---
title: "Azure OpenAI"
description: "import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/azure-2
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:08:09.213Z
license: CC-BY-4.0
attribution: "Azure OpenAI — Claudary (https://claudary.paisolsolutions.com/skills/azure-2)"
---

# Azure OpenAI
import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

## Overview

import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Azure OpenAI

## Overview

| Property | Details |
|-------|-------|
| Description | Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o1, o1-mini, GPT-5, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. Also supports Claude models via Azure Foundry. |
| Provider Route on LiteLLM | `azure/`, [`azure/o_series/`](#o-series-models), [`azure/gpt5_series/`](#gpt-5-models), [`azure/claude-*`](./azure_anthropic) (Claude models via Azure Foundry) |
| Supported Operations | [`/chat/completions`](#azure-openai-chat-completion-models), [`/responses`](./azure_responses), [`/completions`](#azure-instruct-models), [`/embeddings`](./azure_embedding), [`/audio/speech`](azure_speech), [`/audio/transcriptions`](../audio_transcription), `/fine_tuning`, [`/batches`](#azure-batches-api), `/files`, [`/images`](../image_generation#azure-openai-image-generation-models), [`/anthropic/v1/messages`](./azure_anthropic) |
| Link to Provider Doc | [Azure OpenAI ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview), [Azure Foundry Claude ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude)

## API Keys, Params
api_key, api_base, api_version etc can be passed directly to `litellm.completion` - see here or set as `litellm.api_key` params see here
```python
import os
os.environ["AZURE_API_KEY"] = "" # "my-azure-api-key"
os.environ["AZURE_API_BASE"] = "" # "https://example-endpoint.openai.azure.com"
os.environ["AZURE_API_VERSION"] = "" # "2023-05-15"

# optional
os.environ["AZURE_AD_TOKEN"] = ""
os.environ["AZURE_API_TYPE"] = ""
```

:::info Azure Foundry Claude Models

Azure also supports Claude models via Azure Foundry. Use `azure/claude-*` model names (e.g., `azure/claude-sonnet-4-5`) with Azure authentication. See the [Azure Anthropic documentation](./azure_anthropic) for details.

:::

## **Usage - LiteLLM Python SDK**
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/LiteLLM_Azure_OpenAI.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

### Completion - using .env variables

```python
from litellm import completion

## set ENV variables
os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

# azure call
response = completion(
    model = "azure/<your_deployment_name>", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)
```

### Completion - using api_key, api_base, api_version

```python
import litellm

# azure call
response = litellm.completion(
    model = "azure/<your deployment name>",             # model = azure/<your deployment name> 
    api_base = "",                                      # azure api base
    api_version = "",                                   # azure api version
    api_key = "",                                       # azure api key
    messages = [{"role": "user", "content": "good morning"}],
)
```

### Completion - using azure_ad_token, api_base, api_version

```python
import litellm

# azure call
response = litellm.completion(
    model = "azure/<your deployment name>",             # model = azure/<your deployment name> 
    api_base = "",                                      # azure api base
    api_version = "",                                   # azure api version
    azure_ad_token="", 									# azure_ad_token 
    messages = [{"role": "user", "content": "good morning"}],
)
```


## **Usage - LiteLLM Proxy Server**

Here's how to call Azure OpenAI models with the LiteLLM Proxy Server

### 1. Save key in your environment

```bash
export AZURE_API_KEY=""
```

### 2. Start the proxy 

```yaml
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/chatgpt-v-2
      api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
      api_version: "2023-05-15"
      api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env.
```

### 3. Test it

<Tabs>
<TabItem value="Curl" label="Curl Request">

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \\
--header 'Content-Type: application/json' \\
--data ' {
      "model": "gpt-3.5-turbo",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'
```
</TabItem>
<TabItem value="openai" label="OpenAI v1.0.0+">

```python
import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
    {
        "role": "user",
        "content": "this is a test request, write a short poem"
    }
])

print(response)

```
</TabItem>
<TabItem value="langchain" label="Langchain">

```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
    model = "gpt-3.5-turbo",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)

print(response)
```
</TabItem>
</Tabs>


### Setting API Version

You can set the `api_version` for Azure OpenAI in your proxy config.yaml in the following ways

#### Option 1: Per Model Configuration

```yaml showLineNumbers title="config.yaml"
model_list:
  - model_name: gpt-4
    litellm_params:
      model: azure/my-gpt4-deployment
      api_base: https://your-resource.openai.azure.com/
      api_version: "2024-08-01-preview"  # Set version per model
      api_key: os.environ/AZURE_API_KEY
```





## Azure OpenAI Chat Completion Models

:::tip

**We support ALL Azure models, just set `model=azure/<your deployment name>` as a prefix when sending litellm requests**

:::

| Model Name       | Function Call                          |
|------------------|----------------------------------------|
| o1-mini | `response = completion(model="azure/<your deployment name>", messages=messages)` |
| o1-preview | `response = completion(model="azure/<your deployment name>", messages=messages)` |
| gpt-5 | `response = completion(model="azure/<your deployment name>", messages=messages)` |
| gpt-4o-mini            | `completion('azure/<your deployment name>', messages)`         |
| gpt-4o            | `completion('azure/<your deployment name>', messages)`         |
| gpt-4            | `completion('azure/<your deployment name>', messages)`         |
| gpt-4-0314            | `completion('azure/<your deployment name>', messages)`         | 
| gpt-4-0613            | `completion('azure/<your deployment name>', messages)`         |
| gpt-4-32k            | `completion('azure/<your deployment name>', messages)`         | 
| gpt-4-32k-0314            | `completion('azure/<your deployment name>', messages)`         |
| gpt-4-32k-0613            | `completion('azure/<your deployment name>', messages)`         | 
| gpt-4-1106-preview            | `completion('azure/<your deployment name>', messages)`         | 
| gpt-4-0125-preview            | `completion('azure/<your deployment name>', messages)`         | 
| gpt-3.5-turbo    | `completion('azure/<your deployment name>', messages)` |
| gpt-3.5-turbo-0301    | `completion('azure/<your deployment name>', messages)` |
| gpt-3.5-turbo-0613    | `completion('azure/<your deployment name>', messages)` |
| gpt-3.5-turbo-16k    | `completion('azure/<your deployment name>', messages)` |
| gpt-3.5-turbo-16k-0613    | `completion('azure/<your deployment name>', messages)`

## Azure OpenAI Vision Models 
| Model Name            | Function Call                                                   |
|-----------------------|-----------------------------------------------------------------|
| gpt-4-vision   | `completion(model="azure/<your deployment name>", messages=messages)` |
| gpt-4o            | `completion('azure/<your deployment name>', messages)`         |

#### Usage
```python
import os 
from litellm import completion

os.environ["AZURE_API_KEY"] = "your-api-key"

# azure call
response = completion(
    model = "azure/<your deployment name>", 
    messages=[
        {
            "role": "user",
            "content": [
                            {
                                "type": "text",
                                "text": "What’s in this image?"
                            },
                            {
                                "type": "image_url",
                                "image_url": {
                                "url": "https://awsmp-logos.s3.amazonaws.com/seller-xw5kijmvmzasy/c233c9ade2ccb5491072ae232c814942.png"
                                }
                            }
                        ]
        }
    ],
)

```

#### Usage - with Azure Vision enhancements

Note: **Azure requires the `base_url` to be set with `/extensions`** 

Example 
```python
base_url=https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions
# base_url="{azure_endpoint}/openai/deployments/{azure_deployment}/extensions"
```

**Usage**
```python
import os 
from litellm import completion

os.environ["AZURE_API_KEY"] = "your-api-key"

# azure call
response = completion(
            model="azure/gpt-4-vision",
            timeout=5,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Whats in this image?"},
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://avatars.githubusercontent.com/u/29436595?v=4"
                            },
                        },
                    ],
                }
            ],
            base_url="https://gpt-4-vision-resource.openai.azure.com/openai/deployments/gpt-4-vision/extensions",
            api_key=os.getenv("AZURE_VISION_API_KEY"),
            enhancements={"ocr": {"enabled": True}, "grounding": {"enabled": True}},
            dataSources=[
                {
                    "type": "AzureComputerVision",
                    "parameters": {
                        "endpoint": "https://gpt-4-vision-enhancement.cognitiveservices.azure.com/",
                        "key": os.environ["AZURE_VISION_ENHANCE_KEY"],
                    },
                }
            ],
)
```

## O-Series Models

Azure OpenAI O-Series models are supported on LiteLLM. 

LiteLLM routes any deployment name with `o1` or `o3` in the model name, to the O-Series [transformation](https://github.com/BerriAI/litellm/blob/91ed05df2962b8eee8492374b048d27cc144d08c/litellm/llms/azure/chat/o1_transformation.py#L4) logic.

To set this explicitly, set `model` to `azure/o_series/<your-deployment-name>`.

**Automatic Routing**

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm

litellm.completion(model="azure/my-o3-deployment", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o3' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
model_list:
  - model_name: o3-mini
    litellm_params:
      model: azure/o3-model
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
```

</TabItem>
</Tabs>

**Explicit Routing**

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm

litellm.completion(model="azure/o_series/my-random-deployment-name", messages=[{"role": "user", "content": "Hello, world!"}]) # 👈 Note: 'o_series/' in the deployment name
```
</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
model_list:
  - model_name: o3-mini
    litellm_params:
      model: azure/o_series/my-random-deployment-name
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
```
</TabItem>
</Tabs>


## GPT-5 Models

| Property | Details |
|-------|-------|
| Description | Azure OpenAI GPT-5 models |
| Provider Route on LiteLLM | `azure/gpt5_series/<custom-name>` or `azure/gpt-5-deployment-name` |

LiteLLM supports using Azure GPT-5 models in one of the two ways:
1. Explicit Routing: `model = azure/gpt5_series/<deployment-name>`. In this scenario the model onboarded to litellm follows the format `model=azure/gpt5_series/<deployment-name>`.
2. Inferred Routing (If the azure deployment name contains `gpt-5` in the name): `model = azure/gpt-5-mini`. In this scenario the model onboarded to litellm follows the format `model=azure/gpt-5-mini`.

#### Explicit Routing
Use `azure/gpt5_series/<deployment-name>` for explicit GPT-5 model routing. 

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm

response = litellm.completion(
    model="azure/gpt5_series/my-gpt-5-deployment",
    messages=[{"role": "user", "content": "Hello, world!"}]
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
model_list:
  - model_name: gpt-5
    litellm_params:
      model: azure/gpt5_series/my-gpt-5-deployment
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
```

</TabItem>
</Tabs>

#### Inferred Routing (gpt-5 in the deployment name)
If your Azure deployment name contains `gpt-5`, LiteLLM automatically recognizes it as a GPT-5 model.

<Tabs>
<TabItem value="sdk" label="SDK">

```python
import litellm

# Deployment name contains 'gpt-5' - automatically inferred
response = litellm.completion(
    model="azure/my-gpt-5-deployment", 
    messages=[{"role": "user", "content": "Hello, world!"}]
)
```

</TabItem>
<TabItem value="proxy" label="PROXY">

```yaml
model_list:
  - model_name: gpt-5-mini
    litellm_params:
      model: azure/my-gpt-5-deployment  # deployment name contains 'gpt-5'
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
```

</TabItem>
</Tabs>






## Azure Audio Model

<Tabs>
<TabItem value="sdk" label="SDK">

```python
from litellm import completion
import os

os.environ["AZURE_API_KEY"] = ""
os.environ["AZURE_API_BASE"] = ""
os.environ["AZURE_API_VERSION"] = ""

response = completion(
    model="azure/azure-openai-4o-audio",
    messages=[
      {
        "role": "user",
        "content": "I want to try out speech to speech"
      }
    ],
    modalities=["text","audio"],
    audio={"voice": "alloy", "format": "wav"}
)

print(response)
```
</TabItem>
<TabItem value="proxy" label="PROXY">

1. Setup config.yaml

```yaml
model_list:
  - model_name: azure-openai-4o-audio
    litellm_params:
      model: azure/azure-open

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/azure-2) · https://claudary.paisolsolutions.com