Merge pull request #5072 from voutilad/o4-mini

Add o4-mini details, update RFT linkage.

Author: prmerger-automator[bot]

articles/ai-services/openai/includes/fine-tune-models.md

@@ -25,6 +25,7 @@ manager: nitinme
 | `gpt-4.1` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text & Vision to Text |
 | `gpt-4.1-mini` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text to Text |
 | `gpt-4.1-nano` (2025-04-14) | North Central US <br> Sweden Central | ✅ | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 32,768 | May 2024 | Text to Text |
+| `o4-mini` (2025-04-16) | East US2 <br> Sweden Central | - | Input: 128,000 <br> Output: 16,384 <br> Training example context length: 65,536 | May 2024 | Text to Text |
 
 > [!NOTE]
 > **Global** training (in Public Preview) provides [more affordable](https://aka.ms/aoai-pricing) training per-token, but does not offer [data residency](https://aka.ms/data-residency). It is currently available to Azure OpenAI resources in the following regions, with more regions coming soon:

articles/ai-services/openai/includes/fine-tuning-studio.md

@@ -115,7 +115,7 @@ The first step is to confirm you model choice and the training method. Not all m
 
 - **Supervised Fine Tuning** (SFT): supported by all non-reasoning models.
 - **Direct Preference Optimization (Preview)** ([DPO](../how-to/fine-tuning-direct-preference-optimization.md)): supported by GPT-4o.
-- **Reinforcement Fine Tuning (Preview)** (RFT): supported by reasoning models, like o4-mini.
+- **Reinforcement Fine Tuning (Preview)** ([RFT](../how-to/reinforcement-fine-tuning.md)): supported by reasoning models, like o4-mini.
 
 When selecting the model, you may also select a [previously fine-tuned model](#continuous-fine-tuning).