benchmarks/document processing/best pdf parser apis for redlined contracts

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

Best PDF Parser APIs for Redlined Contracts, 2026

Claude Fable 5.1 leads 8 document parsing providers at 89.1% accuracy on redlined contracts. LlamaParse, Reducto, Datalab and 4 more benchmarked, 2026.

What this page compares. A redlined-contract parser must preserve not just the words on the page but which terms were deleted, retained, or replaced. This benchmark tests whether that edit history survives PDF parsing well enough for a downstream reader to answer from the negotiated agreement rather than superseded language.

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	Claude Fable 5.1claude-fable-5-1 · pdf in · bedrock	Frontier models (VLMs)	89.1%	95.5%	327s / 461s	$109.39	$187.18	21.3M	$11 / $55 per 1M tokens
2	LlamaParse agentic plustier=agentic_plus · 2026-09-24	Long running agentic parsers	83.4%	86.5%	91s / 148s	$56.25	$102.79	24.3M	$0.05625 / page
3	LlamaParse agentictier=agentic · 2026-09-24	Specialised document parsers	80.0%	81.1%	52s / 77s	$12.50	$23.81	24.8M	$0.0125 / page
4	Reductomodel=r-1 · 2026-09-25 build	Specialised document parsers	78.7%	79.1%	7s / 10s	$10.00	$19.36	28.3M	$0.010 / page
5	Datalab track changestrack-changes endpoint	Specialised document parsers	78.6%	78.9%	34s / 45s	$10.00	$19.39	25.6M	$0.006 / page
6	Extendengine=parse_performance	Specialised document parsers	74.8%	72.9%	28s / 41s	$25.00	$50.94	27.3M	$0.025 / page
7	GPT-6 Astragpt-6-astra · pdf in	Frontier models (VLMs)	63.3%	54.6%	228s / 309s	$51.00	$122.85	30.8M	$10 / $50 per 1M tokens
8	Datalab convertconvert · mode=accurate	Specialised document parsers	62.4%	53.2%	23s / 55s	$10.00	$24.42	30.7M	$0.010 / page
9	Pulsemodel=pulse-ultra-2	Specialised document parsers	52.7%	37.8%	43s / 98s	$15.00	$43.41	81.5M	$0.015 / page
10	Mistral OCRmodel=mistral-ocr-4-1	Specialised document parsers	43.4%	23.1%	11s / 21s	$4.00	$14.04	43.3M	$0.004 / page

[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 →

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 ↗.

[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

A redlined-contract parser must preserve not just the words on the page but which terms were deleted, retained, or replaced. This benchmark tests whether that edit history survives PDF parsing well enough for a downstream reader to answer from the negotiated agreement rather than superseded language.

[04] faq

Questions answered by this comparison

Which provider leads best pdf parser apis for redlined contracts?

Claude Fable 5.1 leads this table at 89.1% 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 28.8%. Stale rate separately counts answers taken from deleted language.

How should latency and cost be read beside accuracy?

Claude Fable 5.1 recorded 327s median parse time and $109.39 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 documents does this benchmark cover?

[05] related comparisons

[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 →