---
title: "Best PDF to JSON Extraction Tools, Independently Benchmarked, 2026 | Openbenchmarks"
description: "Extend leads 7 specialised document parsers at 93.1% accuracy on contracts (JSON proxy). Reducto, LlamaParse, Datalab and 3 more benchmarked, 2026."
url: https://openbenchmarks.com/document-processing/best-pdf-to-json-extraction-tools
---

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[Detailed benchmark →](https://openbenchmarks.com/document-processing) 

[benchmarks](https://openbenchmarks.com/) / [document processing](https://openbenchmarks.com/document-processing) / best pdf to json extraction tools 

independently benchmarked · 1,500 questions · 94 contracts · updated Oct 25, 2026

# Best PDF to JSON Extraction Tools, 2026

Extend leads 7 specialised document parsers at 93.1% accuracy on contracts (JSON proxy). Reducto, LlamaParse, Datalab and 3 more benchmarked, 2026.

**What this page compares.** PDF-to-JSON extraction depends on the parser first preserving which text is operative, deleted, or replaced. If that meaning is lost, a perfectly valid JSON object can still contain the wrong contract terms. This benchmark tests that semantic input layer through a common Markdown representation rather than scoring JSON syntax or schema adherence.

**How to read the result.** The table ranks complete runs by downstream answer accuracy. Gap closed places each result between the markup-blind baseline and the best-case parse, so raw accuracy is not mistaken for parser capability alone.

**Workload and controls.** Each measured configuration processed the same 94 tagged contract PDFs. The same downstream reader then answered the same 1,500 questions. A best-case parse and a markup-blind baseline bound the score.

\[01\] measured ranking

## Ranked by downstream answer accuracy

The table ranks complete runs by downstream answer accuracy. Gap closed places each result between the markup-blind baseline and the best-case parse, so raw accuracy is not mistaken for parser capability alone.

| Rank | Provider | Type | Accuracy | Gap closed | Median / P95 latency | Parser $ / 1k pages | Parser $ / 1k correct | Total tokens / 1k correct | List price |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | [Extend](https://docs.extend.ai/) engine=parse\_performance | Specialised document parsers | **93.1%** ± 0.3% | 97.9% | 17s / 26s | $20.00 | $32.73 | 22.5M | [$0.020 / page](https://www.extend.ai/pricing) |
| 2 | [Reducto](https://docs.reducto.ai/) model=r-1 | Specialised document parsers | **84.8%** ± 0.3% | 85.9% | 5s / 12s | $10.00 | $17.97 | 26.4M | [$0.010 / page](https://reducto.ai/pricing) |
| 3 | [LlamaParse agentic plus](https://docs.cloud.llamaindex.ai/llamaparse/overview) tier=agentic\_plus · 2026-09-24 | Long running agentic parsers | **84.7%** ± 0.3% | 85.8% | 82s / 138s | $56.25 | $101.17 | 24.0M | [$0.05625 / page](https://developers.llamaindex.ai/llamaparse/general/pricing/) |
| 4 | [LlamaParse agentic](https://docs.cloud.llamaindex.ai/llamaparse/overview) tier=agentic · 2026-09-29 | Specialised document parsers | **83.8%** ± 0.6% | 84.5% | 16s / 71s | $12.50 | $22.71 | 23.9M | [$0.0125 / page](https://developers.llamaindex.ai/llamaparse/general/pricing/) |
| 5 | [Datalab track changes](https://documentation.datalab.to/) track-changes endpoint | Specialised document parsers | **80.7%** ± 0.4% | 80.0% | 34s / 50s | $10.00 | $18.89 | 25.0M | [$0.010 / page](https://www.datalab.to/pricing) |
| 6 | [Datalab convert](https://documentation.datalab.to/) convert · mode=accurate | Specialised document parsers | **63.9%** ± 3.4% | 55.7% | 24s / 55s | $10.00 | $23.86 | 30.2M | [$0.010 / page](https://www.datalab.to/pricing) |
| 7 | [Pulse](https://docs.runpulse.com/) model=pulse-ultra-2 | Specialised document parsers | **48.4%** ± 0.4% | 33.5% | 53s / 102s | $15.00 | $47.17 | 89.4M | [$0.015 / page](https://www.runpulse.com/pricing) |
| 8 | [Mistral OCR](https://docs.mistral.ai/capabilities/OCR/basic%5Focr/) model=mistral-ocr-4-1 | Specialised document parsers | **36.9%** ± 0.4% | 16.8% | 15s / 72s | $4.00 | $16.54 | 51.2M | [$0.004 / page](https://mistral.ai/pricing/api/) |
| 9 | [Unstructured](https://docs.unstructured.io/transform/api/parseRun) parse · profile=best | Specialised document parsers | **27.0%** ± 0.2% | 2.6% | 65s / 119s | $15.00 | $84.67 | 70.3M | [$0.015 / page](https://docs.unstructured.io/transform/billing) |

\[02\] how it was measured

## How document-processing accuracy, latency, and cost were measured

This is the compact protocol. Corpus construction, all nine task types, parser settings, scoring rules, and cost treatment are documented in the [full benchmark methodology →](https://openbenchmarks.com/document-processing#methodology) 

* **Same corpus.** 94 legal contracts and 1,500 questions were reused for every measured configuration; no vendor received an easier document set.
* **One changing layer.** Every arm received the complete PDF and returned Markdown. The downstream reader, question, prompt, and scoring path stayed fixed, so the parser output was the variable under test.
* **Independent answer key.** Correct and stale answers were derived from tracked changes in the source Word files before vendor output was produced. The residual-answer judge was not shown the parser identity.
* **Bounded accuracy.** The best-case reference preserves every deletion; the markup-blind baseline removes every mark. Gap closed reports where each parser landed between those controls.
* **Separate operational metrics.** Parse latency and measured parser spend are reported beside semantic accuracy, never blended into a synthetic score. Published list price remains separate from measured corpus spend.
* **Reproducible evidence.** The benchmark runner and published data are open in [openbenchmarks-labs/document-processing ↗](https://github.com/openbenchmarks-labs/document-processing).

\[03\] how to use this result

## What this ranking establishes

### Semantic accuracy

A correct answer follows the language the parties agreed. A stale answer follows language they struck, while a fused answer combines deleted and surviving text into a value that never existed.

### Independent controls

The best-case reference preserves every deletion. The baseline removes every mark. Gap closed places each measured system between those controls instead of pretending 100% is always reachable.

### Scope

PDF-to-JSON extraction depends on the parser first preserving which text is operative, deleted, or replaced. If that meaning is lost, a perfectly valid JSON object can still contain the wrong contract terms. This benchmark tests that semantic input layer through a common Markdown representation rather than scoring JSON syntax or schema adherence.

\[04\] faq

## Questions answered by this comparison

### Which specialised document parser leads best pdf to json extraction tools?

Extend leads the specialised document parsers in this table at 93.1% accuracy. The result comes from 1,500 questions across 94 contracts.

### What does document-parsing accuracy mean here?

Accuracy is the share of questions answered from the operative contract language. The markup-blind baseline scored 25.2%. Stale rate separately counts answers taken from deleted language.

### How should latency and cost be read beside accuracy?

Extend recorded 17s median parse time and $20.00 in measured parse cost per 1,000 pages. The full board also divides parse cost and the answering agent's tokens by correct answers, which shows what a parser costs for every 1,000 right answers it leads to.

### How was this document-processing comparison independently benchmarked?

Each system converted the same full PDFs to Markdown. The same reader model then answered the same hidden-ground-truth questions; vendor identity was not shown to the residual-answer judge.

### What does this page not test?

PDF-to-JSON extraction depends on the parser first preserving which text is operative, deleted, or replaced. If that meaning is lost, a perfectly valid JSON object can still contain the wrong contract terms. This benchmark tests that semantic input layer through a common Markdown representation rather than scoring JSON syntax or schema adherence.

\[05\] related comparisons

## Related document-processing pages

* [Document Processing Benchmark: complete leaderboard and methodology →](https://openbenchmarks.com/document-processing)
* [Best Document Parsing APIs](https://openbenchmarks.com/document-processing/best-document-parsing-apis)
* [Best Document Parser APIs](https://openbenchmarks.com/document-processing/best-document-parser-apis)
* [Best Document Parsing Tools](https://openbenchmarks.com/document-processing/best-document-parsing-tools)
* [Best Document Parser Tools](https://openbenchmarks.com/document-processing/best-document-parser-tools)
* [Best Document Parsing Software](https://openbenchmarks.com/document-processing/best-document-parsing-software)
* [Best Document Parser Software](https://openbenchmarks.com/document-processing/best-document-parser-software)

\[06\] source benchmark

## Read the complete Document Processing Benchmark

Task design, corpus transformation, parser settings, scoring, controls, cost accounting, and both tagged-PDF and scanned-PDF leaderboards live on the [Document Processing Benchmark →](https://openbenchmarks.com/document-processing#methodology) 

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