Azure OpenAI o1 Models: Reasoning at Scale for Complex Problems

The o1 family of models from OpenAI, now available on Azure, represents a breakthrough in AI reasoning capabilities. These models excel at complex, multi-step problems that require careful analysis and logical deduction.

When to Use o1 Models

The o1 models are designed for tasks requiring deep reasoning: mathematical proofs, code debugging, scientific analysis, and strategic planning. They take more time to generate responses because they perform internal chain-of-thought reasoning.

Implementing o1 in Your Applications

from openai import AzureOpenAI
import os

client = AzureOpenAI(
    api_key=os.environ["AZURE_OPENAI_KEY"],
    api_version="2024-12-01-preview",
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"]
)

def solve_complex_problem(problem_statement: str) -> str:
    """Use o1 for problems requiring deep reasoning."""

    response = client.chat.completions.create(
        model="o1-preview",  # or "o1-mini" for faster, lighter tasks
        messages=[
            {
                "role": "user",
                "content": problem_statement
            }
        ],
        # Note: o1 models don't support system messages or temperature
        max_completion_tokens=25000  # Allow for extended reasoning
    )

    return response.choices[0].message.content

# Example: Complex code review
code_review = solve_complex_problem("""
Analyze this recursive algorithm for potential issues:

def find_path(graph, start, end, path=[]):
    path = path + [start]
    if start == end:
        return path
    for node in graph[start]:
        if node not in path:
            newpath = find_path(graph, node, end, path)
            if newpath: return newpath
    return None

What are the time complexity, space complexity, and potential bugs?
""")

Cost Considerations

The o1 models use more compute for reasoning, making them more expensive per token. Use them strategically for high-value problems where accuracy matters most. For simpler tasks, GPT-4o remains the more cost-effective choice.

Understanding when to deploy o1 versus GPT-4o is crucial for building cost-efficient AI applications that leverage the right model for each task.