What is the best document processing API with confidence scoring?

As of October 2026 it depends on the level you need. For field-level confidence on extracted values, Azure Document Intelligence (0–1 per field; prebuilt models $10 and custom extraction $30 per 1,000 pages), Google Document AI Custom Extractor (0–1 per entity; $30 per 1,000 pages, $20 above 1 million) and Reducto Extract (a confidence label plus numeric extract and parse confidence per cited field; $20 per 1,000 pages) are the strongest. AWS Textract returns a 0–100 score on every word, key-value pair and table cell. Mistral OCR 4.1 ($4 per 1,000 pages) and LandingAI's DPT-3 Verity give block- or word-level transcription confidence, which tells you how well the page was read but not whether a field was mapped correctly.

What is the difference between OCR confidence and extraction confidence?

OCR (parse) confidence measures whether characters and words were read correctly from the image. Extraction confidence measures whether the right text was assigned to the right field in your schema, for example whether 1,240.00 is really the invoice total. A clean scan can have perfect OCR confidence and still map the subtotal into the total field. For straight-through processing you need both: Reducto returns them separately (parse_confidence and extract_confidence), while Azure and Google give a single field-level score.

How do I use confidence scores for human review?

Set a per-field threshold, auto-accept values above it and route values below it to a review queue with the page and bounding box shown. Calibrate the threshold on a labelled sample of your own documents rather than trusting a default: measure the error rate of accepted fields at several thresholds and pick the one that meets your accuracy target. Use stricter thresholds for fields that move money or decisions (totals, account numbers, dates) and looser ones for descriptive fields.

Do LLM-based extractors return reliable confidence scores?

Not by default. A general-purpose LLM asked to fill a JSON schema returns values with no calibrated probability, and self-reported confidence in the prompt is not reliable. Document APIs that pair extraction with grounding (a citation to the page region the value came from) plus a parse-level score, such as Reducto, LandingAI ADE and Azure's Content Understanding analyzers, are the practical way to get a review signal from model-based extraction.

Quick Answer

Best Document Processing API With Confidence Scoring 2026

Published: October 8, 2026

The short answer

If you need a score on every extracted field, use Azure Document Intelligence, Google Document AI’s Custom Extractor or Reducto Extract. If you only need to know how well the page was read, AWS Textract, Mistral OCR 4.1 and LandingAI DPT-3 Verity are cheaper or more granular. The distinction matters: a transcription score tells you the word “1,240.00” was read correctly; a field score tells you it really is the invoice total. Straight-through processing needs the second. Prices are list USD per 1,000 pages, read October 8, 2026.

The comparison

API	Confidence level	Scale	Price per 1,000 pages (list)	Best for
Azure Document Intelligence	Field, word, document type	0–1	Read $1.50 · prebuilt/layout $10 · custom extraction $30	Invoices, receipts, IDs; Microsoft shops
Google Document AI Custom Extractor	Entity (field)	0–1	$30 (first 1M/month), $20 above · Enterprise OCR $1.50	Custom schemas with few training docs
Reducto Extract	Field: label + numeric extract and parse confidence, with citation	high/low + 0–1	Extract $20 · Deep Extract $40 · Parse $10 · 20% off batch	Messy real-world PDFs, audit trails
AWS Textract	Word, line, key-value pair, table cell, query answer	0–100	Detect Text $1.50 · Tables $15 · Queries $15 · Forms $50 (first 1M)	AWS pipelines; forms and tables
Mistral OCR 4.1	Page, block, word	0–1	$4 · batch $2 · Document AI annotations $5	High-volume parsing to markdown
LandingAI ADE (DPT-3 Verity)	Word (lowest per-character score)	0–1	Credit-based	Digital documents needing word-level grounding

API notes

Azure Document Intelligence. Microsoft’s documentation defines field confidence as an estimated probability between 0 and 1 that the prediction is correct — 0.95 means right about 19 times in 20 — and returns separate document-type, field and word confidence. Custom models can be retrained on low-confidence samples to raise the score. Its newer Content Understanding analyzers add field-level source grounding plus confidence via the estimateFieldSourceAndConfidence setting, aimed at auto-approve-or-review workflows.

Google Document AI. The Custom Extractor returns a 0–1 confidence per entity, and Google’s own guidance is to trigger manual review when it is low. Its generative mode reaches production quality with 0–50+ training documents on variable layouts, and a template mode needs about 3 documents for fixed forms.

Reducto. With citations enabled, every extracted field returns the page, bounding box, source text and a confidence: a high/low label, and with numerical confidence on, a granular_confidence object holding extract_confidence and parse_confidence as 0–1 numbers. That split is the most useful design on this list, because it tells a reviewer whether the page was misread or the field was mis-mapped. List prices took effect September 1, 2026; dense extractions over 100 fields per page may incur a surcharge.

AWS Textract. Every block it returns carries a confidence between 0 and 100, and AWS’s best-practices page recommends a minimum threshold with low-scoring results discarded or sent for human review. Confidence is on reads, key-value pairs, cells and query answers rather than on your own schema, so mapping to business fields is your code’s job.

Mistral OCR 4.1. The cheapest structured option: $4 per 1,000 pages ($2 batch) with paragraph-level bounding boxes, block labels and block-level confidence (average and minimum content confidence plus block-type confidence), or per-word scores via confidence_scores_granularity. Use it as the parse layer and score extracted fields downstream.

LandingAI ADE. DPT-3 Verity returns a 0–1 score for every word, computed as the lowest per-character score in the word, alongside word-level grounding. DPT-3 Pro, the model for handwriting and complex scans, does not return confidence scores, and the Extract API returns field source ranges rather than its own field confidence.

How to choose

Prebuilt document types (invoices, receipts, IDs): Azure prebuilt at $10 per 1,000 pages — field confidence out of the box.
Your own schema, variable layouts: Google Custom Extractor or Reducto Extract.
Regulated review with an audit trail: Reducto, for separate parse and extract confidence plus citations.
Millions of pages, cost first: Mistral OCR 4.1 or Textract for parsing, then your own field validation (totals that sum, dates that parse, IDs that pass checksums).

Whatever you pick, validation rules beat confidence alone: a total that equals the sum of its line items is stronger evidence than any model score. The full vendor ranking is in best OCR and document extraction APIs; for hallucinated fields in LLM extraction see how to stop hallucinated fields.

Last verified: October 8, 2026. Prices are list USD; volume tiers and contracts differ.

The short answer

The comparison

API notes

How to choose

Sources