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articles/ai-foundry/foundry-models/includes/model-matrix/global-standard.md

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title: Global standard availability for Azure Direct models
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manager: nitinme
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ms.topic: include
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ms.date: 09/15/2025
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ms.date: 09/29/2025
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---
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<!-- ## GlobalStandard.md -->
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| **Region** | **DeepSeek-R1-0528** | **DeepSeek-R1** | **DeepSeek-V3-0324** | **DeepSeek-V3.1** | **FLUX.1-Kontext-pro** | **FLUX-1.1-pro** | **grok-4-fast-reasoning** | **grok-4-fast-non-reasoning** | **grok-code-fast-1** | **grok-3** | **grok-3-mini** | **Llama-4-Maverick-17B-128E-Instruct-FP8** | **Llama-3.3-70B-Instruct** | **MAI-DS-R1** | **mistral-document-ai-2505** |
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|:-------------------|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|
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| **Region** | **DeepSeek-R1-0528** | **DeepSeek-R1** | **DeepSeek-V3-0324** | **DeepSeek-V3.1** | **FLUX.1-Kontext-pro** | **FLUX-1.1-pro** | **grok-4** | **grok-4-fast-reasoning** | **grok-4-fast-non-reasoning** | **grok-code-fast-1** | **grok-3** | **grok-3-mini** | **Llama-4-Maverick-17B-128E-Instruct-FP8** | **Llama-3.3-70B-Instruct** | **MAI-DS-R1** | **mistral-document-ai-2505** |
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|:-------------------|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|
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articles/ai-foundry/foundry-models/includes/models-azure-direct-others.md

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- Grok 4 Fast, an efficiency-optimized language model that delivers near-Grok 4 reasoning capabilities with significantly lower latency and cost, and can bypass reasoning entirely for ultra-fast applications. It is trained for safe and effective tool use, with built-in refusal behaviors, a fixed safety-enforcing system prompt, and input filters to prevent misuse.
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- Grok 4 is the latest reasoning model from xAI with advanced reasoning and tool-use capabilities,
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enabling it to achieve new state-of-the-art performance across challenging academic and industry
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benchmarks. [Registration is required for access to the grok-4 model](https://aka.ms/xai/grok-4).
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| Model | Type | Capabilities | Deployment type (region availability) | Project type |
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| ------ | ---- | ------------ | ------------------------------------- | ------------ |
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| [grok-4](https://ai.azure.com/explore/models/grok-4/version/1/registry/azureml-xai/?cid=learnDocs) | chat-completion | - **Input:** text, image (256,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** yes <br /> - **Response formats:** text |- Global standard (all regions) | Foundry, Hub-based |
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| [grok-4-fast-reasoning](https://ai.azure.com/explore/models/grok-4-fast-reasoning/version/1/registry/azureml-xai/?cid=learnDocs) | chat-completion | - **Input:** text, image (2,000,000 tokens) <br /> - **Output:** text (2,000,000 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** yes <br /> - **Response formats:** text |- Global standard (all regions) | Foundry, Hub-based |
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| [grok-4-fast-non-reasoning](https://ai.azure.com/explore/models/grok-4-fast-non-reasoning/version/1/registry/azureml-xai/?cid=learnDocs) | chat-completion | - **Input:** text, image (2,000,000 tokens) <br /> - **Output:** text (2,000,000 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** yes <br /> - **Response formats:** text |- Global standard (all regions) | Foundry, Hub-based |
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| [grok-code-fast-1](https://ai.azure.com/explore/models/grok-code-fast-1/version/1/registry/azureml-xa/?cid=learnDocs) | chat-completion | - **Input:** text (256,000 tokens) <br /> - **Output:** text (8,192 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** yes <br /> - **Response formats:** text |- Global standard (all regions) | Foundry, Hub-based |

articles/ai-foundry/openai/includes/dall-e-typescript.md

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## Next steps
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* Explore the Image APIs in more depth with the [Image API how-to guide](../how-to/dall-e.md).
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* For more examples check out the [Azure OpenAI Samples GitHub repository](https://github.com/Azure/openai-samples).
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* For more examples check out the [Azure OpenAI Samples GitHub repository](https://github.com/Azure/azure-openai-samples).

articles/ai-foundry/openai/includes/models-azure-direct-openai.md

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> - Meta: `Llama-4-Maverick-17B-128E-Instruct-FP8`, `Llama-3.3-70B-Instruct`
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> - Microsoft: `MAI-DS-R1`
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> - Mistral: `mistral-document-ai-2505`
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> - xAI: `grok-code-fast-1`, `grok-3`, `grok-3-mini`, `grok-4-fast-reasoning`, `grok-4-fast-non-reasoning`
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> - xAI: `grok-code-fast-1`, `grok-3`, `grok-3-mini`, `grok-4-fast-reasoning`, `grok-4-fast-non-reasoning`, `grok-4`
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>
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> To learn about these models, switch to [Other model collections](../../foundry-models/concepts/models-sold-directly-by-azure.md?pivots=azure-direct-others) at the top of this article.
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articles/ai-foundry/responsible-ai/includes/non-english-translation.md

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> [!IMPORTANT]
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> Non-English translations are provided for convenience only. Please consult the `EN-US` version of this document for the binding version.
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> Non-English translations are provided for convenience only. Please consult the `EN-US` version of this document for the definitive version.

articles/ai-foundry/tutorials/deploy-chat-web-app.md

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First, identify the resources you need to set up in the Azure AI Foundry portal.
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1. Open the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), then select the project you used to deploy the Azure OpenAI chat model.
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1. Open the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), then select the hub-based project you used to deploy the Azure OpenAI chat model.
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1. Select **Management center** from the left pane.
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1. Select **Connected resources** under your project.
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1. Identify the three resources you need to configure: the **Azure OpenAI**, the **Azure AI Search**, and the **Azure Blob storage** that corresponds to your **workspaceblobstore**.
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:::image type="content" source="../media/tutorials/deploy-chat-web-app/resources.png" alt-text="Screenshot shows the connected resources that need to be configured.":::
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> If you don't see **Type** in the table, select **Columns** in the upper right corner and add to or reorder the **Selected columns**.
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> If you have multiple **Azure OpenAI** resources, use the one that contains your deployed chat model.
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1. Search for each of these names in the [Azure portal](https://portal.azure.com). Open each one in a new browser tab so that you can switch between them.
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1. When you're done, you have three new browser tabs open: **Search service**, **Azure AI Foundry**, and **blobstore Container**. Keep all three tabs open because you switch between them to set up the resources.
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articles/ai-services/speech-service/voice-live-api-reference.md

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| model | string | The transcription model. Supported: `whisper-1`, `gpt-4o-transcribe`, `gpt-4o-mini-transcribe`, `azure-speech` |
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| language | string | Optional language code in BCP-47 (e.g., `en-US`), or ISO-639-1 (e.g., `en`), or multi languages with auto detection, (e.g., `en,zh`). |
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| custom_speech | object | Optional configuration for custom speech models |
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| phrase_list | string[] | Optional list of phrase hints to bias recognition |
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| custom_speech | object | Optional configuration for custom speech models, only valid for `azure-speech` model. |
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| phrase_list | string[] | Optional list of phrase hints to bias recognition, only valid for `azure-speech` model. |
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| prompt | string | Optional prompt text to guide transcription, only valid for `whisper-1`, `gpt-4o-transcribe`, and `gpt-4o-mini-transcribe` models. |
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Azure End-of-Utterance (EOU) could indicate when the end-user stopped speaking while allowing for natural pauses. End of utterance detection can significantly reduce premature end-of-turn signals without adding user-perceivable latency.
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| model | string | Must be `"semantic_detection_v1"` |
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| threshold | number | Optional. Detection threshold |
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| model | string | Could be `semantic_detection_v1` supporting English or `semantic_detection_v1_multilingual` supporting English, Spanish, French, Italian, German (DE), Japanese, Portuguese, Chinese, Korean, Hindi |
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| threshold_level | string | Optional. Detection threshold level (`low`, `medium`, `high` and `default`), the default is `default` |
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| timeout_ms | number | Maximum time in milliseconds to wait for more user speech. Defaults to 1000 ms. |
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articles/machine-learning/how-to-use-mlflow-configure-tracking.md

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> Instead of `mlflow`, consider using [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/skinny/README_SKINNY.md). This package is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. It's recommended for users who primarily need MLflow tracking and logging capabilities but don't want to import the full suite of features, including deployments.
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> Instead of `mlflow`, consider using [`mlflow-skinny`](https://github.com/mlflow/mlflow/blob/master/libs/skinny/README_SKINNY.md). This package is a lightweight MLflow package without SQL storage, server, UI, or data science dependencies. It's recommended for users who primarily need MLflow tracking and logging capabilities but don't want to import the full suite of features, including deployments.
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articles/search/cognitive-search-aml-skill.md

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You can call the **AML** skill with the 2024-07-01 stable API version or an equivalent Azure SDK. For connections to the model catalog in the Azure AI Foundry portal, use the 2024-05-01-preview API version or later.
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You can call the **AML** skill with the stable REST API version or an equivalent Azure SDK. For connections to the model catalog in the Azure AI Foundry portal, use a preview API version.
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articles/search/cognitive-search-attach-cognitive-services.md

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{

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