CapyParse vs Nanonets: Document OCR Comparison (2026)

CapyParse and Nanonets both use AI to pull data out of documents. But they're built for completely different people. CapyParse is a no-code tool for accountants, bookkeepers, and small teams who need bank statements and shipping documents converted into usable formats. Nanonets is an API-first platform for developers and enterprises who want to automate document processing across dozens of document types. Different tools, different audiences.

At a Glance

Ease of Use

This is where the two tools are most different.

CapyParse: Built for Non-Technical Users

CapyParse was designed for people who aren't developers. Open the web interface, upload a PDF bank statement, and within seconds you get a clean CSV, Excel, or QBO file. No API to configure, no JSON to parse, no integration code to write. Drag and drop.

If you've got 15 bank statements from different clients that need to be in QuickBooks before end of day, you don't want to spend an hour reading API docs. You want to upload, check the output, and download. That's exactly what CapyParse does.

The tradeoff is customization. You can't define custom extraction fields, build multi-step processing pipelines, or trigger extraction programmatically from another app. For most accountants and bookkeepers, that's fine.

Nanonets: Built for Developers and IT Teams

Nanonets is fundamentally an API platform. It has a web dashboard for managing models and reviewing data, but the core use case assumes you're integrating it into a larger system. Send documents to the API, get structured JSON back, build your own logic around the results.

For a dev team building an invoice processing pipeline or an automated compliance system, that's exactly right. The API is well-documented, supports webhooks, and gives you fine-grained control over extraction fields and validation. Nanonets also connects with Zapier, which opens up no-code automation for teams that want the middleware without writing raw API calls.

The tradeoff is setup time. Getting Nanonets running in production usually requires a developer, API integration work, and ongoing maintenance. For a non-technical accountant, the learning curve is steep.

If you're not a developer, CapyParse is the easier tool. If you are, Nanonets gives you more control.

Document Type Support

Nanonets has a significant advantage here, and it's worth being upfront about it.

What Nanonets Supports

Nanonets offers pre-trained models for dozens of document types: invoices, receipts, purchase orders, tax forms, identity documents, insurance claims, bank statements, bills of lading, and more. If you've got a document type that isn't covered, you can train a custom model by uploading annotated samples. That flexibility makes Nanonets a strong choice if you process many different document types and want a single platform for all of them.

What CapyParse Supports

CapyParse focuses on bank statements as its core document type, with growing support for shipping documents including bills of lading, packing lists, and freight rate confirmations. The bank statement extraction is deeply specialized. It understands transaction tables, opening and closing balances, date formats, and the quirks of statements from hundreds of different banks. For shipping documents, it extracts key fields like shipper, consignee, commodity details, and weights.

If you need to process invoices, tax forms, or identity documents, CapyParse isn't the right tool today. That's by design. By specializing, CapyParse can optimize extraction quality for its supported types rather than spreading thin across dozens of categories.

Nanonets wins on breadth. CapyParse wins on depth for bank statements and shipping docs.

Bank Statement Accuracy

This is where CapyParse has the clearest edge. Not because Nanonets is bad, but because CapyParse was built specifically for this use case while Nanonets treats bank statements as one of many document types.

CapyParse: Purpose-Built for Bank Statements

Every part of CapyParse's extraction pipeline is optimized for bank statement data. The AI models understand transaction table structure, recognize date and amount formats from different banks, handle multi-page statements that split transactions across pages, and correctly distinguish between debits, credits, and running balances. The output isn't raw OCR text. It's structured financial data with proper column mapping.

This specialization shows up in edge cases that general-purpose tools miss: statements with multiple accounts, pages mixing summary info with transaction detail, credit card statements with different column layouts, and statements from small banks or credit unions with nonstandard formatting. The output goes directly into CSV, Excel, or QBO without manual cleanup.

Nanonets: General-Purpose Extraction

Nanonets does have a bank statement model, and for common formats it produces reasonable results. But because it's a general-purpose platform, the extraction isn't as fine-tuned for bank statement nuances. You may need to configure custom fields, handle edge cases in your own code, and manually map the output to accounting-ready formats like QBO.

If bank statements are a small part of a larger document workflow, Nanonets might be good enough. But if bank statement conversion is your primary need (which it is for most accountants), CapyParse's specialized approach delivers cleaner output with less post-processing.

Shipping Document Support

This one's roughly even. Both tools are expanding here, but they approach it differently.

CapyParse: Simple Upload for Common Shipping Docs

CapyParse supports bills of lading, packing lists, and freight rate confirmations. Same workflow as bank statements: upload the document, CapyParse extracts key fields (shipper name, consignee, port of loading, container numbers, weights, commodity descriptions), and you download the result.

The limitation is that you can't define custom field definitions for shipping documents yet. CapyParse extracts a standard set of fields. If your workflow requires fields unique to your document format, you may find the output incomplete.

Nanonets: Flexible Extraction with Custom Models

Nanonets offers pre-trained models for bills of lading and other shipping docs, and the big differentiator is custom model training. If you process a unique BOL format with fields the pre-trained model doesn't capture, you can annotate sample documents and fine-tune the model to extract exactly what you need. Powerful for freight forwarders and logistics companies with highly specific requirements.

The tradeoff is complexity. Setting up custom models requires uploading annotated training data, configuring extraction fields, and integrating via API. Not quick, but the payoff is a model tailored to your documents.

CapyParse is simpler and faster for standard shipping doc extraction. Nanonets is more flexible if you need custom fields and fine-tuned models.

Pricing

This is one of the biggest differentiators.

The gap is stark. $20/month vs ~$499/month. CapyParse is accessible to solo accountants, small bookkeeping firms, and freelancers. You get 250 pages, which covers most small practices' monthly workload. The 10 free pages with no credit card let you test on real documents before spending anything.

Nanonets' price reflects its enterprise positioning. The ~$499 starting point includes API access, custom model training, and features CapyParse doesn't offer. But those features only matter if your workflow actually requires them. A bookkeeper who just needs bank statement conversion shouldn't be paying 25x more for API access and custom model training.

To be fair, if you're building a production automation pipeline that processes thousands of documents per month across multiple types, the Nanonets investment may be justified. The question is whether you need those capabilities or whether a simpler tool at a fraction of the cost gets the job done.

API and Integrations

If programmatic access matters to you, this is the section that counts.

Nanonets: Mature API with Broad Integration Support

Nanonets was built API-first, and it shows. The REST API lets you upload documents, poll for extraction results, and receive structured JSON programmatically. Webhooks notify you when extraction completes. Validation rules catch errors before they hit your downstream systems. And the Zapier integration opens up hundreds of no-code automation possibilities: forward emails to Nanonets, extract invoice data, push results into Google Sheets or your ERP without writing code.

For dev teams, this is a genuine strength. Building Nanonets into an existing software stack, whether that's an accounting platform, a logistics system, or a custom internal tool, is something CapyParse simply can't do today.

CapyParse: Web-Based, API on the Roadmap

CapyParse doesn't have a public API yet. It's entirely web-based: upload through the browser, download converted files manually. For accountants and bookkeepers processing bank statements in batches, this workflow works fine. You don't need an API to upload 10 bank statements and download 10 CSV files.

An API is on the roadmap, which will eventually enable programmatic access. But if you need API access today, Nanonets is the clear choice here.

Custom Model Training

This is one of Nanonets' strongest features, and CapyParse doesn't compete here.

Nanonets: Fine-Tune Models on Your Documents

Nanonets lets you train custom extraction models by uploading samples and annotating the fields you want. This is powerful for organizations processing document types that pre-trained models don't cover well. A freight company with a proprietary BOL format, an insurance firm with nonstandard claim forms. Over time, the model learns from corrections and improves on your specific documents.

The custom training workflow takes effort: 50+ annotated samples, field configuration, output validation. But for organizations with unique formats, the accuracy improvement is real.

CapyParse: No Custom Training, Less Need for It

CapyParse doesn't offer custom model training. Its models are maintained by the CapyParse team and updated to handle new bank statement formats as they come up. The philosophy is that users shouldn't have to train their own models. The tool should just work.

For bank statements, this approach works because CapyParse's models are already optimized for the financial data patterns that show up across hundreds of bank formats. For users who need highly customized extraction on unusual document types, the lack of custom training is a real limitation.

Who Should Choose What

Pick the tool that matches your workflow. If you just need bank statements converted, CapyParse. If you're building a document pipeline, Nanonets.

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