An honest side-by-side look at ContractParser and DigiParser — where each one is stronger, and which to pick based on what you actually need.
DigiParser pricing and features verified May 25, 2026. Vendor terms change — check digiparser.com/pricing for current rates.
DigiParser and ContractParser are often grouped together as “AI document parsers,” but they run on fundamentally different stacks. That difference shapes what each is good at, how much it costs to run, and how each one handles the cases where extraction is hard.
DigiParser is a horizontal document parser built on OCR plus specialized document-AI models, with a library of pre-built templates for common document types (invoices, receipts, purchase orders, delivery notes, credit card statements). Subscription pricing with volume tiers, a 7-day free trial, broad integrations, and per-field numeric confidence scores available as a paid add-on.
ContractParser is purpose-built for contracts and runs Anthropic's current-frontier reasoning models on every page — Claude Sonnet 4.6 on the Quick tier and Claude Opus 4.7 on the default Verified tier. Pay-per-page, no subscription. Verified runs a second Opus audit pass with narrative reasoning, included in the tier price.
If you're processing invoices, receipts, or similar structured forms at scale, DigiParser's stack is well-matched to the work and priced accordingly. If your job is contract review — where understanding clauses, catching contradictions, and reasoning about ambiguity matter more than reading fields off a familiar layout — ContractParser's frontier-LLM approach is the better fit, and the price difference reflects what it actually costs to run those models.
The pricing gap below is large, and it reflects a real architectural difference. The two tools are not running the same kind of compute, and what each can do well falls out of that.
DigiParser describes its own stack as “pre-trained OCR models” with smart field detection on top, plus a library of pre-built templates for common document types. They do not name any frontier large-language-model vendor — no GPT, no Claude, no Gemini — and the “AI” in their messaging refers to specialized document-AI models, not general-purpose reasoning models.
If you haven't run into the term before: OCR (Optical Character Recognition) is the technology that converts images of text — scanned pages, photos of documents, image-based PDFs — into machine-readable characters and detects roughly where each field sits on the page. Modern OCR engines do this well, fast and cheap. But OCR itself doesn't understand what any of the text means. It's a pre-processing step that produces a stream of characters and bounding boxes, which something else then has to read.
That “something else” is where the architectural choice happens. The cheap, narrow option is to put rules, templates, or a small layout-aware model on top of the OCR output and let it pick fields out of the recognized text. The expensive, broad option is to skip the separate OCR step entirely and hand the page itself to a vision-capable LLM, which reads the page the way a person would — looking at layout, fonts, tables, and surrounding text all at once, in the same pass as the reasoning step.
ContractParser takes the second route. PDFs and images go directly to Anthropic's API as-is — there is no OCR engine in our pipeline. Every page you submit is read by Anthropic's Claude Sonnet 4.6 (Quick tier) or Claude Opus 4.7 (Verified default) via its vision pathway. Opus 4.7 is one of the most capable reasoning models available as of 2026; it reads the document end-to-end, understands clauses in context, and can answer questions like “what's the indemnification cap?” or “is the renewal automatic?” even when the document never uses those exact phrases. The Verified audit pass is a second Opus run that re-reads the extraction, looks for contradictions and arithmetic errors, and returns plain-English reasoning about what it flagged. (For text-native formats — DOCX, RTF, HTML, TXT — the text is extracted with standard libraries and sent to Claude as text. Still no OCR; those formats are already characters, not pixels.)
The per-page price reflects what that costs. ContractParser's $0.15/page Verified rate is close to what Anthropic charges us to run Opus 4.7 on a typical contract page plus the second audit pass — we do not have margin to discount under that without changing what we're running. DigiParser's $0.053/page Scale-tier rate is below the inference cost of running a frontier LLM on a single contract page; they cannot afford to do so and they don't. They run something cheaper and faster, optimized for structured forms.
The practical effect: DigiParser is excellent at what its stack was built for — invoices, receipts, purchase orders, delivery notes — where fields are predictable and OCR plus a layout-aware extraction model handles the work cleanly. It hits a ceiling on contracts, where understanding intent, cross-referencing clauses, and reasoning about ambiguous values are the work. That's the territory we built ContractParser for, and the territory vision-capable frontier LLMs are uniquely good at.
There's a secondary effect worth flagging. OCR-then-extract pipelines can compound errors: if the OCR engine misreads “$1,200” as “$l,200” (a lowercase L instead of the digit 1, a common OCR mistake), the extraction model only sees the bad string — the original page is already gone by that point and there's nothing to reconcile against. A vision-LLM pipeline keeps the page in front of the model the whole time, so the reasoning step has the visual evidence to fall back on when the text alone is ambiguous. Not a difference you'll notice on a clean contract, but it shows up on scanned, photographed, or low-quality documents — exactly the cases where errors are most expensive.
DigiParser: monthly subscription with volume tiers. 7-day free trial, no credit card. Starter tier begins at $20/month for 100 pages, scaling to $46/month for 500 pages. Pro tier is $66/month for 1,000 pages or $113/month for 2,000 pages. Scale tier runs $266/month for 5,000 pages up to $466/month for 10,000 pages. Yearly billing includes four months free. One credit = one page. Confidence scores on extracted fields are a paid add-on that consumes an extra page credit per page when enabled.
ContractParser: pay per page. $0.15/page Verified (default, audit pass included — no add-on pricing), $0.10/page Quick for bulk runs. No subscription. No monthly commitment. No expiring credits. $2.00 minimum per batch. Full breakdown on the pricing page.
On sticker price, DigiParser is cheaper per page — well below ContractParser at every tier above the entry plan. $46/month for 500 pages works out to $0.092/page; $66/month for 1,000 pages is $0.066/page; Scale 5,000 pages at $266/month is about $0.053/page. It's important to be honest about why, because the gap is too large to attribute to operational efficiency.
As covered above, frontier-LLM inference on a multi-page contract costs more in raw API charges than DigiParser's Scale tier charges total. They cannot pay for frontier-LLM inference on every page at those prices, and their own technical messaging confirms they're not trying to — their stack is OCR plus specialized document-AI models, which is materially cheaper to run. ContractParser's $0.15/page Verified rate, by contrast, is close to what Anthropic charges to run Opus 4.7 on a typical contract plus the second audit pass. We have very little discount room without changing what's running underneath.
So the per-page comparison is really a comparison of compute classes. If your work is structured forms (invoices, receipts, POs), the cheaper stack is well-matched to it. If your work is contracts and you need a model that understands what it's reading, you're paying for that capability at both vendors — DigiParser charges for a thinner version of it as the confidence-score add-on (which doubles their per-page rate, and still only returns a number); ContractParser includes the equivalent capability in the base Verified tier as narrative reasoning from Opus 4.7.
Even setting compute aside, the commitment shape differs. DigiParser is a subscription with monthly credits — cheap if you'll use them every month, expensive if your work is uneven. ContractParser charges only for the pages you ran. A one-time 300-contract review runs about $30-$45 (Quick to Verified) with no ongoing cost. Below 100 pages/month, ContractParser Quick is cheaper outright than the Starter subscription.
DigiParser uses a “document inbox” concept — create a parser for each document type, upload or email documents to it, and extract with schema detection. Supports bulk upload and can auto-detect document structure for unfamiliar formats. Works well for teams standardizing a pipeline around recurring types.
ContractParser is batch-first. Drag-drop a folder or a ZIP (up to 1,000 documents per batch), pick fields from a checklist of common contract terms or write a custom prompt in plain English, download a CSV. No inbox to create, no parser to configure, no account required for first use. The prompt goes straight to Opus 4.7, which is why “extract the total contract value” works whether the document calls it Total Contract Value, Sum Payable, or implies it via unit price × quantity × term.
The two stacks handle scanned and image-based documents differently. DigiParser uses OCR as a discrete first step — recognize characters from the page image, then run templates and a layout-aware extraction model over the recognized text. ContractParser has no OCR engine in the pipeline at all; PDFs and images go directly to Claude's vision pathway, which reads the page itself in the same pass as the reasoning. See What each one runs above for why that matters on low-quality scans.
Both address the uncertainty problem — what happens when the system isn't sure about an extracted value — but handle it very differently, and the difference falls directly out of the stack each one runs.
DigiParser offers per-field numeric confidence scores as an opt-in feature billed per page. The score is a number (typically 0 to 1) reflecting the underlying extraction model's per-field certainty — essentially “how clean was the OCR + field-detection on this particular value.” It tells you which fields might need human review; it does not reason across fields, and it does not explain why a field is uncertain.
ContractParser's Verified tier ($0.15/page, the default, no add-on pricing) runs a second Claude Opus 4.7 pass after the initial extraction. The pass re-reads the document and the first extraction together, audits every field, catches cross-field contradictions (a total that doesn't match unit price × quantity × term, dates that fall outside the contract period, renewal terms that conflict with termination terms), and returns narrative reasoning explaining what it flagged.
A Verified flag looks like this:
totalValue calculation appears confused — mixes per-site, per-year, and portfolio figures inconsistently; $4,752/site/year × 4 sites × 10 years = $190,080, not $220,777.52.
That output isn't possible from a confidence score because a confidence score isn't a reasoning step — it's a number coming out of the same extraction model that produced the value. Two things distinguish the audit pass from confidence scoring, and both follow from running a reasoning model rather than an extraction model:
Neither approach is universally better. Confidence scores are cheaper and faster when you just need a flag on uncertain fields for human review — and on structured forms like invoices, that's often enough. Verification reasoning earns its cost when the stakes of getting a field wrong justify understanding why, which is the usual case in contracts.
DigiParser wins here. Zapier, Google Sheets, QuickBooks, Xero, Salesforce, Excel, webhooks, and a public API. The breadth reflects a horizontal platform strategy.
ContractParser currently imports directly from Salesforce (pick contracts attached to your Salesforce records) and exports CSV. Google Drive, OneDrive, Box, and Dropbox imports are planned. No public API at launch — on the roadmap.
If you need parsing integrated into a broader automation pipeline, DigiParser has more connectivity today.
DigiParser competes effectively in the structured-document parsing market — invoices, receipts, POs, delivery notes. Their template library is legitimately useful if those are the documents you work with, and their OCR-based stack is well-matched to that work at the price they charge.
We built ContractParser for a different problem: contract extraction by frontier LLMs with a built-in audit pass, designed for batch work rather than ongoing streams. Different tool, different buyer, different compute underneath. If the documents in front of you are contracts and the questions you're asking are open-ended, you want a reasoning model reading them — and you should know which one.
Still weighing options? Our roundup of the 10 best contract parsers and document extraction tools places every major tool — DigiParser included — at the buyer and use case where it genuinely wins. Or browse other comparisons side-by-side.