PeterParser vs LlamaParse vs Unstructured: 2026 Document Parsing Comparison
Choosing a document parsing API in 2026 means deciding between dozens of options that all claim “AI-powered extraction.” This comparison focuses on the three APIs developers actually use for production workloads: PeterParser, LlamaParse, and Unstructured.
We tested all three on the same corpus: 500 financial PDFs (invoices, bank statements, tax forms) with complex tables, multi-column layouts, and scanned pages.
Feature Comparison
| Feature | PeterParser | LlamaParse | Unstructured |
|---|---|---|---|
| Table accuracy | 99.5% | ~92% | ~75-95% |
| Extraction presets | 16 built-in | None | None |
| Custom output templates | ✅ Any JSON schema | ❌ | ❌ |
| Source grounding | ✅ Char-level | ❌ | ❌ |
| PII detection/redaction | ✅ 9 types | ❌ | ❌ |
| RAG chunking | ✅ 3 strategies | Via LlamaIndex | ✅ Built-in |
| Async + webhooks | ✅ + SSE events | ✅ Polling only | ✅ Via platform |
| Large docs (1000+ pages) | ✅ Auto-chunked | ✅ | ✅ |
| Website parsing | ✅ CSS selectors | ✅ | ❌ |
| Output formats | 7 (JSON, MD, HTML, XML, CSV, Text, DocTags) | 2 (JSON, MD) | 3 (JSON, HTML, Text) |
| OCR | ✅ Built-in | ✅ | ✅ Tesseract |
| Self-hosted option | Docker | ❌ Cloud only | ✅ Open source |
| Document classification | ✅ Auto-detect | ❌ | ✅ |
When to Use Each
Choose PeterParser when:
- → You need structured JSON output matching a specific schema (not just text/markdown)
- → Table accuracy is critical (financial documents, invoices, bank statements)
- → You need an audit trail with char-level grounding
- → PII detection and redaction are requirements (healthcare, fintech)
- → You process diverse document types and want presets instead of custom prompts
Choose LlamaParse when:
- → You're already in the LlamaIndex ecosystem
- → You primarily need markdown output for LLM context
- → Speed matters more than structured extraction (6-second processing regardless of size)
- → Your documents are text-heavy with simple layouts
Choose Unstructured when:
- → You need to self-host everything (open source)
- → You're building complex ETL pipelines with custom connectors
- → You need document partitioning more than structured extraction
- → You want a free tier with no API key
Pricing (10,000 Invoices/Month)
| API | Cost | Notes |
|---|---|---|
| PeterParser | $100 | $0.01/page, invoice preset, volume discount at 10k |
| LlamaParse | $130-200 | Varies by plan, no preset — custom prompting needed |
| Unstructured (hosted) | $200+ | Page-based pricing, higher for complex layouts |
| Unstructured (self-hosted) | $0 + infra | Free software, but GPU servers cost $500+/mo |
The Verdict
If you need raw text or markdown for LLM context, LlamaParse is simple and fast. If you need to own your infrastructure, Unstructured is the only open-source option. If you need structured extraction with specific schemas, grounding, PII handling, and production-grade reliability, PeterParser is the most complete API available in 2026.
The biggest differentiator is char-level grounding. No other parsing API tells you where in the document each value was found. For regulated industries (finance, healthcare, legal), that audit trail isn't optional — it's required.