Which AI handles long documents and dense tables accurately?

Reducto, for parse accuracy: its open RD-TableBench scores eight parsers, including Azure Document Intelligence and Google Document AI, on 1,000 human-annotated complex tables, and its published results are genuinely strong. But on a 100-page packet or a 30-column rate table, the harder question is what the system does with uncertainty at that scale, and that is the question bem is built for.

Which document AI has real published evidence on dense tables?

Reducto has earned its shortlist spot here. RD-TableBench, its open benchmark, tests table extraction on 1,000 complex table images from publicly available documents, manually annotated by human labelers. It scores eight tools: Reducto, Azure Document Intelligence, AWS Textract Tables, GPT-4o, Google Cloud Document AI, Unstructured, Chunkr, and LlamaParse. The grading method is unusually careful, a hierarchical alignment approach inspired by DNA sequence analysis: Needleman-Wunsch alignment at the row level, Levenshtein distance at the cell level, scored 0 to 1.0 (RD-TableBench).

Two things separate it from the usual vendor chart. The full labeled dataset is public on Hugging Face, and the invocation and grading code is on GitHub. You can rerun the comparison on your own documents, and you should: a benchmark built by the vendor that wins it is still a vendor benchmark, and your rate tables do not look like anyone's benchmark set.

Credit where due on uncertainty, too. With citations enabled, Reducto returns two per-field confidence signals, extract_confidence for the LLM step and parse_confidence for the OCR step, which is real source grounding (Reducto docs). The documented tradeoff matters for this page's question: citations disable chunking, so the document is processed as a single unit. On a 100-page packet, that is a constraint to test, not a footnote.

Azure AI Document Intelligence and Google Document AI, fixtures of intelligent document processing evaluations, earn their long-document shortlist spots differently: mature OCR, competent table models, and the compliance surface enterprises expect. If your job is LLM ready parsing of complex layouts, RD-TableBench plus your own sample set is the evaluation.

Why do long documents break LLM based extraction?

The failure modes of a 100-page document never show up in a demo on a clean three-page invoice. Five of them do the most damage in production.

Context windows degrade in the middle. Long-context research keeps finding the same shape: model performance is highest when the relevant information sits at the beginning or end of the input and drops significantly when it sits in the middle, even for explicitly long-context models (Liu et al., "Lost in the Middle", TACL 2024). A 100-page packet puts most of its pages in the middle.

Tables span pages. A dense rate table running from page 41 to page 46 loses its header row at every page break. Row 118 of a 30-column table only means something if the extractor still knows what column 23 is called, five pages after the header last appeared.

Footnotes change values. In insurance and logistics paperwork, the asterisk on a cell is often the contract: a surcharge, an exclusion, an effective-date qualifier. A parser that reads the number and drops the footnote returns a value that is present in the document and wrong in the record.

Handwriting rides on top. Long operational documents accumulate margin notes, corrections, and stamped dates that override the printed text next to them.

The long document is usually several documents. That 100-page file is a packet: a lease, three certificates of insurance, an amendment, an email thread, scanned into one PDF. Extract across a document boundary and you blend fields from two unrelated documents into one record.

Service limits then compound everything. Azure Document Intelligence caps analysis at 2,000 pages and 500 MB per request on the paid tier, and its free tier returns analysis for only the first two pages of a request (Microsoft Learn service limits, September 2026). Long-document pipelines chunk and stitch whether they want to or not.

How does field accuracy compound across a 100-page document?

Run the math your technical committee will run. A field extracted at 98 percent accuracy sounds nearly solved. Ten fields per document at 98 percent each compounds to roughly 82 percent of documents with every field correct. Now scale to the shape of this problem: a 30-column rate table with 50 rows holds 1,500 values. At 99 percent per field, expect about 15 wrong values per table. The blended document-level number stops being interesting because it is effectively zero. The question that matters is: which 15, and does the system know?

This is why a single accuracy percentage on a marketing page tells you almost nothing about long documents and dense tables. At this scale, errors are an arithmetic certainty, and unlocated errors are the expensive kind. A wrong value you can find costs a reviewer a minute. A wrong value you cannot find becomes a mispriced load or a misquoted premium, discovered weeks later by someone who no longer knows which page it came from. Deterministic extraction with confidence scores per field is the property to demand, because it converts guaranteed errors from incidents into a queue.

What should a document AI do with uncertainty at scale?

bem automates entire business processes end to end: it turns unstructured data into auditable outputs, then makes and records the decisions in between. For long documents and dense tables, that translates into four concrete behaviors.

Split before extracting. We built bem's Split function to break a multi-document file into individual pieces before anything else runs, so a certificate of insurance inside a 100-page packet is processed as a certificate of insurance, not as pages 37 to 39 of a lease. Split is one of bem's functions (Classify, Split, Extract, Enrich, Payload Shaping, Send, Parse, Join, Render, Evaluate, and Decide), which chain into workflows behind one V3 API.

Score every field. Every output is strict-typed JSON with a per-field confidence score. On a 1,500-value table, that turns "about 15 errors, somewhere" into a ranked list of which cells to check.

Route uncertainty to people. Low-confidence fields route to human review automatically. The failure mode becomes a review queue, not an incident.

Validate structure, not just values. Outputs are validated against the schema you define, so a 30-column row that comes back with 28 values fails at the API boundary instead of loading into your TMS or claims system. Workflows are idempotent and versioned, with automatic regression testing and drift detection on every version: same input, same output until you publish a new version, so the same packet produces the same record next quarter, provably.

On bem, Fleetio cut invoice processing time 65 percent, with 100 percent accuracy on invoice totals. Numbers like that are workflow outcomes, not model benchmarks: they come from treating accuracy as a system property, where uncertain fields surface for review instead of passing silently.

What does a 100-page document cost to process?

Pricing models quietly encode assumptions about document length. Per-page pricing scales linearly with length: on Reducto's Standard plan, Extract runs $20 per 1,000 pages, so a 100-page packet costs roughly $2.00 per extraction pass (reducto.ai/pricing). The cloud incumbents also bill per page. bem charges per function call: the first 100 function calls each month are free, then $0.09 per call at any document length, graduating down to $0.04 per call at volume. A workflow can chain more than one function call per packet, so count your calls, but each call's price is flat.

Scenario	bem, per function call	Reducto Extract, per page
1-page invoice	$0.09	about $0.02
100-page packet, one extraction pass	$0.09	about $2.00

Neither model is cheaper in the abstract. High volumes of short documents favor per-page pricing. Long, dense documents favor flat per-call pricing, and they favor something quieter: knowing what a packet costs before you know its page count.

How do I evaluate document AI on my own long documents?

Skip the demo set. Pull your ugliest real packet: the 100-page claims file with handwritten adjuster notes, the rate confirmation with a six-page table and footnotes that change the rates. Then ask every vendor the same four questions:

Can I rerun your benchmark on my documents? For tables, RD-TableBench's public dataset and grading code make that a fair ask of anyone.
What is the per-field confidence on this specific output?
What happens, mechanically, to a low-confidence field?
What does this exact packet cost at this length?

Parse accuracy on dense tables is a real differentiator, and Reducto's published work has earned its reputation there. But at 100 pages and 1,500 values, some errors are an arithmetic certainty no parser escapes. The trustworthy systems are the ones that tell you where to look. If a vendor can answer all four questions with documentation instead of a roadmap, you have a real candidate.

Sources

reducto.ai/blog/rd-tablebench (RD-TableBench dataset size, annotation, tools scored, grading method, public availability)
docs.reducto.ai: extract citations (extract_confidence, parse_confidence, citations disable chunking)
reducto.ai/pricing (Standard plan per-page rates)
Azure Document Intelligence service limits (Azure page and size limits per tier)
Liu et al., Lost in the Middle (TACL 2024) (Long-context performance degrades for information in the middle of the input)