ChatGPT-4o VS o1-mini

When choosing between OpenAI's frontier models, developers and businesses often struggle to decide between the versatile GPT-4o and the reasoning-focused o1-mini. While o1-mini is engineered to excel in STEM fields, GPT-4o remains a powerhouse for general tasks. This comparison breaks down the technical specs, benchmarks, and real-world performance to help you decide.

1. Specifications: o1-mini vs. GPT-4o

The primary technical distinction lies in output capacity and speed. o1-mini is built for heavy lifting with a massive output token limit, whereas GPT-4o prioritizes speed.

2. Technical Benchmarks

Based on official release notes and open benchmarks, here is how they stack up in specific domains:

• 🎓 Undergraduate Knowledge (MMLU): GPT-4o (88.7%) vs o1-mini (85.2%)
• 🧠 Graduate Reasoning (GPQA): GPT-4o (53.6%) vs o1-mini (60.0%)
• 💻 Coding (Human Eval): GPT-4o (90.2%) vs o1-mini (92.4%)
• 🔢 Math (MATH): GPT-4o (70.2%) vs o1-mini (90.0%)

3. Practical Tests: Real-World Scenarios

Benchmarks are useful, but real-world performance reveals the true capabilities. We tested logical reasoning, language comprehension, and coding.

4. API Pricing Comparison

Contrary to typical trends where newer "mini" models are cheaper, o1-mini commands a premium due to its reasoning capabilities.

5. How to Compare Them Yourself

You can run a direct comparison using the Python script below. Simply add your API key.

 import openai

def main(): # Insert your API key setup here model1 = 'gpt-4o-2024-08-06' model2 = 'o1-mini' selected_models = [model1, model2]

for model in selected_models:
    try:
        response = client.chat.completions.create(
            model=model,
            messages=[{'role': 'user', 'content': "Your Prompt Here"}],
            max_tokens=2000,
        )
        print(f"{model} response: {response.choices[0].message.content}")
    except Exception as error:
        print(f"Error with {model}:", error)
if name == "main": main() 

Frequently Asked Questions (FAQ)

1. Which model is better for coding, o1-mini or GPT-4o?

o1-mini is generally better for complex algorithmic coding and backend logic. However, GPT-4o often performs better for frontend tasks like HTML, CSS, and UI design.

2. Is o1-mini cheaper than GPT-4o?

No, o1-mini is slightly more expensive. Input costs are approximately 20% higher, and output costs are also higher compared to the standard GPT-4o model.

3. Can o1-mini process images?

Currently, GPT-4o is the superior choice for multimodal tasks, including image analysis and vision capabilities. o1-mini is optimized primarily for text-based reasoning.

4. What is the output token limit for o1-mini?

o1-mini supports a massive output of 64k tokens, which is significantly higher than GPT-4o's 16k token limit, making it ideal for generating long documents or extensive code files.