Introduction
LandingAI Agentic Document Extraction (ADE) turns documents into structured, machine-readable data. ADE v2 is powered by the DPT-3 model family and provides two composable REST APIs:
- Parse: converts documents into structured Markdown and a hierarchical structure of typed blocks, with exact page and coordinate references.
- Extract: pulls specific fields using JSON schemas, grounding each value to a character range in the source.
Splitting and classification are ADE Gen 1 operations, both in preview: the Split API separates multi-document files, and the Classify API labels documents by type.
Gemini handles documents through its multimodal API: you upload via the Files API or send inline with a prompt, and its vision models interpret the document based on your instruction.
The practical difference: ADE returns a durable structured artifact you can extract from many times without re-parsing, while Gemini returns a text response that costs a new billed call on each query.
Core capabilities comparison
| Feature | LandingAI ADE | Gemini Document Processing |
|---|---|---|
| Table extraction | Returns tables as HTML by default, preserving cell boundaries, merged cells, and row-column relationships; every table and cell is a typed block | Interprets tables through vision; quality depends on visual clarity and prompt |
| Form field detection | Form content is returned as grounded text and table blocks; named fields are pulled with the Extract API using a JSON schema, each grounded to its source range | Interprets forms through vision prompting |
| Element categorization | Returns blocks typed as: text, table, table_cell, figure, marginalia, attestation (signatures/stamps), logo, card (IDs/passports), scan_code (barcodes/QR codes) | No element type categorization; interprets document holistically |
| Visual grounding | Page number, Markdown range, and a normalized bounding box for every block ({page: 3, xmin: 0.10, ymin: 0.34, xmax: 0.60, ymax: 0.54}) | No coordinate-level grounding in standard outputs |
| Multi-document handling | Available on ADE Gen 1: the Split API separates multi-document files, the Classify API labels documents by type (both in preview) | Processes multiple PDFs; no automatic classification/separation |
| File limits | Sync up to 50 MiB or 100 pages; Parse Jobs up to 6,000 pages or 1 GiB per PDF (50 MiB for images), async | 50MB or 1000 pages per PDF; multiple files in single request |
Pricing and workflow costs
LandingAI ADE:
Credit-based pricing: Usage is measured in credits, where $1 buys 100 credits on the Explore and Team plans. Parse consumes 1 credit per page plus 0.5 credits per 1,000 output characters, and Extract consumes 1 credit per 2,500 input characters plus 1 per 500 output characters. Asynchronous jobs on the standard service tier use half the credits of priority, with slower turnaround. For current plans and details, see the ADE pricing page and credit consumption.
Persistent parsing advantage: You can parse a document once and then run multiple extractions or schemas against that parsed output without reprocessing from scratch, making costs more predictable for repetitive extraction tasks.
Predictable costs: Because ADE credits scale with pages and extracted text rather than a running token count, high-volume repetitive extraction workflows are easier to budget than purely token-based models.
Gemini:
Token-based pricing: You pay for input tokens (prompt + document) and output tokens (response) based on the model you select.
Rates, as published by Google at the time of writing:
- Gemini 2.5 Flash: about $0.30 per 1M input tokens and $2.50 per 1M output tokens.
- Gemini 3 Flash Preview: about $0.50 per 1M input tokens and $3.00 per 1M output tokens.
- Gemini 3 Pro Preview: about $2.00 per 1M input tokens and $12.00 per 1M output tokens for prompts up to 200K tokens, rising to $4.00 and $18.00 for larger inputs.
File handling: You upload files once via the Files API, but each extraction or Q&A still incurs token costs based on the model's rates.
Context caching: Available to reduce expenses when repeatedly querying the same context.
When to choose each platform
Choose LandingAI ADE when:
- Documents have complex tables requiring structure preservation.
- You need coordinate-level grounding for compliance and audit trails.
- Field extraction accuracy impacts business outcomes (financial, medical, legal).
- Processing batched multi-document files needing classification.
- Repetitive extraction at scale (parse-once-extract-many cost model).
- Building RAG applications needing pre-structured blocks.
Choose Gemini when:
- Primary use case is conversational document Q&A.
- You need multimodal AI beyond documents.
- Document types and extraction requirements vary unpredictably.
- Prototyping with rapid iteration using natural language prompts.
- Documents are relatively simple (standard layouts, basic tables).
- Already using Google Cloud and want consolidated AI services.
ADE use cases
Medical records processing: For healthcare systems processing thousands of patient intake forms daily, ADE's field extraction accuracy directly impacts the capture of key details such as medication allergies, insurance information, and emergency contacts. See document processing for healthcare.
Financial data extraction: Investment firms extracting data from earnings reports, 10-K filings, and financial statements need ADE's table structure preservation, because nested tables with multi-level hierarchies must map correctly to database schemas. See document intelligence in financial services.
Connecting the dots
Teams can benefit from using both ADE for extraction and Gemini for generation. For example, an insurance system uses ADE to extract claim fields (amount, policy number, dates, medical codes), each grounded to its source location. That structured data feeds into Gemini to generate summaries, flag fraud patterns, and answer adjuster questions. This separation often outperforms asking one model to do both.
FAQ
What is the key difference between LandingAI ADE and Gemini for document processing?
ADE parses documents into structured Markdown and a grounded block structure with exact page and coordinate references. Gemini interprets documents and generates text answers, without a structured block tree or precise element coordinates.
How do their pricing models differ?
ADE uses credit-based pricing tied to pages and extracted text, so you parse once and extract many times without reprocessing. Gemini charges per token on each call, including repeated queries, with costs varying by model.
When should I choose LandingAI ADE over Gemini?
Choose ADE for complex documents needing accurate table and field extraction, coordinate-level grounding, batched file classification, or large-scale repetitive extraction.
Can I use ADE and Gemini together?
Yes. Use ADE to extract structured, grounded data, then feed it into Gemini for summarization, question answering, or other generation.
Does ADE support the same file types as Gemini?
ADE v2 Parse accepts PDFs and images (JPEG, PNG, plus TIFF, BMP, GIF, and WEBP through the API). Office documents and spreadsheets (Word, PowerPoint, Excel, CSV) run on ADE Gen 1, which converts them to PDF before parsing. Gemini's Files API accepts PDFs, images, video, audio, and text with a 50 MB or 1,000-page limit per PDF. See ADE supported file types.
Does ADE return confidence scores for extracted fields?
No. Every extracted value instead carries grounding: the character range in the source Markdown and the bounding box on the page. A reviewer opens the exact source location and verifies the value directly, which is what audit and human-in-the-loop review workflows require. For more, see confidence scores vs visual grounding. Gemini does not return field-level confidence scores.