Merge pull request #5400 from ssalgadodev/patch-568116

Update models-featured.md

Author: prmerger-automator[bot]

articles/ai-foundry/concepts/models-featured.md

@@ -181,8 +181,8 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 
 | Model  | Type | Capabilities |
 | ------ | ---- | ------------ |
-| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
-| [Llama 4-Maverick-17B-128E-Instruct-FP8](https://aka.ms/aifoundry/landing/llama-4-maverick-17b-128e-instruct-fp8) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** Yes <br /> - **Response formats:** Text |
+| [Llama-4-Scout-17B-16E-Instruct](https://aka.ms/aifoundry/landing/llama-4-scout-17b-16e-instruct) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
+| [Llama 4-Maverick-17B-128E-Instruct-FP8](https://aka.ms/aifoundry/landing/llama-4-maverick-17b-128e-instruct-fp8) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.3-70B-Instruct](https://ai.azure.com/explore/models/Llama-3.3-70B-Instruct/version/4/registry/azureml-meta) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-90B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-90B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
 | [Llama-3.2-11B-Vision-Instruct](https://ai.azure.com/explore/models/Llama-3.2-11B-Vision-Instruct/version/1/registry/azureml-meta) | [chat-completion (with images)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text and image (128,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |

articles/ai-foundry/model-inference/concepts/models.md

@@ -97,6 +97,16 @@ Meta Llama models and tools are a collection of pretrained and fine-tuned genera
 
 See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=meta).
 
+### xAI
+
+xAI's Grok 3 and Grok 3 Mini models are designed to excel in various enterprise domains. Grok 3, a non-reasoning model pre-trained by the Colossus datacenter, is tailored for business use cases such as data extraction, coding, and text summarization, with exceptional instruction-following capabilities. It supports a 131,072 token context window, allowing it to handle extensive inputs while maintaining coherence and depth, and is particularly adept at drawing connections across domains and languages. On the other hand, Grok 3 Mini is a lightweight reasoning model trained to tackle agentic, coding, mathematical, and deep science problems with test-time compute. It also supports a 131,072 token context window for understanding codebases and enterprise documents, and excels at using tools to solve complex logical problems in novel environments, offering raw reasoning traces for user inspection with adjustable thinking budgets.
+
+| Model  | Type | Tier | Capabilities |
+| ------ | ---- | --- | ------------ |
+| [grok-3](https://ai.azure.com/explore/models/grok-3/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
+| [grok-3-mini](https://ai.azure.com/explore/models/grok-3-mini/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
+
+
 ## Models from Partners and Community
 
 Models from Partners and Community available for deployment with pay-as-you-go billing (for example, Cohere models) are offered by the model provider but hosted in Microsoft-managed Azure infrastructure and accessed via API in the Azure AI Foundry. Model providers define the license terms and set the price for use of their models, while Azure AI Foundry manages the hosting infrastructure.
@@ -210,15 +220,6 @@ See [this model collection in Azure AI Foundry portal](https://ai.azure.com/expl
 | ------ | ---- | --- | ------------ |
 | [tsuzumi-7b](https://ai.azure.com/explore/models/Tsuzumi-7b/version/1/registry/azureml-nttdata) | chat-completion | Global standard | - **Input:** text (8,192 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Languages:** `en` and `jp` <br />  - **Tool calling:** No <br /> - **Response formats:** Text |
 
-### xAI
-
-xAI's Grok 3 and Grok 3 Mini models are designed to excel in various enterprise domains. Grok 3, a non-reasoning model pre-trained by the Colossus datacenter, is tailored for business use cases such as data extraction, coding, and text summarization, with exceptional instruction-following capabilities. It supports a 131,072 token context window, allowing it to handle extensive inputs while maintaining coherence and depth, and is particularly adept at drawing connections across domains and languages. On the other hand, Grok 3 Mini is a lightweight reasoning model trained to tackle agentic, coding, mathematical, and deep science problems with test-time compute. It also supports a 131,072 token context window for understanding codebases and enterprise documents, and excels at using tools to solve complex logical problems in novel environments, offering raw reasoning traces for user inspection with adjustable thinking budgets.
-
-| Model  | Type | Tier | Capabilities |
-| ------ | ---- | --- | ------------ |
-| [grok-3](https://ai.azure.com/explore/models/grok-3/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
-| [grok-3-mini](https://ai.azure.com/explore/models/grok-3-mini/version/1/registry/azureml-xai) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** text (131,072 tokens) <br /> - **Languages:** `en` <br />  - **Tool calling:** yes <br /> - **Response formats:** text |
-
 ## Open and protected models
 
 The [Azure AI model catalog](../../../ai-studio/how-to/model-catalog-overview.md) offers a larger selection of models, from a bigger range of providers. As opposite to Azure AI Foundry Models where models are provided as APIs, these models might require you to host them on your infrastructure, including the creation of an AI hub and project, and providing the underlying compute quota to host the models.