GPT-4o vs PaddleOCR: API vs Open Source

GPT-4o costs money but thinks. PaddleOCR is free but extracts. I tested both to find out when the thinking is worth paying for.

The Test

Same invoice, both systems, measured everything.

The Results

The Key Difference: Structure

Both got every character right. Zero errors. The difference is what they did with the table.

PaddleOCR Output

INVOICE
Invoice #: INV-2025-001
Date: December 16, 2025
Bill To:
John Smith
123 Main Street
San Francisco, CA 94102
Description
Qty
Price
Total
Web Development Services
40
$150.00
$6,000.00
...

"Description", "Qty", "Price", "Total" are separate lines. The table became a list of words. If you want to know that "Web Development Services" costs "$150.00", you need to write code to reconstruct that relationship.

GPT-4o Output

INVOICE

Invoice #: INV-2025-001
Date: December 16, 2025

Bill To:
John Smith
123 Main Street
San Francisco, CA 94102

Description                 Qty   Price         Total
Web Development Services    40    $150.00     $6,000.00
UI/UX Design                20    $125.00     $2,500.00
Server Hosting (Annual)      1     $480.00       $480.00

                            Subtotal:        $8,980.00
                            Tax (8.5%):       $763.30
                            Total:          $9,743.30

The table headers align with values. You can see that "Web Development Services" has Qty 40, Price $150.00, Total $6,000.00. GPT-4o understood the document.

When to Pay for GPT-4o

GPT-4o wins when you need to understand documents, not just extract text:

• Tables with complex layouts
• Documents where you want to ask questions ("What's the total?")
• Mixed content with forms, tables, and text
• Small batches where $0.01/image is irrelevant

When PaddleOCR is Better

PaddleOCR wins when you're processing at scale and can write your own parsing logic:

• 100,000 documents = $1,000 with GPT-4o, $0 with PaddleOCR
• Privacy-sensitive documents that can't leave your server
• Consistent document formats where you can write regex
• Batch processing where speed matters more than structure

The Code

PaddleOCR

from paddleocr import PaddleOCR

ocr = PaddleOCR(lang='en')
result = ocr.predict('invoice.png')

for item in result:
    for text in item.get('rec_texts', []):
        print(text)

GPT-4o

import base64
from openai import OpenAI

client = OpenAI()

with open('invoice.png', 'rb') as f:
    img = base64.b64encode(f.read()).decode()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Extract all text from this image."},
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}
    ]}]
)

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

The Hybrid Approach

For production systems, consider both:

1. Use PaddleOCR for bulk processing (cheap, fast)
2. Send complex or failed documents to GPT-4o (accurate, understands structure)
3. Extract with PaddleOCR, then ask GPT-4o questions about the text

This gives you 99% of documents at $0/each and 1% at $0.01/each.

My Recommendation

Start with PaddleOCR. It's free and handles most cases. When you hit documents it struggles with - complex tables, mixed layouts - send those to GPT-4o.

More

• Getting started with OCR in Python
• PaddleOCR vs Tesseract comparison
• Best OCR for invoices