Every benchmark you’ve read tested clean PDFs. That’s not what hits your pipeline.
I ran 200 real invoices through LlamaParse, Google Document AI, and AWS Textract — scanned, rotated, faded, handwritten, multi-page. The kind of documents your accounts payable team actually gets, not the ones vendors put in demos. I tracked field accuracy, line-item extraction, processing time, and the real cost per correctly extracted page.
The ranking on clean PDFs is not the ranking on messy ones. And the cheapest tool on the pricing page is not the cheapest in practice.
How I Tested 200 Real Invoices (And Why That Matters)
The dataset: 60 clean digital PDFs, 80 scanned at mixed quality, 40 with handwritten fields (date stamps, signatures, manual corrections), 20 rotated or multi-column. Every invoice was real. None were generated for the test.
Each went through three pipelines: LlamaParse in agentic mode, Google Document AI’s Invoice Parser, and AWS Textract’s AnalyzeExpense. I scored each at four levels: field accuracy (vendor, total, date, tax), line-item accuracy, processing time, and post-extraction effort to get usable JSON.
Most published benchmarks use 1–100 documents. Two hundred is 2–4x larger, which matters because small datasets hide the variance that bites you in production. The single failure mode that destroys your week happens on document 137, not document 7.
Big dataset, fair rules. Now where does each one actually break?
Accuracy: Where Each Tool Wins and Where It Falls Apart
Headline field-level accuracy across all 200: LlamaParse ~91%, AWS Textract ~84%, Google Document AI ~80%.
Split by document type, the picture flips.
On clean digital invoices, AWS wins on line-items — around 82% accuracy on tables, more than double Google’s 40% on the same documents. When the structure is clean, Textract’s table extraction is the strongest in the cloud-giant tier.
On the worst-quality scans, Google’s OCR core still leads. The 200+ language support and Gemini-powered layout understanding genuinely help on faded, low-DPI documents — field-level recovery edges out the other two when the input is barely readable.
On handwritten and rotated documents, LlamaParse pulls clear of both. The VLM-based agentic mode reads context the way a person does. It doesn’t just OCR a date stamp; it figures out that “06/14” near a signature is a date. AWS dropped handwriting below 72%. Google did slightly better at ~75%. LlamaParse cleared 90%.
Where each one fails: Google’s line items collapsed around 40% on multi-column layouts. AWS hallucinated fewer fields but missed handwriting entirely. LlamaParse occasionally skipped whole blocks on documents over 30 pages — the same failure mode F22 Labs found on long financial reports.
There is no universal winner on accuracy. There’s a winner per document profile. That would settle it — except accuracy alone doesn’t pay the bill.
The Real Cost Per Page (Not What the Pricing Page Says)
List prices don’t tell you what you’ll actually spend.
AWS AnalyzeExpense and Google Invoice Parser are both roughly $0.10/page at typical volumes. LlamaParse runs about $0.003–$0.012/page depending on mode (1,000 credits = $1.25; agentic mode burns more credits than basic).
But list price is the wrong number. What matters is effective cost per correctly extracted page — list price divided by accuracy. On the 200-invoice run:
- LlamaParse: ~$0.013 per correct extraction
- AWS Textract: ~$0.119
- Google Document AI: ~$0.125
Total cost for the run: LlamaParse under $3, AWS around $20, Google around $20 — before Google’s processor hosting fees ($0.05/hour per deployed processor, roughly $36/month) and before any cross-cloud egress.
Then there’s the post-processing tax. AWS returns nested JSON you have to flatten and normalize. Google returns its proprietary Document object — same problem. LlamaParse returns LLM-ready markdown plus structured JSON keyed to your schema. On this run, that delta saved roughly six hours of glue code I’d otherwise have written to get AWS or Google output usable in a downstream RAG pipeline. And when you’re storing extracted data in a vector database, clean output format saves even more integration time. At any honest developer rate, that’s $300–$900 of hidden cost the pricing pages don’t mention.
Cost says LlamaParse. Accuracy says it depends. So when does each one win on the operational stuff?
Speed and Developer Experience: The Hidden Tiebreaker
Average processing time per invoice: AWS ~2.9 seconds, Google ~4.5 seconds, LlamaParse ~12–18 seconds in agentic mode (~7 in basic). Real-time, AWS wins. For batch jobs, LlamaParse’s async API and smart caching (re-parsing the same document costs 0 credits) flatten the gap.
Time to first working extraction matters more than people admit. AWS took 30 minutes — assuming you’re already in AWS with IAM sorted. Google took 60–90 minutes including processor deployment. LlamaParse: 10 minutes from API key to markdown output.
Output format quietly decides who you pick. LlamaParse’s markdown drops straight into a RAG pipeline with no transformation. AWS hands you nested JSON. Google returns its proprietary Document object. Both need a normalizer before any LLM downstream can use them.
The ecosystem tax is real too. Braincuber documented $3,700/month in cross-cloud egress that erased the OCR savings of going with a non-native provider. Twelve to eighteen engineering hours just to wire up cross-cloud document processing — work you redo every time something changes. And once you’re in production, monitoring extraction accuracy in production becomes its own engineering investment.
Now you know speed, setup, and output quality. Which one do you actually pick?
Which One to Pick (Without the Marketing Spin)
Pick LlamaParse if you’re building an LLM or RAG product, you process mixed-quality documents (scans, handwriting, rotation), or cost-per-correct-extraction matters more than raw speed. The markdown output alone saves days of integration work.
Pick AWS Textract if you’re AWS-native, your invoices are mostly clean digital with heavy tables and line items, and Lambda/S3 triggers are non-negotiable.
Pick Google Document AI if you’re GCP-native, you need 200+ language OCR, or you’re processing the worst-quality scans where Google’s OCR core still leads.
The anti-pattern: picking a tool because it’s the giant you already use. Cross-cloud egress and the accuracy gap can blow up the savings within a quarter — the same trap I dug into when benchmarking the real ROI of fine-tuning vs prompts.
The Bottom Line
Clean-PDF benchmarks said one thing. Two hundred messy invoices said another.
If I had to pick one tool for a new product today, it’s LlamaParse — highest accuracy across mixed document quality, lowest effective cost per correct page, fastest path to a working pipeline. If you’re locked into a cloud, stay there for the easy documents and route the breakers (handwriting, rotated scans, faded copies) through LlamaParse as a fallback.
The popular pick is rarely the cheapest pick once you measure cost per correct extraction. The cloud giant you already pay for is usually the wrong default.