSuffix: Azure OpenAI
+description: Learn how to use stored completions & distillation with Azure OpenAI
+manager: nitinme
+ms.service: azure-ai-openai
+ms.topic: how-to
+ms.custom: references_regions
+ms.date: 12/12/2024
+author: mrbullwinkle
+ms.author: mbullwin
+recommendations: false
+---
+
+# Azure OpenAI stored completions & distillation (preview)
+
+Stored completions allow you to capture the conversation history from chat completions sessions to use as datasets for [evaluations](./evaluations.md) and [fine-tuning](./fine-tuning.md).
+
+## Stored completions support
+
+### API support
+
+- `2024-10-01-preview`
+
+### Model support
+
+- `gpt-4o-2024-08-06`
+
+### Regional availability
+
+- Sweden Central
+
+## Configure stored completions
+
+To enable stored completions for your Azure OpenAI deployment set the `store` parameter to `True`. Use the `metadata` parameter to enrich your stored completion dataset with additional information.
+
+
+# [Python (Microsoft Entra ID)](#tab/python-secure)
+
+```python
+import os
+from openai import AzureOpenAI
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+
+token_provider = get_bearer_token_provider(
+    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
+)
+
+client = AzureOpenAI(
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
+  azure_ad_token_provider=token_provider,
+  api_version="2024-10-01-preview"
+)
+
+completion = client.chat.completions.create(
+    
+    model="gpt-4o", # replace with model deployment name
+    store= True,
+    metadata =  {
+    "user": "admin",
+    "category": "docs-test",
+  },
+    messages=[
+    {"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."},
+    {"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data."}
+    ]   
+)
+
+print(completion.choices[0].message)
+
+
+```
+
+# [Python (API Key)](#tab/python-key)
+
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+```python
+import os
+from openai import AzureOpenAI
+    
+client = AzureOpenAI(
+    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
+    api_version="2024-10-01-preview",
+    azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
+    )
+
+ompletion = client.chat.completions.create(
+    
+    model="gpt-4o", # replace with model deployment name
+    store= True,
+    metadata =  {
+    "user": "admin",
+    "category": "docs-test",
+  },
+    messages=[
+    {"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."},
+    {"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data."}
+    ]   
+)
+
+print(completion.choices[0].message)
+```
+---
+
+Once stored completions are enabled for an Azure OpenAI deployment, they'll begin to show up in the [Azure AI Foundry portal](https://oai.azure.com) in the **Stored Completions** pane.
+
+:::image type="content" source="../media/stored-completions/stored-completions.png" alt-text="Screenshot of the stored completions User Experience." lightbox="../media/stored-completions/stored-completions.png":::
+
+## Distillation
+
+Distillation allows you to turn your stored completions into a fine-tuning dataset. A common use case is to use stored completions with a larger more powerful model for a particular task and then use the stored completions to train a smaller model on high quality examples of model interactions.
+
+Distillation requires a minimum of 10 stored completions, though it's recommended to provide hundreds to thousands of stored completions for the best results.
+
+1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://oai.azure.com) use the **Filter** options to select the completions you want to train your model with.
+
+2. To begin distillation, select **Distill**
+
+    :::image type="content" source="../media/stored-completions/distill.png" alt-text="Screenshot of the stored completions User Experience with distill highlighted." lightbox="../media/stored-completions/distill.png":::
+
+3. Pick which model you would like to fine-tune with your stored completion dataset.
+
+    :::image type="content" source="../media/stored-completions/fine-tune.png" alt-text="Screenshot of the stored completion distillation model selection." lightbox="../media/stored-completions/fine-tune.png":::
+
+4. Confirm which version of the model you want to fine-tune:
+
+    :::image type="content" source="../media/stored-completions/version.png" alt-text="Screenshot of the stored completion distillation version." lightbox="../media/stored-completions/version.png":::
+
+5. A `.jsonl` file with a randomly generated name will be created as a training dataset from your stored completions. Select the file > **Next**.
+
+    > [!Note]
+    > Stored completion distillation training files cannot be accessed directly and cannot be exported externally/downloaded.
+
+    :::image type="content" source="../media/stored-completions/file-name.png" alt-text="Screenshot of the stored completion training dataset jsonl file." lightbox="../media/stored-completions/file-name.png":::
+
+The rest of the steps correspond to the typical Azure OpenAI fine-tuning steps. To learn more, see our [fine-tuning getting started guide](./fine-tuning.md).
+
+## Evaluation
+
+The [evaluation](./evaluations.md) of large language models is a critical step in measuring their performance across various tasks and dimensions. This is especially important for fine-tuned models, where assessing the performance gains (or losses) from training is crucial. Thorough evaluations can help your understanding of how different versions of the model may impact your application or scenario.
+
+Stored completions can be used as a dataset for running evaluations.
+
+1. From the **Stored Completions** pane in the [Azure AI Foundry portal](https://oai.azure.com) use the **Filter** options to select the completions you want to be part of your evaluation dataset.
+
+2. To configure the evaluation, select **Evaluate**
+
+    :::image type="content" source="../media/stored-completions/evaluate.png" alt-text="Screenshot of the stored completion pane with evaluate selected." lightbox="../media/stored-completions/evaluate.png":::
+
+3. This launches the **Evaluations** pane with a prepopulated `.jsonl` file with a randomly generated name that is created as an evaluation dataset from your stored completions.
+
+    > [!Note]
+    > Stored completion evaluation data files cannot be accessed directly and cannot be exported externally/downloaded.
+
+    :::image type="content" source="../media/stored-completions/evaluation-data.png" alt-text="Screenshot of the evaluations pane." lightbox="../media/stored-completions/evaluation-data.png":::
+
+To learn more about evaluation see, [getting started with evaluations](./evaluations.md)
+
+## Troubleshooting
+
+### Do I need special permissions to use stored completions?
+
+Stored completions access is controlled via two DataActions:
+
+- `Microsoft.CognitiveServices/accounts/OpenAI/stored-completions/read`
+- `Microsoft.CognitiveServices/accounts/OpenAI/stored-completions/action`
+
+By default `Cognitive Services OpenAI Contributor` has access to both these permissions:
+
+:::image type="content" source="../media/stored-completions/permissions.png" alt-text="Screenshot of stored completions permissions." lightbox="../media/stored-completions/permissions.png":::
+
+### How do I delete stored data?
+
+Data can be deleted by deleting the associated Azure OpenAI resource. If you wish to only delete stored completion data you must open a case with customer support.
+
+### How much stored completion data can I store?
+
+You can store a maximum 10 GB of data.
+
+### Can I prevent stored completions from ever being enabled on a subscription?
+
+You'll need to open a case with customer support to disable stored completions at the subscription level.
+
+### TypeError: Completions.create() got an unexpected argument 'store'
+
+This error occurs when you're running an older version of the OpenAI client library that predates the stored completions feature being released. Run  `pip install openai --upgrade`.
+

@@ -26,4 +26,4 @@ ms.date: 10/25/2024
 | uksouth          | -                          | -                       | -                      | -                      | -                           | -               | ✅                        | ✅                        | -                         | -                           | -                   | ✅                       | ✅                       | ✅                       | ✅                       | ✅                           |
 | westeurope       | -                          | -                       | -                      | -                      | -                           | -               | -                       | -                       | -                         | -                           | -                   | ✅                       | -                      | -                      | -                      | -                          |
 | westus           | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -               | ✅                        | -                       | ✅                          | ✅                            | -                   | -                      | -                      | ✅                       | ✅                       | -                          |
-| westus3          | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -               | ✅                        | -                       | -                         | ✅                            | -                   | -                      | -                      | -                      | ✅                       | -                          |
+| westus3          | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -               | ✅                        | -                       | -                         | ✅                            | -                   | -                      | -                      | -                      | ✅                       | -                          |
\ No newline at end of file

@@ -8,28 +8,28 @@ ms.topic: include
 ms.date: 10/25/2024
 ---
 
-| **Region**     | **o1-preview**, **2024-09-12**   | **o1-mini**, **2024-09-12**   | **gpt-4o**, **2024-05-13**   | **gpt-4o**, **2024-08-06**   | **gpt-4o-mini**, **2024-07-18**   | **gpt-4o-realtime-preview**, **2024-10-01**   | **gpt-4**, **turbo-2024-04-09**   |
-|:-------------------|:------------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|:-------------------------------------------:|:-------------------------------:|
-| australiaeast      | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| brazilsouth        | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| canadaeast         | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| eastus             | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| eastus2            | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | ✅                                        | ✅                            |
-| francecentral      | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| germanywestcentral | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| japaneast          | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| koreacentral       | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| northcentralus     | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| norwayeast         | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| polandcentral      | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| southafricanorth   | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| southcentralus     | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| southindia         | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| spaincentral       | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| swedencentral      | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | ✅                                        | ✅                            |
-| switzerlandnorth   | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| uaenorth           | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| uksouth            | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| westeurope         | -                          | -                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| westus             | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
-| westus3            | ✅                           | ✅                        | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
+| **Region**     | **o1-preview**, **2024-09-12**   | **o1-mini**, **2024-09-12**   | **gpt-4o**, **2024-05-13**   | **gpt-4o**, **2024-08-06**   | **gpt-4o**, **2024-11-20**   | **gpt-4o-mini**, **2024-07-18**   | **gpt-4o-realtime-preview**, **2024-10-01**   | **gpt-4**, **turbo-2024-04-09**   |
+|:-------------------|:------------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|:-------------------------------------------:|:-------------------------------:|
+| australiaeast      | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| brazilsouth        | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| canadaeast         | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| eastus             | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
+| eastus2            | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | ✅                                        | ✅                            |
+| francecentral      | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| germanywestcentral | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| japaneast          | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| koreacentral       | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| northcentralus     | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
+| norwayeast         | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| polandcentral      | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| southafricanorth   | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| southcentralus     | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
+| southindia         | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| spaincentral       | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| swedencentral      | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | ✅                                        | ✅                            |
+| switzerlandnorth   | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| uaenorth           | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| uksouth            | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| westeurope         | -                          | -                       | ✅                       | ✅                       | -                      | ✅                            | -                                       | ✅                            |
+| westus             | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
+| westus3            | ✅                           | ✅                        | ✅                       | ✅                       | ✅                       | ✅                            | -                                       | ✅                            |
\ No newline at end of file

@@ -5,9 +5,10 @@ description: Quota and limits for Azure OpenAI by region.
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 10/25/2024
+ms.date: 12/16/2024
 ---
 
+
 | **Region**   | **o1-preview**, **2024-09-12**   | **o1-mini**, **2024-09-12**   | **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**, **vision-preview**   | **gpt-4**, **turbo-2024-04-09**   | **gpt-4-32k**, **0613**   | **gpt-35-turbo**, **0301**   | **gpt-35-turbo**, **0613**   | **gpt-35-turbo**, **1106**   | **gpt-35-turbo**, **0125**   | **gpt-35-turbo-16k**, **0613**   | **gpt-35-turbo-instruct**, **0914**   | **text-embedding-3-small**, **1**   | **text-embedding-3-large**, **1**   | **text-embedding-ada-002**, **1**   | **text-embedding-ada-002**, **2**   | **dall-e-2**, **2.0**   | **dall-e-3**, **3.0**   | **babbage-002**, **1**   | **davinci-002**, **1**   | **tts**, **001**   | **tts-hd**, **001**   | **whisper**, **001**   |
 |:-----------------|:------------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|:-------------------:|:---------------------------:|:---------------------------:|:-----------------------------:|:-------------------------------:|:-----------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:------------------------------:|:-----------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------------------:|:---------------------:|:---------------------:|:----------------------:|:----------------------:|:----------------:|:-------------------:|:--------------------:|
 | australiaeast    | -                          | -                       | -                      | -                      | -                           | ✅                | ✅                        | -                       | ✅                          | -                           | ✅                    | -                      | ✅                       | ✅                       | ✅                       | ✅                           | -                               | ✅                              | ✅                              | -                             | ✅                              | -                 | ✅                  | -                  | -                  | -            | -               | -                |

@@ -40,7 +40,7 @@ landingContent:
         links:
            - text: Chat completions
              url: chatgpt-quickstart.md
-           - text: GPT-4 Turbo with Vision
+           - text: Vision-enabled models
              url: gpt-v-quickstart.md  
            - text: DALL-E
              url: dall-e-quickstart.md
@@ -98,7 +98,7 @@ landingContent:
                url: ./how-to/role-based-access-control.md 
              - text: GPT-3.5 Turbo & GPT-4
                url:  ./how-to/chatgpt.md
-             - text: GPT-4 Turbo with Vision
+             - text: Vision-enabled models
                url:  ./how-to/gpt-with-vision.md
              - text: Provisioned Throughput Units (PTU) 
                url: ./how-to/provisioned-throughput-onboarding.md

@@ -22,7 +22,7 @@ Azure OpenAI Service provides REST API access to OpenAI's powerful language mode
 | --- | --- |
 | Models available | **o1-preview** & **o1-mini** - (Limited Access - [Request Access](https://aka.ms/oai/modelaccess))<br>**GPT-4o & GPT-4o mini**<br> **GPT-4 series (including GPT-4 Turbo with Vision)** <br>**GPT-3.5-Turbo series**<br> Embeddings series <br> Learn more in our [Models](./concepts/models.md) page.|
 | Fine-tuning | `GPT-4o-mini` (preview) <br> `GPT-4` (preview) <br>`GPT-3.5-Turbo` (0613) <br> `babbage-002` <br> `davinci-002`.|
-| Price | [Available here](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> For details on GPT-4 Turbo with Vision, see the [special pricing information](../openai/concepts/gpt-with-vision.md#special-pricing-information).|
+| Price | [Available here](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) <br> For details on vision-enabled chat models, see the [special pricing information](../openai/concepts/gpt-with-vision.md#special-pricing-information).|
 | Virtual network support & private link support | Yes.  |
 | Managed Identity| Yes, via Microsoft Entra ID | 
 | UI experience | [Azure portal](https://portal.azure.com) for account & resource management, <br> [Azure AI Foundry](https://ai.azure.com) for model exploration and fine-tuning |

@@ -54,15 +54,15 @@ There are nine client events that can be sent from the client to the server:
 
 | Event | Description |
 |-------|-------------|
-| [RealtimeClientEventConversationItemCreate](#realtimeclienteventconversationitemcreate) | Send this client event when adding an item to the conversation. |
-| [RealtimeClientEventConversationItemDelete](#realtimeclienteventconversationitemdelete) | Send this client event when you want to remove any item from the conversation history. |
-| [RealtimeClientEventConversationItemTruncate](#realtimeclienteventconversationitemtruncate) | Send this client event when you want to truncate a previous assistant message's audio. |
-| [RealtimeClientEventInputAudioBufferAppend](#realtimeclienteventinputaudiobufferappend) | Send this client event to append audio bytes to the input audio buffer. |
-| [RealtimeClientEventInputAudioBufferClear](#realtimeclienteventinputaudiobufferclear) | Send this client event to clear the audio bytes in the buffer. |
-| [RealtimeClientEventInputAudioBufferCommit](#realtimeclienteventinputaudiobuffercommit) | Send this client event to commit audio bytes to a user message. |
-| [RealtimeClientEventResponseCancel](#realtimeclienteventresponsecancel) | Send this client event to cancel an in-progress response. |
-| [RealtimeClientEventResponseCreate](#realtimeclienteventresponsecreate) | Send this client event to trigger a response generation. |
-| [RealtimeClientEventSessionUpdate](#realtimeclienteventsessionupdate) | Send this client event to update the session's default configuration. |
+| [RealtimeClientEventConversationItemCreate](#realtimeclienteventconversationitemcreate) | The client `conversation.item.create` event is used to add a new item to the conversation's context, including messages, function calls, and function call responses. |
+| [RealtimeClientEventConversationItemDelete](#realtimeclienteventconversationitemdelete) | The client `conversation.item.delete` event is used to remove an item from the conversation history. |
+| [RealtimeClientEventConversationItemTruncate](#realtimeclienteventconversationitemtruncate) | The client `conversation.item.truncate` event is used to truncate a previous assistant message's audio. |
+| [RealtimeClientEventInputAudioBufferAppend](#realtimeclienteventinputaudiobufferappend) | The client `input_audio_buffer.append` event is used to append audio bytes to the input audio buffer. |
+| [RealtimeClientEventInputAudioBufferClear](#realtimeclienteventinputaudiobufferclear) | The client `input_audio_buffer.clear` event is used to clear the audio bytes in the buffer. |
+| [RealtimeClientEventInputAudioBufferCommit](#realtimeclienteventinputaudiobuffercommit) | The client `input_audio_buffer.commit` event is used to commit the user input audio buffer. |
+| [RealtimeClientEventResponseCancel](#realtimeclienteventresponsecancel) | The client `response.cancel` event is used to cancel an in-progress response. |
+| [RealtimeClientEventResponseCreate](#realtimeclienteventresponsecreate) | The client `response.create` event is used to instruct the server to create a response via model inferencing. |
+| [RealtimeClientEventSessionUpdate](#realtimeclienteventsessionupdate) | The client `session.update` event is used to update the session's default configuration. |
 
 ### RealtimeClientEventConversationItemCreate
 
@@ -143,7 +143,7 @@ The client `input_audio_buffer.append` event is used to append audio bytes to th
 
 In Server VAD (Voice Activity Detection) mode, the audio buffer is used to detect speech and the server decides when to commit. When server VAD is disabled, the client can choose how much audio to place in each event up to a maximum of 15 MiB. For example, streaming smaller chunks from the client can allow the VAD to be more responsive. 
 
-Unlike made other client events, the server doesn't send a confirmation response to client `input_audio_buffer.append` event.
+Unlike most other client events, the server doesn't send a confirmation response to client `input_audio_buffer.append` event.
 
 #### Event structure
 
@@ -281,34 +281,34 @@ There are 28 server events that can be received from the server:
 
 | Event | Description |
 |-------|-------------|
-| [RealtimeServerEventConversationCreated](#realtimeservereventconversationcreated) | Server event when a conversation is created. Emitted right after session creation. |
-| [RealtimeServerEventConversationItemCreated](#realtimeservereventconversationitemcreated) | Server event when a conversation item is created. |
-| [RealtimeServerEventConversationItemDeleted](#realtimeservereventconversationitemdeleted) | Server event when an item in the conversation is deleted. |
-| [RealtimeServerEventConversationItemInputAudioTranscriptionCompleted](#realtimeservereventconversationiteminputaudiotranscriptioncompleted) | Server event when input audio transcription is enabled and a transcription succeeds. |
-| [RealtimeServerEventConversationItemInputAudioTranscriptionFailed](#realtimeservereventconversationiteminputaudiotranscriptionfailed) | Server event when input audio transcription is configured, and a transcription request for a user message failed. |
-| [RealtimeServerEventConversationItemTruncated](#realtimeservereventconversationitemtruncated) | Server event when the client truncates an earlier assistant audio message item. |
-| [RealtimeServerEventError](#realtimeservereventerror) | Server event when an error occurs. |
-| [RealtimeServerEventInputAudioBufferCleared](#realtimeservereventinputaudiobuffercleared) | Server event when the client clears the input audio buffer. |
-| [RealtimeServerEventInputAudioBufferCommitted](#realtimeservereventinputaudiobuffercommitted) | Server event when an input audio buffer is committed, either by the client or automatically in server VAD mode. |
-| [RealtimeServerEventInputAudioBufferSpeechStarted](#realtimeservereventinputaudiobufferspeechstarted) | Server event in server turn detection mode when speech is detected. |
-| [RealtimeServerEventInputAudioBufferSpeechStopped](#realtimeservereventinputaudiobufferspeechstopped) | Server event in server turn detection mode when speech stops. |
-| [RealtimeServerEventRateLimitsUpdated](#realtimeservereventratelimitsupdated) | Emitted after every "response.done" event to indicate the updated rate limits. |
-| [RealtimeServerEventResponseAudioDelta](#realtimeservereventresponseaudiodelta) | Server event when the model-generated audio is updated. |
-| [RealtimeServerEventResponseAudioDone](#realtimeservereventresponseaudiodone) | Server event when the model-generated audio is done. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventResponseAudioTranscriptDelta](#realtimeservereventresponseaudiotranscriptdelta) | Server event when the model-generated transcription of audio output is updated. |
-| [RealtimeServerEventResponseAudioTranscriptDone](#realtimeservereventresponseaudiotranscriptdone) | Server event when the model-generated transcription of audio output is done streaming. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventResponseContentPartAdded](#realtimeservereventresponsecontentpartadded) | Server event when a new content part is added to an assistant message item during response generation. |
-| [RealtimeServerEventResponseContentPartDone](#realtimeservereventresponsecontentpartdone) | Server event when a content part is done streaming in an assistant message item. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventResponseCreated](#realtimeservereventresponsecreated) | Server event when a new Response is created. The first event of response creation, where the response is in an initial state of "in_progress". |
-| [RealtimeServerEventResponseDone](#realtimeservereventresponsedone) | Server event when a response is done streaming. Always emitted, no matter the final state. |
-| [RealtimeServerEventResponseFunctionCallArgumentsDelta](#realtimeservereventresponsefunctioncallargumentsdelta) | Server event when the model-generated function call arguments are updated. |
-| [RealtimeServerEventResponseFunctionCallArgumentsDone](#realtimeservereventresponsefunctioncallargumentsdone) | Server event when the model-generated function call arguments are done streaming. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventResponseOutputItemAdded](#realtimeservereventresponseoutputitemadded) | Server event when a new output item is added to a response. |
-| [RealtimeServerEventResponseOutputItemDone](#realtimeservereventresponseoutputitemdone) | Server event when an output item is done streaming. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventResponseTextDelta](#realtimeservereventresponsetextdelta) | Server event when the model-generated text is updated. |
-| [RealtimeServerEventResponseTextDone](#realtimeservereventresponsetextdone) | Server event when the model-generated text is done. Also emitted when a response is interrupted, incomplete, or canceled. |
-| [RealtimeServerEventSessionCreated](#realtimeservereventsessioncreated) | Server event when a session is created. |
-| [RealtimeServerEventSessionUpdated](#realtimeservereventsessionupdated) | Server event when a session is updated. |
+| [RealtimeServerEventConversationCreated](#realtimeservereventconversationcreated) | The server `conversation.created` event is returned right after session creation. One conversation is created per session. |
+| [RealtimeServerEventConversationItemCreated](#realtimeservereventconversationitemcreated) | The server `conversation.item.created` event is returned when a conversation item is created. |
+| [RealtimeServerEventConversationItemDeleted](#realtimeservereventconversationitemdeleted) | The server `conversation.item.deleted` event is returned when the client deleted an item in the conversation with a `conversation.item.delete` event. |
+| [RealtimeServerEventConversationItemInputAudioTranscriptionCompleted](#realtimeservereventconversationiteminputaudiotranscriptioncompleted) | The server `conversation.item.input_audio_transcription.completed` event is the result of audio transcription for speech written to the audio buffer. |
+| [RealtimeServerEventConversationItemInputAudioTranscriptionFailed](#realtimeservereventconversationiteminputaudiotranscriptionfailed) | The server `conversation.item.input_audio_transcription.failed` event is returned when input audio transcription is configured, and a transcription request for a user message failed. |
+| [RealtimeServerEventConversationItemTruncated](#realtimeservereventconversationitemtruncated) | The server `conversation.item.truncated` event is returned when the client truncates an earlier assistant audio message item with a `conversation.item.truncate` event. |
+| [RealtimeServerEventError](#realtimeservereventerror) | The server `error` event is returned when an error occurs, which could be a client problem or a server problem. |
+| [RealtimeServerEventInputAudioBufferCleared](#realtimeservereventinputaudiobuffercleared) | The server `input_audio_buffer.cleared` event is returned when the client clears the input audio buffer with a `input_audio_buffer.clear` event. |
+| [RealtimeServerEventInputAudioBufferCommitted](#realtimeservereventinputaudiobuffercommitted) | The server `input_audio_buffer.committed` event is returned when an input audio buffer is committed, either by the client or automatically in server VAD mode. |
+| [RealtimeServerEventInputAudioBufferSpeechStarted](#realtimeservereventinputaudiobufferspeechstarted) | The server `input_audio_buffer.speech_started` event is returned in `server_vad` mode when speech is detected in the audio buffer. |
+| [RealtimeServerEventInputAudioBufferSpeechStopped](#realtimeservereventinputaudiobufferspeechstopped) | The server `input_audio_buffer.speech_stopped` event is returned in `server_vad` mode when the server detects the end of speech in the audio buffer. |
+| [RealtimeServerEventRateLimitsUpdated](#realtimeservereventratelimitsupdated) | The server `rate_limits.updated` event is emitted at the beginning of a response to indicate the updated rate limits. |
+| [RealtimeServerEventResponseAudioDelta](#realtimeservereventresponseaudiodelta) | The server `response.audio.delta` event is returned when the model-generated audio is updated. |
+| [RealtimeServerEventResponseAudioDone](#realtimeservereventresponseaudiodone) | The server `response.audio.done` event is returned when the model-generated audio is done. |
+| [RealtimeServerEventResponseAudioTranscriptDelta](#realtimeservereventresponseaudiotranscriptdelta) | The server `response.audio_transcript.delta` event is returned when the model-generated transcription of audio output is updated. |
+| [RealtimeServerEventResponseAudioTranscriptDone](#realtimeservereventresponseaudiotranscriptdone) | The server `response.audio_transcript.done` event is returned when the model-generated transcription of audio output is done streaming. |
+| [RealtimeServerEventResponseContentPartAdded](#realtimeservereventresponsecontentpartadded) | The server `response.content_part.added` event is returned when a new content part is added to an assistant message item. |
+| [RealtimeServerEventResponseContentPartDone](#realtimeservereventresponsecontentpartdone) | The server `response.content_part.done` event is returned when a content part is done streaming. |
+| [RealtimeServerEventResponseCreated](#realtimeservereventresponsecreated) | The server `response.created` event is returned when a new response is created. This is the first event of response creation, where the response is in an initial state of `in_progress`. |
+| [RealtimeServerEventResponseDone](#realtimeservereventresponsedone) | The server `response.done` event is returned when a response is done streaming. |
+| [RealtimeServerEventResponseFunctionCallArgumentsDelta](#realtimeservereventresponsefunctioncallargumentsdelta) | The server `response.function_call_arguments.delta` event is returned when the model-generated function call arguments are updated. |
+| [RealtimeServerEventResponseFunctionCallArgumentsDone](#realtimeservereventresponsefunctioncallargumentsdone) | The server `response.function_call_arguments.done` event is returned when the model-generated function call arguments are done streaming. |
+| [RealtimeServerEventResponseOutputItemAdded](#realtimeservereventresponseoutputitemadded) | The server `response.output_item.added` event is returned when a new item is created during response generation. |
+| [RealtimeServerEventResponseOutputItemDone](#realtimeservereventresponseoutputitemdone) | The server `response.output_item.done` event is returned when an item is done streaming. |
+| [RealtimeServerEventResponseTextDelta](#realtimeservereventresponsetextdelta) | The server `response.text.delta` event is returned when the model-generated text is updated. |
+| [RealtimeServerEventResponseTextDone](#realtimeservereventresponsetextdone) | The server `response.text.done` event is returned when the model-generated text is done streaming. |
+| [RealtimeServerEventSessionCreated](#realtimeservereventsessioncreated) | The server `session.created` event is the first server event when you establish a new connection to the Realtime API. This event creates and returns a new session with the default session configuration. |
+| [RealtimeServerEventSessionUpdated](#realtimeservereventsessionupdated) | The server `session.updated` event is returned when a session is updated by the client. If there's an error, the server sends an `error` event instead. |
 
 ### RealtimeServerEventConversationCreated
 
@@ -1080,7 +1080,7 @@ The server `session.updated` event is returned when a session is updated by the
 | Field | Type | Description | 
 |-------|------|-------------|
 | type | [RealtimeClientEventType](#realtimeclienteventtype) | The type of the client event. |
-| event_id | string | The unique ID of the event. |
+| event_id | string | The unique ID of the event. The ID can be specified by the client to help identify the event. |
 
 ### RealtimeClientEventType
 
@@ -1117,18 +1117,28 @@ The item to add to the conversation.
 
 ### RealtimeConversationRequestItem
 
+You use the `RealtimeConversationRequestItem` object to create a new item in the conversation via the [conversation.item.create](#realtimeclienteventconversationitemcreate) event.
+
 | Field | Type | Description | 
 |-------|------|-------------|
 | type | [RealtimeItemType](#realtimeitemtype) | The type of the item. |
-| id | string | The unique ID of the item. |
+| id | string | The unique ID of the item. The ID can be specified by the client to help manage server-side context. If the client doesn't provide an ID, the server generates one. |
 
 ### RealtimeConversationResponseItem
 
+The `RealtimeConversationResponseItem` object represents an item in the conversation. It's used in some of the server events, such as:
+
+- [conversation.item.created](#realtimeservereventconversationitemcreated)
+- [response.output_item.added](#realtimeservereventresponseoutputitemadded)
+- [response.output_item.done](#realtimeservereventresponseoutputitemdone)
+- [`response.created`](#realtimeservereventresponsecreated) (via the `response` property type [`RealtimeResponse`](#realtimeresponse))
+- [`response.done`](#realtimeservereventresponsedone) (via the `response` property type [`RealtimeResponse`](#realtimeresponse))
+
 | Field | Type | Description | 
 |-------|------|-------------|
-| object | string | The conversation response item.<br><br>Allowed values: `realtime.item` |
-| type | [RealtimeItemType](#realtimeitemtype) | The type of the item. | 
-| id | string | The unique ID of the item.<br><br>This property is nullable. |
+| object | string | The identifier for the returned API object.<br><br>Allowed values: `realtime.item` |
+| type | [RealtimeItemType](#realtimeitemtype) | The type of the item.<br><br>Allowed values: `message`, `function_call`, `function_call_output` | 
+| id | string | The unique ID of the item. The ID can be specified by the client to help manage server-side context. If the client doesn't provide an ID, the server generates one.<br><br>This property is nullable. |
 
 ### RealtimeFunctionTool
 
@@ -1138,8 +1148,8 @@ The definition of a function tool as used by the realtime endpoint.
 |-------|------|-------------|
 | type | string | The type of the tool.<br><br>Allowed values: `function` |
 | name | string | The name of the function. |
-| description | string | The description of the function. |
-| parameters | object | The parameters of the function. |
+| description | string | The description of the function, including usage guidelines. For example, "Use this function to get the current time." |
+| parameters | object | The parameters of the function in the form of a JSON object. |
 
 ### RealtimeItemStatus
 
@@ -1214,6 +1224,8 @@ The definition of a function tool as used by the realtime endpoint.
 
 ### RealtimeRequestSession
 
+You use the `RealtimeRequestSession` object when you want to update the session configuration via the [session.update](#realtimeclienteventsessionupdate) event.
+
 | Field | Type | Description | 
 |-------|------|-------------|
 | modalities | array | The modalities that the session supports.<br><br>Allowed values: `text`, `audio`<br/><br/>For example, `"modalities": ["text", "audio"]` is the default setting that enables both text and audio modalities. To enable only text, set `"modalities": ["text"]`. You can't enable only audio. |
@@ -1224,7 +1236,7 @@ The definition of a function tool as used by the realtime endpoint.
 | input_audio_transcription | [RealtimeAudioInputTranscriptionSettings](#realtimeaudioinputtranscriptionsettings) | The settings for audio input transcription.<br><br>This property is nullable. |
 | turn_detection | [RealtimeTurnDetection](#realtimeturndetection) | The turn detection settings for the session.<br><br>This property is nullable. |
 | tools | array of [RealtimeTool](#realtimetool) | The tools available to the model for the session. |
-| tool_choice | [RealtimeToolChoice](#realtimetoolchoice) | The tool choice for the session. |
+| tool_choice | [RealtimeToolChoice](#realtimetoolchoice) | The tool choice for the session.<br><br>Allowed values: `auto`, `none`, and `required`. Otherwise, you can specify the name of the function to use. |
 | temperature | number | The sampling temperature for the model. The allowed temperature values are limited to [0.6, 1.2]. Defaults to 0.8. |
 | max_response_output_tokens | integer or "inf" | The maximum number of output tokens per assistant response, inclusive of tool calls.<br><br>Specify an integer between 1 and 4096 to limit the output tokens. Otherwise, set the value to "inf" to allow the maximum number of tokens.<br><br>For example, to limit the output tokens to 1000, set `"max_response_output_tokens": 1000`. To allow the maximum number of tokens, set `"max_response_output_tokens": "inf"`.<br><br>Defaults to `"inf"`. |
 
@@ -1324,6 +1336,10 @@ The response resource.
 
 ### RealtimeResponseSession
 
+The `RealtimeResponseSession` object represents a session in the Realtime API. It's used in some of the server events, such as:
+- [`session.created`](#realtimeservereventsessioncreated)
+- [`session.updated`](#realtimeservereventsessionupdated)
+
 | Field | Type | Description | 
 |-------|------|-------------|
 | object | string | The session object.<br><br>Allowed values: `realtime.session` |
@@ -1369,7 +1385,7 @@ The response resource.
 | Field | Type | Description | 
 |-------|------|-------------|
 | type | [RealtimeServerEventType](#realtimeservereventtype) | The type of the server event. |
-| event_id | string | The unique ID of the event. |
+| event_id | string | The unique ID of the server event. |
 
 ### RealtimeServerEventRateLimitsUpdatedRateLimitsItem
 
@@ -1436,21 +1452,21 @@ The base representation of a realtime tool definition.
 
 ### RealtimeToolChoice
 
-The combined set of available representations for a realtime tool_choice parameter, encompassing both string literal options like 'auto' and structured references to defined tools.
+The combined set of available representations for a realtime `tool_choice` parameter, encompassing both string literal options like 'auto' and structured references to defined tools.
 
 ### RealtimeToolChoiceFunctionObject
 
-The representation of a realtime tool_choice selecting a named function tool.
+The representation of a realtime `tool_choice` selecting a named function tool.
 
 | Field | Type | Description | 
 |-------|------|-------------|
-| type | string | The type of the tool_choice.<br><br>Allowed values: `function` |
+| type | string | The type of the `tool_choice`.<br><br>Allowed values: `function` |
 | function | object | The function tool to select.<br><br>See nested properties next.|
 | + name | string | The name of the function tool.<br><br>A property of the `function` object. | 
 
 ### RealtimeToolChoiceLiteral
 
-The available set of mode-level, string literal tool_choice options for the realtime endpoint.
+The available set of mode-level, string literal `tool_choice` options for the realtime endpoint.
 
 **Allowed Values:**
 
@@ -1460,11 +1476,11 @@ The available set of mode-level, string literal tool_choice options for the real
 
 ### RealtimeToolChoiceObject
 
-A base representation for a realtime tool_choice selecting a named tool.
+A base representation for a realtime `tool_choice` selecting a named tool.
 
 | Field | Type | Description | 
 |-------|------|-------------|
-| type | [RealtimeToolType](#realtimetooltype) | The type of the tool_choice. |
+| type | [RealtimeToolType](#realtimetooltype) | The type of the `tool_choice`. |
 
 ### RealtimeToolType
 

@@ -32,7 +32,7 @@ items:
     - name: Chat
       href: chatgpt-quickstart.md  
       displayName: ChatGPT, chatgpt
-    - name: GPT-4 Turbo with Vision
+    - name: Vision-enabled chats
       href: gpt-v-quickstart.md
     - name: DALL-E
       href: dall-e-quickstart.md
@@ -64,7 +64,7 @@ items:
       href: ./concepts/understand-embeddings.md
     - name: Fine-tuning
       href: ./concepts/fine-tuning-considerations.md
-    - name: GPT-4 Turbo with Vision
+    - name: Vision-enabled models
       href: ./concepts/gpt-with-vision.md
     - name: Red teaming large language models (LLMs)
       href: ./concepts/red-teaming.md
@@ -122,7 +122,7 @@ items:
     - name: GPT-35-Turbo & GPT-4 
       href: ./how-to/chatgpt.md
       displayName: ChatGPT, chatgpt
-    - name: GPT-4 Turbo with Vision
+    - name: Vision-enabled chats
       href: ./how-to/gpt-with-vision.md
     - name: DALL-E
       href: ./how-to/dall-e.md
@@ -170,6 +170,8 @@ items:
         displayName: fine-tuning, finetuning
       - name: Weights & Biases integration (preview)
         href: ./how-to/weights-and-biases-integration.md
+  - name: Stored completions
+    href: ./how-to/stored-completions.md
   - name: Use your data
     items:
       - name: Text data

@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - ignite-2024
 ms.topic: whats-new
-ms.date: 11/12/2024
+ms.date: 11/16/2024
 recommendations: false
 ---
 
@@ -21,6 +21,18 @@ This article provides a summary of the latest releases and major documentation u
 
 ## December 2024
 
+### GPT-4o 2024-11-20
+
+`gpt-4o-2024-11-20` is now available for [global standard deployment](./how-to/deployment-types.md) in:
+
+- East US
+- East US 2
+- North Central US
+- South Central US
+- West US
+- West US 3
+- Sweden Central
+
 ### NEW data zone provisioned deployment type
 
 Data zone provisioned deployments are available in the same Azure OpenAI resource as all other Azure OpenAI deployment types but allow you to leverage Azure global infrastructure to dynamically route traffic to the data center within the Microsoft defined data zone with the best availability for each request. Data zone provisioned deployments provide reserved model processing capacity for high and predictable throughput using Azure infrastructure within Microsoft specified data zones. Data zone provisioned deployments are supported on `gpt-4o-2024-08-06`, `gpt-4o-2024-05-13`, and `gpt-4o-mini-2024-07-18` models.