Best AI document extraction API

Combined ranking

1. 1
   LlamaParse—11 pts

   GPT #2Claude #4Gemini #1

   Natively uses vision-language models (VLMs) to reconstruct complex layouts, multi-column pages, and nested tables directly into clean Markdown or structured JSON, preserving semantic context far better than traditional OCR.

   GPT Agentic mode delivers the strongest demonstrated semantic parsing of tables, charts, formatting and visual grounding, while its Extract API produces typed JSON from developer-defined schemas; particularly strong for financial reports and RAG ingestion.

   Claude The easiest on-ramp for RAG builders — cheap, generous free tier, first-class LlamaIndex integration, and good-enough markdown/table output for most ingestion pipelines; near-tie with Docling below, ranked ahead only because it's a managed API with zero ops.

   Where it falls short

   per GPT The highest-quality mode is slower and costlier than Mistral, and the core service is proprietary and hosted.

   per Claude Parse fidelity trails Reducto and even careful Gemini prompting on genuinely complex layouts, so it's not for high-stakes extraction where a swapped table cell matters.

   per Gemini It is a closed-source, cloud-only managed API, making it unusable for teams with strict data privacy policies or air-gapped requirements.

2. 2
   Reducto—9 pts

   GPT #3Claude #1Gemini #5

   Consistently the accuracy leader on the documents that actually break parsers — dense financial tables, multi-column layouts, scanned forms — with schema-driven structured extraction, per-field citations/confidence, and a strong public-benchmark culture (RD-TableBench); it's what teams graduate to when Textract-class tools misread their tables. Assumption: the practitioner is shipping production extraction and can pay for a commercial API.

   GPT The most complete high-accuracy workflow for difficult enterprise documents: strong layout and table parsing, schema extraction, document splitting, iterative Deep Extract, source citations and numerical confidence, with reusable parse results that avoid repeated OCR.

   Gemini A modern, developer-first API designed for visual-aware PDF layout parsing and schema-based structured data extraction, using an agentic OCR layer to review and correct outputs.

   Where it falls short

   per GPT Agentic and deep-extraction workflows can become materially more expensive than the leaders, especially at volume.

   per Claude Premium per-page pricing is hard to justify for simple, clean digital PDFs — overkill if a cheap LLM pass already hits your accuracy bar.

   per Gemini It is a closed-source SaaS API with higher cost per page and lacks broad ecosystem integrations or support for non-PDF file formats.

3. 3
   Azure AI Document Intelligenceincumbent—5 pts

   GPT —Claude #3Gemini #4

   The enterprise workhorse — mature prebuilt models (invoices, receipts, IDs, contracts), trainable custom extractors, layout/OCR that handles handwriting and scans well, plus the compliance, regional deployment, and SLA story regulated buyers require.

   Gemini The strongest cloud provider API, offering highly optimized pre-built models for standard documents (invoices, receipts) and a robust layout parser that directly outputs LLM-ready Markdown with enterprise-grade SLAs.

   Where it falls short

   per Claude Clunky for arbitrary ad-hoc schemas — custom model training and its API surface feel dated next to prompt-a-schema LLM-native rivals, and per-page costs climb fast on the specialized models.

   per Gemini It features a steep learning curve, high integration overhead, and custom schema training is expensive and requires labeled datasets.

4. 4
   Doclingincumbent—5 pts

   GPT —Claude #5Gemini #2

   A powerful open-source library that runs locally and utilizes advanced layout analysis models to convert PDFs, DOCX, and PPTX files into high-quality Markdown/JSON without per-page costs or data privacy concerns. It is in a near-tie with LlamaParse, offering comparable layout fidelity but prioritizing local-first privacy over managed VLM cloud processing.

   Claude The best open-source option — MIT-licensed, strong layout and table-structure models, clean markdown/JSON output, and free local execution that solves the privacy/data-residency problem no hosted API can; near-tie with LlamaParse, trading managed convenience for control.

   Where it falls short

   per Claude You own the ops — GPU provisioning, scaling, and throughput tuning — and processing is slower than hosted APIs, so it's wrong for teams that just want an endpoint today.

   per Gemini Requires managing your own compute and GPU infrastructure for high-throughput scaling, and local table extraction is resource-intensive.

5. 5
   MMistral Document AI—5 pts

   GPT #1Claude —Gemini —

   Best overall value for mixed-document workloads: OCR 4 combines excellent complex-layout, table, formula and multilingual parsing with bounding boxes, confidence scores and schema-defined JSON; API pricing is exceptionally low, and self-hosting is available. Near-tied with LlamaParse, but ranked first on cost, speed and breadth.

   Where it falls short

   per GPT OCR 4 is very new, so risk-averse teams lack the long production history and extensive independent validation of older platforms.

6. 6
   GGemini 2.5 Flash—4 pts

   GPT —Claude #2Gemini —

   The best value in the category by a wide margin — native PDF/vision understanding plus enforced structured output turns "PDF to JSON" into one API call at pennies per thousand pages, and by 2026 this is genuinely the default first attempt for typical practitioners; it ranks this high on merit-per-dollar, not brand.

   Where it falls short

   per Claude It's a general LLM, not a document pipeline — no deterministic confidence scores or reliable bounding boxes, and it can silently hallucinate cell values in long or degraded tables, so it's not for compliance-grade extraction without a verification layer.

7. 7
   UUnstructured API—3 pts

   GPT —Claude —Gemini #3

   The industry standard for high-volume enterprise ingestion, supporting over 20 document formats (emails, presentations, PDFs) and partitioning them into metadata-rich elements with VPC and serverless hosting options.

   Where it falls short

   per Gemini Relies primarily on layout heuristics and rule-based partitioning, causing it to struggle with highly complex visual elements or non-standard structures.

8. 8
   Google Cloud Document AI—2 pts

   GPT #4Claude —Gemini —

   Mature, scalable and operationally dependable, with inexpensive OCR, strong layout parsing, generative or trained custom extractors, useful pretrained processors and excellent Google Cloud integration; a near-tie with Reducto for standardized high-volume workflows.

   Where it falls short

   per GPT Custom processors add configuration, hosting and labeling overhead, and long-tail charts or irregular layouts are less reliably handled than by the leading agentic parsers.

9. 9
   LLandingAI Agentic Document Extraction—1 pts

   GPT #5Claude —Gemini —

   A cohesive API suite for parsing, schema construction, extraction, classification, splitting and sectioning, with auditable source grounding and good developer tooling; especially useful when schema-first business-document automation matters more than raw Markdown fidelity.

   Where it falls short

   per GPT Quality and cost vary substantially with model tier and document density, making it less predictable than the higher-ranked choices.

Just missed the top 5

By model

Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled continuously