Reasoning Models Evolution: From o1 to o3 and Beyond

OpenAI’s o1 model introduced a new paradigm: models that think before responding. With o3 now available, let’s explore how reasoning models work and when to use them.

What Makes Reasoning Models Different?

Traditional LLMs generate responses token by token. Reasoning models add an explicit “thinking” phase:

Traditional: Input → Generate → Output
Reasoning:   Input → Think → Verify → Output

Using o3 in Practice

from openai import OpenAI

client = OpenAI()

# o3 for complex reasoning tasks
response = client.chat.completions.create(
    model="o3",
    messages=[
        {
            "role": "user",
            "content": """
            Design a data architecture for a multi-tenant SaaS platform that:
            1. Serves 10,000 tenants with varying data volumes
            2. Requires sub-second query performance
            3. Must comply with GDPR (data residency)
            4. Needs to support real-time analytics
            5. Budget constraint: $50K/month

            Provide a detailed architecture with trade-off analysis.
            """
        }
    ],
    reasoning_effort="high"  # Control thinking depth
)

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

Reasoning Effort Levels

# Low effort - quick reasoning for simpler problems
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Calculate the total cost of 5 EC2 instances at $0.10/hour for 30 days"}],
    reasoning_effort="low"
)

# Medium effort - balanced for most tasks
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Design an ETL pipeline for daily sales data"}],
    reasoning_effort="medium"
)

# High effort - deep reasoning for complex problems
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": "Architect a globally distributed real-time analytics system"}],
    reasoning_effort="high"
)

When to Use Reasoning Models

Good Use Cases

# 1. Complex technical design
response = await client.chat.completions.create(
    model="o3",
    messages=[{
        "role": "user",
        "content": """
        We're migrating from on-premise SQL Server to Azure.
        Current state: 50TB database, 1000 concurrent users,
        complex stored procedures, SSIS packages.

        Design the migration strategy with:
        - Minimal downtime approach
        - Data validation plan
        - Rollback strategy
        - Timeline estimation
        """
    }],
    reasoning_effort="high"
)

# 2. Code review with reasoning
response = await client.chat.completions.create(
    model="o3",
    messages=[{
        "role": "user",
        "content": f"""
        Review this data pipeline code for:
        - Correctness
        - Performance issues
        - Edge cases
        - Security vulnerabilities

        Code:
        {pipeline_code}

        Explain your reasoning for each finding.
        """
    }],
    reasoning_effort="medium"
)

# 3. Debugging complex issues
response = await client.chat.completions.create(
    model="o3",
    messages=[{
        "role": "user",
        "content": f"""
        Our Spark job is failing intermittently with OOM errors.

        Configuration: {spark_config}
        Error logs: {error_logs}
        Data characteristics: {data_info}

        Diagnose the root cause and provide fix.
        """
    }],
    reasoning_effort="high"
)

Not Ideal For

# Simple factual questions - use regular models
# Bad: o3 for "What is the capital of France?"

# High-volume, low-complexity tasks - too expensive
# Bad: o3 for classifying 1 million support tickets

# Creative writing - reasoning doesn't help much
# Bad: o3 for "Write a blog post about Python"

Combining Reasoning with Tools

tools = [
    {
        "type": "function",
        "function": {
            "name": "query_database",
            "description": "Execute SQL query",
            "parameters": {...}
        }
    },
    {
        "type": "function",
        "function": {
            "name": "analyze_performance",
            "description": "Analyze query execution plan",
            "parameters": {...}
        }
    }
]

response = await client.chat.completions.create(
    model="o3",
    messages=[{
        "role": "user",
        "content": "Our customer_orders query is slow. Investigate and optimize it."
    }],
    tools=tools,
    reasoning_effort="high"
)

# o3 reasons about the problem, then uses tools strategically
# It might:
# 1. Query to understand current performance
# 2. Analyze the execution plan
# 3. Reason about optimization strategies
# 4. Propose and validate improvements

Cost Considerations

Reasoning models use more tokens due to the thinking process:

from openai import OpenAI

client = OpenAI()

# Track token usage
response = client.chat.completions.create(
    model="o3",
    messages=[{"role": "user", "content": complex_question}],
    reasoning_effort="high"
)

usage = response.usage
print(f"Input tokens: {usage.prompt_tokens}")
print(f"Reasoning tokens: {usage.completion_tokens_details.reasoning_tokens}")
print(f"Output tokens: {usage.completion_tokens - usage.completion_tokens_details.reasoning_tokens}")

# Reasoning tokens can be 10-100x the output tokens for complex problems

Model Selection Strategy

def select_model(task):
    """Choose the right model for the task."""

    # Task complexity assessment
    complexity_indicators = {
        "multi_step_reasoning": task.requires_planning,
        "technical_depth": task.domain_expertise_needed,
        "verification_needed": task.accuracy_critical,
        "creativity_needed": task.open_ended,
        "latency_sensitive": task.real_time
    }

    complexity_score = sum(complexity_indicators.values())

    if complexity_score >= 4 and not task.real_time:
        return "o3"  # Complex reasoning, can wait
    elif complexity_score >= 2:
        return "gpt-4o"  # Moderate complexity
    else:
        return "gpt-4o-mini"  # Simple tasks

Building Reasoning Pipelines

class ReasoningPipeline:
    """Multi-stage reasoning for complex problems."""

    def __init__(self):
        self.client = OpenAI()

    async def solve(self, problem: str) -> dict:
        # Stage 1: Problem decomposition
        decomposition = await self.client.chat.completions.create(
            model="o3",
            messages=[{
                "role": "user",
                "content": f"Decompose this problem into sub-problems:\n{problem}"
            }],
            reasoning_effort="medium"
        )

        sub_problems = self.parse_sub_problems(decomposition)

        # Stage 2: Solve each sub-problem
        solutions = []
        for sub in sub_problems:
            solution = await self.client.chat.completions.create(
                model="o3",
                messages=[{
                    "role": "user",
                    "content": f"Solve this specific problem:\n{sub}"
                }],
                reasoning_effort="high"
            )
            solutions.append(solution)

        # Stage 3: Synthesize final answer
        synthesis = await self.client.chat.completions.create(
            model="o3",
            messages=[{
                "role": "user",
                "content": f"""
                Original problem: {problem}
                Sub-solutions: {solutions}

                Synthesize a complete solution, ensuring consistency.
                """
            }],
            reasoning_effort="high"
        )

        return {
            "problem": problem,
            "decomposition": sub_problems,
            "sub_solutions": solutions,
            "final_solution": synthesis
        }

The Future of Reasoning Models

Expect to see:

• Longer reasoning chains for more complex problems
• Domain-specific reasoning models
• Hybrid architectures combining fast and slow thinking
• Verifiable reasoning with formal proofs
• Cost optimization as the technology matures

Reasoning models represent a significant advancement in AI capability. Use them for problems that truly require deep thinking, and you’ll see dramatically better results.