Merge pull request #2056 from mrbullwinkle/mrb_12_18_2024_o1_additional

[Azure OpenAI] [Release branch] Update What's new

Author: prmerger-automator[bot]

articles/ai-services/openai/how-to/prompt-caching.md

@@ -22,6 +22,7 @@ Caches are typically cleared within 5-10 minutes of inactivity and are always re
 
 Currently only the following models support prompt caching with Azure OpenAI:
 
+- `o1-2024-12-17`
 - `o1-preview-2024-09-12`
 - `o1-mini-2024-09-12`
 - `gpt-4o-2024-05-13`

articles/ai-services/openai/how-to/structured-outputs.md

@@ -6,7 +6,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 12/17/2024
+ms.date: 12/18/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false

articles/ai-services/openai/whats-new.md

@@ -11,7 +11,7 @@ ms.custom:
   - references_regions
   - ignite-2024
 ms.topic: whats-new
-ms.date: 11/17/2024
+ms.date: 11/18/2024
 recommendations: false
 ---
 
@@ -21,12 +21,29 @@ This article provides a summary of the latest releases and major documentation u
 
 ## December 2024
 
+### o1 reasoning model released for limited access
+
+The latest `o1` model is now available for API access and model deployment. **Registration is required, and access will be granted based on Microsoft's eligibility criteria**. Customers who previously applied and received access to `o1-preview`, don't need to reapply as they are automatically on the wait-list for the latest model.
+
+Request access: [limited access model application](https://aka.ms/OAI/o1access)
+
+To learn more about the advanced `o1` series models see, [getting started with o1 series reasoning models](./how-to/reasoning.md).
+
+### Region availability
+
+| Model | Region |
+|---|---|
+|`o1` <br>(Version: 2024-12-17)| East US2 (Global Standard) <br> Sweden Central (Global Standard) |
+
 ### Preference fine-tuning (preview)
 
 [Direct preference optimization (DPO)](./how-to/fine-tuning.md#direct-preference-optimization-dpo-preview) is a new alignment technique for large language models, designed to adjust model weights based on human preferences. Unlike reinforcement learning from human feedback (RLHF), DPO does not require fitting a reward model and uses simpler data (binary preferences) for training. This method is computationally lighter and faster, making it equally effective at alignment while being more efficient. DPO is especially useful in scenarios where subjective elements like tone, style, or specific content preferences are important. We’re excited to announce the public preview of DPO in Azure OpenAI Service, starting with the `gpt-4o-2024-08-06` model.
 
 For fine-tuning model region availability, see the [models page](./concepts/models.md#fine-tuning-models).
 
+### Stored completions & distillation
+
+[Stored completions](./how-to/stored-completions.md) allow you to capture the conversation history from chat completions sessions to use as datasets for [evaluations](./how-to/evaluations.md) and [fine-tuning](./how-to/fine-tuning.md).
 
 ### GPT-4o 2024-11-20