GPT-5.4 costs money but thinks. PaddleOCR is free but extracts. I tested both to find out when the thinking is worth paying for. Same invoice. Different approaches.
| Metric | PaddleOCR | GPT-5.4 |
|---|---|---|
| Time | 4.85s | 7.58s |
| Confidence | 99.6% | N/A |
| Character errors | 0 | 0 |
| Table structure | Lost | Preserved |
| Cost per image | $0 | ~$0.01 |
| Tokens used | N/A | 943 |

Test invoice. 800x600 pixels, white background, standard fonts.
Every character correct, but the table became a flat list of words.
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
...The table headers align with values — “Web Development Services” has Qty 40, Price $150.00, Total $6,000.00.
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: $9,743.30Get the full OCR comparison spreadsheet
30+ models × 8 benchmarks, accuracy + price per page. We email it and keep it current.
This gives you 99% of documents at $0/each and 1% at $0.01/each.
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)import base64
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)Get the full OCR comparison spreadsheet
30+ models × 8 benchmarks, accuracy + price per page. We email it and keep it current.