November 3, 2024 2 min read

OpenAI o1 Reasoning Models: A New Paradigm for Complex Problem Solving

Understanding o1’s Architecture

Traditional LLMs generate text sequentially. o1 models introduce a reasoning phase:

User Query → [Reasoning Tokens] → [Response Tokens]
                  (hidden)           (visible)

The reasoning tokens are where the model works through the problem step-by-step, similar to how humans solve complex problems.

When to Use o1 vs GPT-4o

Use Case	Best Model	Why
Data pipeline debugging	o1	Complex reasoning needed
Simple Q&A	GPT-4o	Speed and cost
Code review	o1	Deep analysis
Content generation	GPT-4o	Creativity over logic
Math/science problems	o1	Accuracy critical
Real-time chat	GPT-4o	Latency matters

Using o1 in Azure OpenAI

from openai import AzureOpenAI

client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    api_version="2024-10-01-preview",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)

# o1 models work differently - no system message, just user prompts
response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user",
            "content": """Analyze this SQL query performance issue:

Query takes 45 minutes on 100M rows:
```sql
SELECT
    c.customer_id,
    c.customer_name,
    SUM(o.order_total) as lifetime_value,
    COUNT(DISTINCT o.order_id) as order_count
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id
LEFT JOIN order_items oi ON o.order_id = oi.order_id
WHERE c.created_date >= '2020-01-01'
GROUP BY c.customer_id, c.customer_name
HAVING SUM(o.order_total) > 1000
ORDER BY lifetime_value DESC;

Tables:

customers: 10M rows, clustered on customer_id
orders: 100M rows, clustered on order_id
order_items: 500M rows, clustered on order_item_id

Provide a complete optimization strategy.""" } ], max_completion_tokens=4000 # Note: different parameter for o1 )

print(response.choices[0].message.content)


## Understanding o1's Response Pattern

o1 models provide structured, thorough responses:

```python
# o1 typically structures responses like this:
"""
## Analysis

First, let me understand the query structure and identify bottlenecks...

### Issue 1: Suboptimal Join Order
The query joins customers → orders → order_items, but...

### Issue 2: Missing Covering Indexes
The GROUP BY requires...

### Issue 3: Unnecessary Table Access
The order_items table is joined but...

## Recommended Solution

### Step 1: Add Supporting Indexes
```sql
CREATE INDEX idx_orders_customer ON orders(customer_id)
INCLUDE (order_id, order_total);

Step 2: Rewrite the Query

…

Expected Impact

Current: 45 minutes
After optimization: ~2-3 minutes
Reasoning: … """


## Comparing Output Quality

Let's compare responses for a complex data engineering problem:

```python
def compare_models(question: str):
    """Compare o1 and GPT-4o responses."""

    # GPT-4o response
    gpt4o_response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a data engineering expert."},
            {"role": "user", "content": question}
        ],
        max_tokens=2000
    )

    # o1 response (no system message)
    o1_response = client.chat.completions.create(
        model="o1-preview",
        messages=[
            {"role": "user", "content": f"As a data engineering expert: {question}"}
        ],
        max_completion_tokens=2000
    )

    return {
        "gpt4o": gpt4o_response.choices[0].message.content,
        "o1": o1_response.choices[0].message.content,
        "gpt4o_tokens": gpt4o_response.usage.total_tokens,
        "o1_tokens": o1_response.usage.total_tokens
    }

question = """
Design a real-time fraud detection system that:
1. Processes 10,000 transactions per second
2. Has sub-100ms latency requirements
3. Uses Microsoft Fabric for data storage
4. Needs 99.99% availability
5. Must explain why transactions are flagged

Provide architecture, technology choices, and trade-offs.
"""

comparison = compare_models(question)

Token Usage and Costs

o1 models use significantly more tokens due to reasoning:

# Check token usage
response = client.chat.completions.create(
    model="o1-preview",
    messages=[{"role": "user", "content": "Complex question..."}],
    max_completion_tokens=4000
)

print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Completion tokens: {response.usage.completion_tokens}")
print(f"Reasoning tokens: {response.usage.completion_tokens_details.reasoning_tokens}")
print(f"Total: {response.usage.total_tokens}")

# Typical ratio for complex problems:
# Reasoning tokens: 2000-5000
# Visible completion: 500-2000

Best Practices for o1

Be specific and detailed - o1 performs better with comprehensive context
Skip the system message - Include instructions in the user message
Allow sufficient tokens - Complex problems need room to reason
Use for high-value tasks - Cost is higher, save for complex problems
Don’t rush it - o1 takes longer but produces better results

# Good o1 prompt
good_prompt = """
I need to design a data pipeline with these requirements:
- Source: 50 REST APIs with varying schemas
- Volume: 1TB daily
- Latency: Data available within 15 minutes
- Target: Microsoft Fabric Lakehouse
- Constraints: $5000/month budget

Consider:
1. Architecture options (streaming vs batch vs hybrid)
2. Technology choices with trade-offs
3. Error handling and monitoring
4. Cost breakdown

Provide a detailed recommendation with reasoning.
"""

# Less effective prompt
weak_prompt = "How do I build a data pipeline?"

The Future of Reasoning Models

o1 represents the beginning of reasoning-capable AI. Expect:

o1 in Azure AI Foundry
Fine-tuning capabilities
Faster inference
Multi-modal reasoning (images, documents)

For data professionals, o1 excels at:

Complex query optimization
Architecture design
Debugging intricate issues
Root cause analysis

The key is knowing when the extra cost and latency are worth the improved reasoning.