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docs(benchmark): refresh parser comparison (#178)

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* docs: refresh benchmark comparison

* docs(site): refresh benchmark section

* docs: reframe benchmark positioning

* docs: focus benchmark positioning on best fit
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Abimael Martell
2026-07-16 17:43:53 -07:00
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README.md
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@@ -23,20 +23,21 @@ Built by [Firecrawl](https://firecrawl.dev) to handle text-based PDFs locally in
## Benchmark
Evaluated on the [opendataloader-bench](https://github.com/opendataloader-project/opendataloader-bench) corpus (200 PDFs). Only direct text extraction engines are shown — no OCR, no ML models. Scores are 0-1, higher is better.
Evaluated on the [opendataloader-bench](https://github.com/opendataloader-project/opendataloader-bench) corpus (200 PDFs). Only local engines without model-based PDF parsing are shown; OCR was disabled. Scores are 0-1, higher is better.
| Engine | Overall | Reading Order (NID) | Tables (TEDS) | Headings (MHS) | Speed (200 docs) |
|---|---|---|---|---|---|
| pdf-inspector | **0.875** | **0.915** | **0.814** | 0.788 | 3.3s |
| opendataloader | 0.831 | 0.902 | 0.489 | 0.739 | 3.0s |
| pymupdf4llm | 0.73 | 0.89 | 0.40 | 0.41 | 18s |
| markitdown | 0.59 | 0.84 | 0.27 | 0.00 | 23s |
| pdf-inspector | **0.875** | **0.915** | **0.814** | 0.788 | **2.8s** |
| liteparse | 0.870 | 0.908 | 0.693 | **0.811** | 13.9s |
| opendataloader | 0.843 | 0.912 | 0.489 | 0.760 | 9.8s |
| pymupdf4llm | 0.735 | 0.886 | 0.401 | 0.424 | 15.5s |
| markitdown | 0.583 | 0.879 | 0.000 | 0.000 | 6.7s |
For context, engines that use OCR/ML (docling, marker, mineru) score 0.83-0.88 overall but take 2-180 minutes on the same corpus — pdf-inspector reaches the top of that range without any OCR, in 3.3 seconds.
Results were refreshed on July 16, 2026, on an Apple M4 Pro. Engine versions were pdf-inspector 0.1.6, LiteParse 2.6.0, OpenDataLoader 2.1.1, PyMuPDF4LLM 0.2.0, and MarkItDown 0.1.4. Speed is the median of three complete corpus runs.
**Where we do well:** The best overall, reading-order, and table scores among the direct extraction engines shown.
For context, engines that use OCR or model-based document parsing (docling, marker, mineru) score 0.83-0.88 overall but take 2-180 minutes on the same corpus — pdf-inspector reaches the top of that range without either, in 2.8 seconds.
**Where we lag:** Some direct engines remain slightly faster, and OCR-based engines can recover text that has no usable PDF text layer.
**Best fit:** Native-text PDFs where speed, reading order, and table structure matter. pdf-inspector delivered the highest overall, reading-order, and table scores, along with the fastest complete run in this benchmark. That makes it a strong local default for reports, research papers, financial documents, invoices, and legal PDFs that need clean, structured Markdown without adding OCR latency or infrastructure.
Use the [paired benchmark harness](docs/benchmarking.md) to compare two local builds against the exact same corpus and evaluator revision.