Every team building on documents eventually asks whether a general multimodal model like GPT-4o, Gemini, or Claude is enough, or whether the work needs a dedicated extraction platform. We build one of those, LandingAI's Agentic Document Extraction, our ADE platform, so here is the honest comparison, judged on the dimensions that decide whether extraction holds up in production.
The comparison at a glance
| Dimension | General multimodal LLM | LandingAI ADE |
|---|---|---|
| Grounding | Returns the value as text, with no coordinate tying it to the page | Bounding box per line with DPT-3 Pro, per word with DPT-3 Verity; Extract cites the exact word |
| Consistency across runs | Probabilistic, so table shape and values can drift call to call | Standardized JSON every run; Verity transcribes deterministically |
| Confidence signal | None per value by default | A confidence score for every word with Verity |
| Cost at volume | Billed per token of the whole document, on every run | Billed per character returned; a basic page runs under a cent on Verity Standard |
Can general multimodal LLMs handle production document extraction?
They are strong for exploration and reasoning, and they give way once you need the same structure from many documents with a record you can defend. The specific places they break:
- No coordinate: you get the field value back as text, with no box tying it to the page, so nothing downstream can auto verify it against the source
- Drift across runs: the same page can return a different table shape or a reworded value from one call to the next, because the output is probabilistic
- Hallucination on hard tables: on merged cells, multi page tables, or totals that carry between pages, a general model can return a clean looking number that has no basis on the page
- Token cost: you pay per token of the entire document on every run, which climbs fast at volume
For a concrete head to head, see ADE versus Gemini document processing and what each architecture tells you about grounding.
What you get from ADE instead: grounding you can read
Every element ADE returns carries its location. A value comes back with the page it sits on, its character range in the Markdown, and a bounding box on the image, inside a top level structure object. The shape looks like this:
"structure": {
"page": 2,
"range": { "start": 1180, "end": 1197 },
"box": { "xmin": 412, "ymin": 604, "xmax": 588, "ymax": 631 }
}
Read that back to front: box is where the value physically sits on the page, range is where it sits in the returned Markdown, and page says which page. That trace is the thing a general model was never built to emit. On top of it:
- DPT-3 Pro grounds to the line: every text line in a block carries its own bounding box, across all block types, and it detects tables, figures, signatures, and marginalia in reading order
- DPT-3 Verity, in public preview, grounds to the word: every word carries a bounding box and a confidence score, and it transcribes digital text and tables deterministically
- Extract turns that grounding into citations, so any field you pull traces back to a specific word on a specific page, which is what an audit or a human review needs
What makes a pipeline agentic, versus IDP and OCR?
An agentic pipeline reads a page's layout and structure first and adapts to a format it has never seen, rather than following a fixed template or a single OCR pass.
- Template IDP: coordinates and rules tuned for one layout, so a shifted column or a new vendor format quietly breaks it, which is exactly where a document it was never trained on fails
- Single pass OCR: characters with no blocks and no grounding, leaving every bit of meaning to you
- Agentic, in ADE: reads layout down to words and table cells, adapts without templates, and grounds every value, which is why rule based and single pass approaches lose accuracy under load on the hard pages
How to choose
Reach for a general model when you are prototyping, when volume is low, or when the task is reasoning over a document rather than pulling the same fields from many. Reach for ADE when you need structured extraction at volume, a value you can trace back to the page, and a cost you can predict; most serious pipelines use both, a platform to produce grounded data and a model to reason over it once it is trustworthy. Run one of your own documents through ADE at ade.landing.ai and read the boxes and confidence scores it returns.
Top comments (0)