Azure OpenAI reasoning models

Artikel
02/20/2025

Azure OpenAI o-series models are designed to tackle reasoning and problem-solving tasks with increased focus and capability. These models spend more time processing and understanding the user's request, making them exceptionally strong in areas like science, coding, and math compared to previous iterations.

Key capabilities of the o-series models:

Complex Code Generation: Capable of generating algorithms and handling advanced coding tasks to support developers.
Advanced Problem Solving: Ideal for comprehensive brainstorming sessions and addressing multifaceted challenges.
Complex Document Comparison: Perfect for analyzing contracts, case files, or legal documents to identify subtle differences.
Instruction Following and Workflow Management: Particularly effective for managing workflows requiring shorter contexts.

Availability

For access to o3-mini, o1, and o1-preview, registration is required, and access will be granted based on Microsoft's eligibility criteria.

Customers who previously applied and received access to o1 or 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

Region availability

Model	Region	Limited access
`o3-mini`	Model availability.	Limited access model application
`o1`	Model availability.	Limited access model application
`o1-preview`	Model availability.	This model is only available for customers who were granted access as part of the original limited access release. We're currently not expanding access to `o1-preview`.
`o1-mini`	Model availability.	No access request needed for Global Standard deployments. Standard (regional) deployments are currently only available to select customers who were previously granted access as part of the `o1-preview` release.

API & feature support

Feature	o3-mini, 2025-01-31	o1, 2024-12-17	o1-preview, 2024-09-12	o1-mini, 2024-09-12
API Version	`2024-12-01-preview` `2025-01-01-preview`	`2024-12-01-preview` `2025-01-01-preview`	`2024-09-01-preview` `2024-10-01-preview` `2024-12-01-preview`	`2024-09-01-preview` `2024-10-01-preview` `2024-12-01-preview`
Developer Messages	✅	✅	-	-
Structured Outputs	✅	✅	-	-
Context Window	Input: 200,000 Output: 100,000	Input: 200,000 Output: 100,000	Input: 128,000 Output: 32,768	Input: 128,000 Output: 65,536
Reasoning effort	✅	✅	-	-
Vision Support	-	✅	-	-
Functions/Tools	✅	✅	-	-
`max_completion_tokens`^*	✅	✅	✅	✅
System Messages^**	✅	✅	-	-
Streaming	✅	-	-	-

^* Reasoning models will only work with the max_completion_tokens parameter.

^**The latest o^* series model support system messages to make migration easier. When you use a system message with o3-mini and o1 it will be treated as a developer message. You should not use both a developer message and a system message in the same API request.

Not Supported

The following are currently unsupported with reasoning models:

Parallel tool calling
temperature, top_p, presence_penalty, frequency_penalty, logprobs, top_logprobs, logit_bias, max_tokens

Usage

These models don't currently support the same set of parameters as other models that use the chat completions API.

Python (Microsoft Entra ID)
Python (key-based auth)

You'll need to upgrade your OpenAI client library for access to the latest parameters.

Windows Command Prompt

pip install openai --upgrade

If you're new to using Microsoft Entra ID for authentication see How to configure Azure OpenAI Service with Microsoft Entra ID authentication.

Python

from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  azure_ad_token_provider=token_provider,
  api_version="2024-12-01-preview"
)

response = client.chat.completions.create(
    model="o1-new", # replace with the model deployment name of your o1-preview, or o1-mini model
    messages=[
        {"role": "user", "content": "What steps should I think about when writing my first Python API?"},
    ],
    max_completion_tokens = 5000

)

print(response.model_dump_json(indent=2))

You might need to upgrade your version of the OpenAI Python library to take advantage of the new parameters like max_completion_tokens.

Windows Command Prompt

pip install openai --upgrade

Python


from openai import AzureOpenAI

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
  api_version="2024-12-01-preview"
)

response = client.chat.completions.create(
    model="o1-new", # replace with the model deployment name of your o1 deployment.
    messages=[
        {"role": "user", "content": "What steps should I think about when writing my first Python API?"},
    ],
    max_completion_tokens = 5000

)

print(response.model_dump_json(indent=2))

Output:

JSON

{
  "id": "chatcmpl-AEj7pKFoiTqDPHuxOcirA9KIvf3yz",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Writing your first Python API is an exciting step in developing software that can communicate with other applications. An API (Application Programming Interface) allows different software systems to interact with each other, enabling data exchange and functionality sharing. Here are the steps you should consider when creating your first Python API...truncated for brevity.",
        "refusal": null,
        "role": "assistant",
        "function_call": null,
        "tool_calls": null
      },
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "protected_material_code": {
          "filtered": false,
          "detected": false
        },
        "protected_material_text": {
          "filtered": false,
          "detected": false
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ],
  "created": 1728073417,
  "model": "o1-2024-12-17",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_503a95a7d8",
  "usage": {
    "completion_tokens": 1843,
    "prompt_tokens": 20,
    "total_tokens": 1863,
    "completion_tokens_details": {
      "audio_tokens": null,
      "reasoning_tokens": 448
    },
    "prompt_tokens_details": {
      "audio_tokens": null,
      "cached_tokens": 0
    }
  },
  "prompt_filter_results": [
    {
      "prompt_index": 0,
      "content_filter_results": {
        "custom_blocklists": {
          "filtered": false
        },
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "jailbreak": {
          "filtered": false,
          "detected": false
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ]
}

Reasoning effort

Notiz

Reasoning models have reasoning_tokens as part of completion_tokens_details in the model response. These are hidden tokens that aren't returned as part of the message response content but are used by the model to help generate a final answer to your request. 2024-12-01-preview adds an additional new parameter reasoning_effort which can be set to low, medium, or high with the latest o1 model. The higher the effort setting, the longer the model will spend processing the request, which will generally result in a larger number of reasoning_tokens.

Developer messages

Functionally developer messages "role": "developer" are the same as system messages.

Adding a developer message to the previous code example would look as follows:

Python (Microsoft Entra ID)
Python (key-based auth)

You'll need to upgrade your OpenAI client library for access to the latest parameters.

Windows Command Prompt

pip install openai --upgrade

If you're new to using Microsoft Entra ID for authentication see How to configure Azure OpenAI Service with Microsoft Entra ID authentication.

Python

from openai import AzureOpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  azure_ad_token_provider=token_provider,
  api_version="2024-12-01-preview"
)

response = client.chat.completions.create(
    model="o1-new", # replace with the model deployment name of your o1-preview, or o1-mini model
    messages=[
        {"role": "developer","content": "You are a helpful assistant."}, # optional equivalent to a system message for reasoning models 
        {"role": "user", "content": "What steps should I think about when writing my first Python API?"},
    ],
    max_completion_tokens = 5000

)

print(response.model_dump_json(indent=2))

You might need to upgrade your version of the OpenAI Python library to take advantage of the new parameters like max_completion_tokens.

Windows Command Prompt

pip install openai --upgrade

Python


from openai import AzureOpenAI

client = AzureOpenAI(
  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
  api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
  api_version="2024-12-01-preview"
)

response = client.chat.completions.create(
    model="o1-new", # replace with the model deployment name of your o1 deployment.
    messages=[
        {"role": "developer","content": "You are a helpful assistant."}, # optional equivalent to a system message for reasoning models 
        {"role": "user", "content": "What steps should I think about when writing my first Python API?"},
    ],
    max_completion_tokens = 5000
)

print(response.model_dump_json(indent=2))

Markdown output

By default the o3-mini and o1 models will not attempt to produce output that includes markdown formatting. A common use case where this behavior is undesirable is when you want the model to output code contained within a markdown code block. When the model generates output without markdown formatting you lose features like syntax highlighting, and copyable code blocks in interactive playground experiences. To override this new default behavior and encourage markdown inclusion in model responses, add the string Formatting re-enabled to the beginning of your developer message.

Adding Formatting re-enabled to the beginning of your developer message does not guarantee that the model will include markdown formatting in its response, it only increases the likelihood. We have found from internal testing that Formatting re-enabled is less effective by itself with the o1 model than with o3-mini.

To improve the performance of Formatting re-enabled you can further augment the beginning of the developer message which will often result in the desired output. Rather than just adding Formatting re-enabled to the beginning of your developer message, you can experiment with adding a more descriptive initial instruction like one of the examples below:

Formatting re-enabled - please enclose code blocks with appropriate markdown tags.
Formatting re-enabled - code output should be wrapped in markdown.

Depending on your expected output you may need to customize your initial developer message further to target your specific use case.

Deelen iwwer

Azure OpenAI reasoning models

Availability

Region availability

API & feature support

Not Supported

Usage

Reasoning effort

Developer messages

Markdown output

Feedback

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