finance

Invoice extraction: which AI model delivers the best cost-accuracy balance?

A practical guide to benchmarking AI models for high-volume invoice processing, with real extraction examples and cost analysis.

8 min read

Invoice processing is one of the most common document extraction use cases. Whether you’re processing 100 or 100,000 invoices per month, choosing the right AI model can mean the difference between a profitable automation and a costly experiment.

The challenge isn’t just accuracy—it’s finding the sweet spot between extraction quality, processing cost, and speed. Let’s explore how to benchmark AI models for invoice extraction and find the optimal choice for your volume and accuracy requirements.

The invoice extraction challenge

Invoices come in countless formats: structured PDFs, scanned documents, handwritten receipts, multi-language documents. A typical invoice extraction workflow needs to capture:

The complexity multiplies when dealing with international invoices, varying quality scans, and industry-specific formats.

Example scenario

Let’s look at a concrete example of what invoice extraction looks like in practice.

Sample input

A standard B2B invoice from a software vendor containing:

Sample output

{
  "vendor": {
    "name": "CloudTech Solutions B.V.",
    "address": "Herengracht 182, 1016 BR Amsterdam",
    "vat_number": "NL123456789B01"
  },
  "invoice": {
    "number": "INV-2024-0847",
    "date": "2024-03-15",
    "due_date": "2024-04-14"
  },
  "line_items": [
    {
      "description": "Enterprise SaaS License (Annual)",
      "quantity": 1,
      "unit_price": 2400.00,
      "total": 2400.00
    },
    {
      "description": "Implementation Support (8 hours)",
      "quantity": 8,
      "unit_price": 150.00,
      "total": 1200.00
    },
    {
      "description": "Training Workshop",
      "quantity": 1,
      "unit_price": 500.00,
      "total": 500.00
    }
  ],
  "totals": {
    "subtotal": 4100.00,
    "vat_rate": 21,
    "vat_amount": 861.00,
    "total": 4961.00
  },
  "payment": {
    "iban": "NL91ABNA0417164300",
    "terms": "Net 30"
  }
}

Model comparison

Running this invoice through multiple AI models reveals interesting trade-offs:

Model comparison
5 models
# ModelAccuracyCostTime
1 GPT-4o 96.1% $0.028 2.8s
2 Gemini 2.0 Flash 93.4% $0.002 1.1s
3 GPT-4o-mini 91.8% $0.003 1.3s
4 Claude 3.5 Haiku 89.2% $0.009 1.0s
5 Gemini 1.5 Flash 87.6% $0.001 0.9s
Best accuracy 96.1%
Lowest cost $0.001
Fastest 0.9s

Field-level accuracy matters

Aggregate accuracy scores can hide important details. When you dig into field-level performance, patterns emerge that can inform your architecture decisions.

Field-level accuracy
6 fields
Field categoryGemini FlashGPT-4o-miniGPT-4o
Vendor name 96.2% 95.8% 97.4%
Invoice number 97.8% 96.9% 98.2%
Total amount 95.4% 94.2% 97.1%
Line items 91.6% 89.8% 94.2%
Tax breakdown 86.4% 84.7% 92.8%
Bank details (IBAN) 82.1% 79.3% 89.6%

Notice how smaller models like Gemini Flash and GPT-4o-mini excel at high-frequency fields (vendor names, invoice numbers) but struggle with complex structured data like tax breakdowns and bank details.

The hybrid approach

Based on benchmark data, a two-stage architecture often delivers the best economics:

Stage 1: Fast model for initial extraction Use a cost-effective model (Gemini 2.0 Flash, GPT-4o-mini) for initial extraction. These models handle 80-90% of invoices perfectly.

Stage 2: Premium model for low-confidence cases Route documents with low confidence scores on critical fields to a premium model (GPT-4o) for re-extraction.

Approach comparison
3 approaches
# ApproachAvg cost/docAccuracyThroughput
1 Gemini Flash + GPT-4o hybrid $0.006 94.8% ~2,800/hr
2 GPT-4o only $0.028 96.1% ~1,200/hr
3 Gemini Flash only $0.002 93.4% ~3,200/hr
Best value Hybrid
Cost savings 79%

The hybrid approach delivers near-premium accuracy at 79% lower cost than using GPT-4o for everything.

Key insights

1. Don’t default to the most expensive model

Premium models offer marginal accuracy improvements that may not justify 4-8x cost increases for your use case. Benchmark with your actual documents first.

2. Field-level analysis reveals architecture opportunities

Understanding which fields each model struggles with enables hybrid architectures that optimize for cost and accuracy simultaneously.

3. Document quality matters more than model choice

Low-quality scans and handwritten documents are challenging for all models. Improving document quality at the source often delivers better ROI than model upgrades.

4. Benchmark with your actual documents

Generic benchmarks don’t reflect your specific document mix. Run tests on representative samples from your actual production volume.

Try it yourself

Ready to find the optimal model for your invoice processing? LLMCompare lets you:

Stop guessing which model to use. Let your data decide.