Read the full article on DataCamp: OpenAI’s O3 API – Step-by-Step Tutorial With Examples

Learn how to use OpenAI’s O3 model for complex, multi-step problem-solving involving visual and textual inputs. This tutorial covers API setup, tool-free and tool-assisted calls, cost management, and best prompting strategies.

Contents

When Should You Use O3?

OpenAI’s o3 model is built for deep reasoning, making it ideal for:

  • Multi-step problem-solving (math, research, software design)
  • Visual input + text combinations
  • Code refactoring and logic-heavy tasks

It supports tools like Python and web search and enables autonomous decision-making for agentic use cases.

Model Strength Use Case
o3 Reasoning, Multimodal, Tools Research, Coding, Visual QA
o4-mini Lightweight, Cost-efficient Fast API calls, lower-cost scenarios
GPT-4o General-purpose + speed Conversational & web tasks

OpenAI O3 API: How to Connect to OpenAI’s API

Step 1: Get API Credentials

Generate your API key from OpenAI’s API portal and verify organization access for o3.

Step 2: Install and Import the OpenAI Library

pip install --upgrade openai

from openai import OpenAI

Step 3: Initialize the Client Object

client = OpenAI(api_key="YOUR_API_KEY")

Step 4: Make an API Call

Example 1: Tool-Free Prompt

response = client.responses.create(
    model="o3",
    input=[{"role": "user", "content": "What’s the difference between inductive and deductive reasoning?"}]
)
print(response.output_text)

Example 2: Code and Math Problem Solving

prompt = "Write a Python function that solves quadratic equations and also explain the math behind it."
response = client.responses.create(
    model="o3",
    reasoning={"effort": "high"},
    input=[{"role": "user", "content": prompt}]
)
print(response.output_text)

Example 3: Code Refactoring Task

prompt = """Instructions:
- Highlight nonfiction books in red...
(React code block)
"""

response = client.responses.create(
    model="o3",
    reasoning={"effort": "medium", "summary": "detailed"},
    input=[{"role": "user", "content": prompt}]
)
print(response.output_text)

Example 4: Visual Reasoning with Images

import base64, mimetypes, pathlib

def to_data_url(path: str) -> str:
    mime = mimetypes.guess_type(path)[0] or "application/octet-stream"
    data = pathlib.Path(path).read_bytes()
    b64  = base64.b64encode(data).decode()
    return f"data:{mime};base64,{b64}"

prompt = "Create a schedule using this conference photo."
image_url = to_data_url("path/to/image.png")

response = client.chat.completions.create(
    model="o3",
    messages=[
        {
            "role": "user",
            "content": [
                { "type": "text", "text": prompt },
                { "type": "image_url", "image_url": { "url": image_url } }
            ]
        }
    ]
)
print(response.choices[0].message.content)

Managing Costs With Reasoning Tokens

O3 uses reasoning tokens in addition to regular input/output tokens.

print(response.usage)

To control cost:

response = client.chat.completions.create(
    model="o3",
    messages=...,
    max_completion_tokens=3000
)

Best Prompting Practices With O3

  • ✅ Be clear and concise
  • ❌ Avoid “think step-by-step” prompts
  • ✅ Use structured delimiters
  • ❌ Avoid too much context (e.g., unnecessary RAG)
  • ✅ Specify summary granularity when needed

Conclusion

OpenAI’s O3 model unlocks a new tier of autonomous, reasoning-capable APIs. Whether it’s solving math, processing images, or rewriting code — O3 helps AI “think before it speaks.”

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