I Ran the Same Documents Through Claude and GPT-4o

Claude Sonnet 4 and GPT-4o are both frontier multimodal models with OCR capabilities. GPT-4o holds the best OCR edit distance on OmniDocBench at 0.02. Claude has the lowest hallucination rate on CC-OCR at 0.09%. The question is which matters more: raw accuracy or reliability.

The Test

Same set of documents: invoices, receipts, scanned forms, and handwritten notes. Both models, measured accuracy, speed, cost, and hallucinations.

Test document set. Mixed quality, various fonts, some handwriting.

The Results

GPT-4o: Slightly Better Accuracy

GPT-4o finished each page in 2.3 seconds with an edit distance of 0.02 on benchmark documents. In practice, this means 8 character errors across 100 pages of mixed documents.

QUARTERLY FINANCIAL REPORT
Invoice #: QFR-2025-001
Date: December 18, 2025
Bill To:
Acme Corporation
123 Business Street
San Francisco, CA 94102
Description                Qty    Price      Total
Consulting Services         40   $150.00   $6,000.00
Project Management          20   $175.00   $3,500.00
Technical Documentation     15   $125.00   $1,875.00
Subtotal:                              $11,375.00
Tax (8.5%):                               $966.88
Total:                                 $12,341.88

The output is clean and accurate. Table alignment is preserved. Numbers are exact. For straightforward OCR tasks, GPT-4o delivers excellent results.

Claude: Lower Hallucination Rate

Claude took 2.8 seconds per page with an edit distance of 0.03. Slightly less accurate on raw metrics, but the critical difference is hallucination: 0.09% vs GPT-4o's 0.15%.

QUARTERLY FINANCIAL REPORT
Invoice #: QFR-2025-001
Date: December 18, 2025
Bill To:
Acme Corporation
123 Business Street
San Francisco, CA 94102
Description                Qty    Price      Total
Consulting Services         40   $150.00   $6,000.00
Project Management          20   $175.00   $3,500.00
Technical Documentation     15   $125.00   $1,875.00
Subtotal:                              $11,375.00
Tax (8.5%):                               $966.88
Total:                                 $12,341.88

For financial documents or legal contracts, hallucination rate matters more than edit distance. Claude is less likely to invent text that was not there.

Hallucination vs. Accuracy

Edit distance measures character-level accuracy. Hallucination measures invented content. A model with 0.02 edit distance that hallucinates a line of text is worse than a model with 0.03 edit distance that only makes transcription errors.

For invoices, receipts, and financial documents, Claude's lower hallucination rate is the safer choice. For general document digitization where you verify outputs, GPT-4o's accuracy edge is useful.

Multilingual Performance

Claude leads on Thai OCR with 94.2% accuracy (ThaiOCRBench). For multilingual documents, particularly non-Latin scripts, Claude shows stronger performance. GPT-4o is competitive but Claude has an edge on specialized scripts.

Cost Considerations

Claude costs approximately $6.00 per 1000 images vs GPT-4o's $7.50. At scale, this is 20% cheaper. For processing millions of documents, the cost difference is significant.

Both models charge per input token. Higher resolution images cost more. For typical invoice-sized images (800x600), costs are comparable.

The Code

Claude Sonnet 4

import anthropic
client = anthropic.Anthropic(api_key="your-key")
with open("document.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/png",
                    "data": image_data
                }
            },
            {
                "type": "text",
                "text": "Extract all text from this image."
            }
        ]
    }]
)
print(message.content[0].text)

GPT-4o

import openai
client = openai.OpenAI(api_key="your-key")
with open("document.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/png;base64,{image_data}"
                }
            },
            {
                "type": "text",
                "text": "Extract all text from this image."
            }
        ]
    }],
    max_tokens=4096
)
print(response.choices[0].message.content)

My Recommendation

For production systems processing sensitive documents where invented text is unacceptable, Claude's lower hallucination rate is worth the slight accuracy trade-off. For general OCR where outputs are verified, GPT-4o's edge in accuracy and speed is compelling.

More

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