When to Use o1: A Decision Guide for AI Applications

With multiple OpenAI models available, knowing when o1 is the right choice can save you money and improve results. Here’s a practical guide for making that decision.

The o1 Sweet Spot

o1 shines in specific scenarios. Understanding these helps you deploy it effectively.

from enum import Enum
from typing import List, Tuple

class TaskComplexity(Enum):
    SIMPLE = 1      # Direct lookup or simple transformation
    MODERATE = 2    # Some reasoning required
    COMPLEX = 3     # Multi-step reasoning essential
    EXPERT = 4      # Requires deep domain expertise

class O1Suitability:
    """Evaluate whether o1 is appropriate for a given task"""

    HIGH_VALUE_TASKS = [
        "mathematical_proofs",
        "algorithm_design",
        "code_architecture",
        "scientific_reasoning",
        "legal_analysis",
        "strategic_planning",
        "root_cause_analysis",
        "security_review"
    ]

    LOW_VALUE_TASKS = [
        "simple_qa",
        "text_formatting",
        "translation",
        "summarization",
        "chat_conversation",
        "content_generation"
    ]

    @staticmethod
    def evaluate(task_type: str, complexity: TaskComplexity,
                 error_cost: float) -> Tuple[str, str]:
        """
        Returns (recommended_model, reasoning)
        """
        if task_type in O1Suitability.LOW_VALUE_TASKS:
            return ("gpt-4o-mini", "Simple task doesn't justify o1 cost")

        if complexity.value >= 3 and error_cost > 100:
            return ("o1-preview", "High complexity + high error cost")

        if task_type in O1Suitability.HIGH_VALUE_TASKS:
            if complexity.value >= 2:
                return ("o1-preview", "Task type benefits from reasoning")

        return ("gpt-4o", "General purpose model sufficient")

Real-World Use Cases

Use Case 1: Code Review and Bug Detection

from openai import OpenAI

client = OpenAI()

def deep_code_review(code: str) -> dict:
    """
    Use o1 for thorough code review - worth the cost
    for catching bugs before production
    """
    response = client.chat.completions.create(
        model="o1-preview",
        messages=[{
            "role": "user",
            "content": f"""
            Review this code for:
            1. Logic errors and bugs
            2. Security vulnerabilities
            3. Performance issues
            4. Edge cases not handled
            5. Race conditions (if applicable)

            Code:
            ```python
            {code}
            ```

            Provide specific line numbers and fixes for each issue found.
            """
        }],
        max_completion_tokens=8192
    )

    return {
        "review": response.choices[0].message.content,
        "reasoning_tokens": response.usage.completion_tokens_details.reasoning_tokens
    }

Use Case 2: Data Pipeline Validation

def validate_etl_logic(pipeline_spec: str) -> dict:
    """
    Validate complex ETL logic - o1 can catch subtle issues
    """
    response = client.chat.completions.create(
        model="o1-preview",
        messages=[{
            "role": "user",
            "content": f"""
            Analyze this ETL pipeline specification for correctness:

            {pipeline_spec}

            Check for:
            1. Data loss scenarios
            2. Ordering dependencies
            3. Idempotency issues
            4. Schema compatibility
            5. Null handling edge cases

            Identify any scenarios where data could be corrupted or lost.
            """
        }],
        max_completion_tokens=8192
    )

    return response.choices[0].message.content

Use Case 3: Architecture Decisions

def evaluate_architecture(requirements: str, options: List[str]) -> str:
    """
    Complex architectural decisions benefit from o1's reasoning
    """
    options_text = "\n".join([f"{i+1}. {opt}" for i, opt in enumerate(options)])

    response = client.chat.completions.create(
        model="o1-preview",
        messages=[{
            "role": "user",
            "content": f"""
            Given these requirements:
            {requirements}

            Evaluate these architectural options:
            {options_text}

            For each option:
            1. Analyze pros and cons
            2. Identify hidden costs and risks
            3. Consider scalability implications
            4. Evaluate operational complexity

            Recommend the best option with detailed justification.
            """
        }],
        max_completion_tokens=8192
    )

    return response.choices[0].message.content

When NOT to Use o1

# DON'T use o1 for simple tasks
# Bad - wasteful
response = client.chat.completions.create(
    model="o1-preview",  # Overkill!
    messages=[{"role": "user", "content": "Convert this to uppercase: hello"}]
)

# Good - appropriate model
response = client.chat.completions.create(
    model="gpt-4o-mini",  # Much cheaper, same result
    messages=[{"role": "user", "content": "Convert this to uppercase: hello"}]
)

# DON'T use o1 for streaming chat
# o1 doesn't support streaming - use GPT-4o instead

# DON'T use o1 when you need function calling
# Not supported yet - use GPT-4o

Cost-Benefit Analysis

def should_use_o1(task_value: float,
                  gpt4_accuracy: float,
                  o1_accuracy: float,
                  gpt4_cost: float,
                  o1_cost: float) -> bool:
    """
    Simple economic analysis for model selection
    """
    # Expected value with GPT-4o
    ev_gpt4 = task_value * gpt4_accuracy - gpt4_cost

    # Expected value with o1
    ev_o1 = task_value * o1_accuracy - o1_cost

    # Use o1 if it provides better expected value
    return ev_o1 > ev_gpt4

# Example: Bug in production costs $10,000
# GPT-4o catches 80% of bugs, costs $0.01
# o1 catches 95% of bugs, costs $0.15
# EV(GPT-4o) = 10000 * 0.80 - 0.01 = $7,999.99
# EV(o1) = 10000 * 0.95 - 0.15 = $9,499.85
# Use o1 for high-value bug detection!

Conclusion

Use o1 when the cost of errors is high and the task genuinely requires reasoning. For everything else, GPT-4o or GPT-4o-mini will serve you better at a fraction of the cost.