# Reducto vs LlamaParse

Updated: 2026-10-16

## Verdict

**Choose Reducto when** you want per-page USD pricing you can forecast, private deployment (hybrid VPC, full VPC, on-prem, air-gapped), and, when citations are enabled, a confidence label on every extracted field. Reducto's Parse r-1 lists at $10 per 1,000 pages and Extract at $20 per 1,000 pages on the Standard plan, with 30-plus input formats and no page limit.

**Choose LlamaParse when** you want 130-plus input formats including audio, a Fast tier at about $1.25 per 1,000 pages that returns spatial text without Markdown, and a platform that already includes Index, Classify, and Split for retrieval pipelines. Its Extract tiers cost 8, 25, or 60 credits per page in total, parsing included (35 for Turbo).

**Choose Nutrient Data Extraction when** you want one credit pool across parse, extract, classify, and split, a mode ladder that lets cheap born-digital pages stay cheap (1 credit per page in text mode, 9 in understand, 18 in agentic) with an Auto mode that picks the mode per page and bills at that mode's rate, and grounded extraction that returns a bounding box, a match label such as `fuzzy_match` or `not_found`, and a relative confidence per field. 5,000 credits renew every month with no card. Nutrient was founded as PSPDFKit in 2011. 3,000-plus organizations, including 15 percent of the Global 500, rely on it, with over a billion document interactions a year. The same extraction engine runs hosted, self-hosted in the Python and Java SDKs with no external API calls for OCR and ICR, or on a Mac in Extract Studio.

## At a glance

_Reducto, LlamaParse (LlamaCloud), Nutrient Data Extraction API; facts fetched 2026-10-15 to 2026-10-16_

| Fact | Reducto | LlamaParse (LlamaCloud) | Nutrient Data Extraction API |
| --- | --- | --- | --- |
| Product type | hosted API [1] | hosted API [2] | Hosted Data Extraction API, web Studio, and macOS Extract Studio [3] |
| Output formats | Markdown, JSON, HTML tables [4] | Markdown, Plain text, JSON (per-page items), XLSX, HTML tables, Annotated PDF, Structured JSON to custom schema [2] | Spatial JSON, Markdown, Schema-shaped JSON with citations, Ranked labels with independent 0-1 scores, Split page ranges and ZIP files [5] |
| OCR for scans | Yes [6] | Yes [7] | Yes: structure, understand, and agentic; text has no OCR; 100+ languages [8] |
| Handwriting | Yes [9] | Yes [10] | Yes: printed-style in understand; cursive/freeform needs agentic [11] |
| Tables | Yes [9] | Yes [2] | Yes: spans and bounds; complex tables may need agentic mode [11] |
| Layout and bounding boxes | Yes [9] | Yes [12] | Yes: element and optional word bounds; word bounds exclude XLSX [11] |
| Per-field confidence | Yes [13] | Yes [14] | Yes: 0-1 relative signal, not a probability [15] |
| Source citations and grounding | Yes [13] | Yes [14] | Yes: field boxes, match labels, confidence, and source boxes [15] |
| Schema extraction | Yes [16] | Yes [17] | Yes: closed JSON Schema subset; 128 KB, 2,000 nodes, depth 5 [18] |
| Classification | Yes [19] | Yes [20] | Yes: zero-shot, 2+ labels; independent 0-1 scores; 1 credit/page [21] |
| Document splitting | Yes [22] | Yes [23] | Yes: boundary detection for PDF/images; 3 credits/page [24] |
| Free tier | $150 in free usage (Standard plan) [25] | Includes 10K credits (Free plan, $0/month) [2] | 5,000 credits/month; no card; all parse modes, including Auto [26] |
| Pricing model | USD per 1,000 pages, per endpoint; Growth/Enterprise custom with volume discounts [27] | Credits per page by tier; 1,000 credits = $1.25 (i.e. $0.00125/credit). Parse v2 tiers: Fast 1, Cost-effective 3, Agentic 10, Agentic Plus 45 credits/page. Add-ons: layout extraction +3, enriched forms +10 per form page, spreadsheet 1/sheet, audio 3/min. [28] | Credits/page by endpoint and mode; Auto routes each page and bills that mode's rate; subscription plus capped overage [29] |
| List price examples | Standard (list), Parse r-1: $10 / 1,000 pages (r-1 parse; OCR data return / Parse citations +$2/1k; prompted blocks +$5/1k)<br>Standard (list), Extract: $20 / 1,000 pages (parsing included; dense extractions (>100 fields/page) may incur per-field surcharge)<br>Standard (list), Deep Extract: $40 / 1,000 pages (agentic extraction; not eligible for batch discount)<br>Standard (list), Split / Deep Split: $20 / $40 per 1,000 pages (parsing included)<br>Standard (list), Classify / Deep Classify: $7.50 / $15 per 1,000 pages (first 5 pages by default; Deep Classify is alpha pricing)<br>Standard (list), Edit: $60 / 1,000 pages ($15 for fully prefilled pages) (standalone, cannot be combined with other products)<br>Batch queue: -20% on Parse and Extract (async, 12-hour completion guarantee)<br>Growth / Enterprise: Custom (contact sales) (volume discounts, ZDR, BAA, EU/AU residency (Growth); VPC/on-prem, SSO/SAML, SLA (Enterprise)) [25] | Free: $0/month (10K credits; 5 concurrent parse jobs; community support)<br>Starter: $50/month (40K credits; pay-as-you-go up to $500/mo (400K credits); basic email support)<br>Pro: $500/month (400K credits; pay-as-you-go up to $5,000/mo; 20 concurrent jobs; priority Slack Connect support; limited-time one-time bonus of 800K credits ($1,000) for business email domains)<br>Enterprise: Custom (Volume discount, 5x higher rate limits, SSO, SaaS or hybrid/VPC deployment, dedicated account manager, 100 concurrent jobs, custom BAAs) [2] | Per-page credits: parse 1-18; extract 7-24 (classify 1; split 3)<br>Auto mode: bills each page at the mode it selects (recommended starting mode; routes clean pages to structure and scans or complex forms to understand)<br>Free: $0/month (5,000 credits; PAYG after a $25 wallet load)<br>Starter: $59/month; $49/month annual (25,000 credits; $0.0028 per extra credit)<br>Pro: $500/month; $450/month annual (500,000 credits; $0.0012 per extra credit) [26] |
| Deployment | SaaS, VPC, on-prem, air-gapped [1] | SaaS (US and EU regions), VPC / hybrid cloud, single-tenant SaaS, BYOC (Azure, AWS, GCP), self-hosting [30] | Hosted API: Nutrient-managed cloud, Self-hosted: Data Extraction SDK for Python and Java, no network required; OCR and ICR run on your infrastructure with zero external API calls, Local VLM: Ollama, LM Studio, or vLLM on your own hardware, or your own OpenAI or Anthropic key, Desktop: Extract Studio for Apple silicon Macs (macOS Tahoe 26.4+); the engine runs on the Mac and, with a local model, documents never leave it; Nutrient counts pages for credits, .NET data extraction SDK also offered [31] |
| SDKs | Python, Node.js, Go, REST API, CLI [32] | Python (llama-cloud, PyPI 2.18.0 at fetch), TypeScript (@llamaindex/llama-cloud), Go, Java, CLI, REST API, n8n node [33] | REST API, Python SDK, Java SDK, .NET page referenced, curl, JavaScript, and raw HTTP samples [34] |
| MCP or agent tools | MCP server (local: uvx mcp-server-reducto; hosted: https://mcp.reducto.ai/mcp), CLI, llms.txt and agent guide [35] | Hosted LlamaParse Platform MCP at mcp.llamaindex.ai/mcp (Parse, Classify, Extract, Split, Index); docs-search MCP; agent skills/plugins for Claude Code and Codex [36] | Hosted MCP, Data Extraction Studio, and agent skills [37] |
| Maximum file size or pages | 100 MB direct upload; up to 5 GB via presigned URL; no page limits on Standard plan [38] | {"Batches": "10,000 files per batch (Pro/Enterprise)", "Classify": "512 MB", "Extract": "100 MB, 500 pages per job", "Parse": "512 MB, no page cap", "Split": "512 MB (PDF/DOCX/PPTX)"} [39] | Direct upload: 150 MB, URL input: 50 MB, URL timeout: 30 seconds, Processing: 5 minutes; HTTP 408 if exceeded, Page limit: not published, Test-key limits: lower, unpublished; Support-enabled [40] |
| Compliance | SOC 2 Type II (all tiers), HIPAA with signed BAA (Growth and Enterprise), Zero Data Retention (Growth and Enterprise) [41] | SOC 2 Type II, GDPR, HIPAA (BAA for Enterprise) [30] | SOC 2: Type 2, audited annually, BAA: no public statement, Transport: HTTPS/TLS only, No-retention plans: files discarded after processing, Retention plans: runs stored; may train/evaluate models, Payments: Paddle [42] |
| Public benchmarks | Parse r-1 vs Reducto's previous agentic pipeline 20% reduction in error rate (error rate (preview)), vendor (Reducto), 2026-09-01 ([source](https://reducto.ai/blog/parse-r-1-model))<br>Extraction benchmark commissioned by Reducto, run by micro1 (225 documents) Reducto Deep Extract: 100% coverage, 99.6% precision, 99.6% recall, 99.3% leaf accuracy; Extend MAX: 93.3% coverage, 86.4% precision, 92.7% recall, 92.8% leaf accuracy (coverage / precision / recall / leaf accuracy), vendor-commissioned (Reducto; conducted by micro1), 2026-09-17 ([source](https://reducto.ai/compare/reducto-vs-extend))<br>RD-TableBench (1,000 complex table images) Not published (hierarchical Needleman-Wunsch table similarity), vendor (Reducto), 2024-11-04 ([source](https://reducto.ai/blog/rd-tablebench))<br>RealDoc-Bench, Reducto rows (run by Extend) layout adjusted F1 0.759; Document Q&A 91.1% (Reducto Agentic) (adjusted F1 / Q&A accuracy), competitor (Extend), 2026-05-26 ([source](https://www.extend.ai/resources/realdocbench))<br>Unstructured parsing benchmark, Reducto Agentic row (run by Unstructured) (page shows '2026 (no day shown)'; date here is the fetch date) adjusted CCT 0.812; table cell content accuracy 0.708; element alignment 0.595 (see source), competitor (Unstructured), 2026-10-15 ([source](https://unstructured.io/benchmarks)) [43] | ParseBench public leaderboard (15 Oct 2026 snapshot) LlamaParse Agentic Plus 90.2 (#1), LlamaParse Agentic 87.0 (#2), LlamaParse Cost Effective 80.6 (#5) of 149 entries (overall 0 to 100), parsebench.ai (LlamaIndex), 2026-10-15 ([source](https://parsebench.ai/))<br>ParseBench (LlamaIndex, ~2,000 pages, 167K rules) LlamaParse Agentic 84.9% overall at ~1.2 cents/page; Cost Effective <0.4 cents/page; formatting 85.2% (Docling 1.0%) (overall of 5 dimensions), LlamaIndex (vendor-authored; self-benchmark), 2026-04-13 ([source](https://www.llamaindex.ai/blog/parsebench))<br>OmniParseBench LlamaParse headline 90.4 (text 88.2, tables 92.6, layout 90.4) (mean of text/tables/layout family pass rates), Datalab (competitor-authored), 2026-10-08 ([source](https://www.datalab.to/blog/omni-parse-bench))<br>OmniExtractBench llamaextract agentic_plus accuracy 84.93, precision 86.57, recall 93.13, coverage 616/620 (accuracy), Datalab (competitor-authored), 2026-09-16 ([source](https://www.datalab.to/blog/omni-extract-bench)) [48] | ParseBench public row 76.4; paired nightly: 82.8 (overall 0 to 100), LlamaIndex, 2026-10-15 ([source](https://parsebench.ai/))<br>ParseBench nightly 82.8; paired public row: 76.4 (overall 0 to 100), Nutrient, 2026-10-15 ([source](https://www.nutrient.io/api/data-extraction-api/benchmarks/))<br>opendataloader-bench, Nutrient's own run understand 0.933; agentic 0.930; structure 0.892; text 0.889 (overall 0 to 1), Nutrient, 2026-10-16 ([source](https://www.nutrient.io/api/data-extraction-api/benchmarks/))<br>opendataloader.org leaderboard Nutrient row (free CLI) 0.885; opendataloader hybrid 0.907 (overall 0 to 1), opendataloader.org, 2026-09-23 ([source](https://opendataloader.org/docs/benchmark)) [52] |

## Pricing side by side

Reducto moved from credits to list prices per 1,000 pages effective September 1, 2026: Parse r-1 $10, Extract $20, with OCR data return and Parse citations at $2 per 1,000 pages extra. New Standard accounts get $150 in free usage; the same pricing page also says 15,000 free credits, so confirm which applies.

LlamaParse bills credits at $1.25 per 1,000. Parse v2 is Fast 1, Cost-effective 3, Agentic 10, and Agentic Plus 45 credits per page, which works out to $0.00125, $0.00375, $0.0125, and $0.05625 per page (our arithmetic from the published rates). The Free plan includes 10,000 credits; Starter is $50 a month for 40,000 credits.

Nutrient bills credits per page by mode: parse text 1, structure 1.5, understand 9, agentic 18; extract adds a fixed 6 (7, 7.5, 15, 24 in total); classify 1; split 3. Auto, the recommended starting mode, routes each page to one of those modes and bills at that mode's rate. The free plan renews 5,000 credits a month, enough for about 555 understand parse pages or 333 understand extract pages.

## Output, grounding, and confidence

All three return Markdown and JSON with table structure and block-level bounding boxes. The differences are in extraction: Reducto's citations carry a high or low confidence per field and, in Extract v3, granular confidence by default. With `confidence_scores` enabled (off by default), LlamaParse Extract returns a 0 to 1 confidence per field. With `cite_sources` on, it returns page, matching text, and bounding boxes per field. The Turbo tier returns text-level citations without boxes. Nutrient's extract returns per-field metadata with a bounding box, a match label (`id_match`, `fuzzy_match`, `not_found`), the source blocks, and a relative confidence that Nutrient's docs say is not a probability or a percentage.

Nutrient's `not_found` match label flags values it could not place; Reducto's low-confidence label and LlamaParse's per-field score are the other signals to route on.

## Deployment and compliance

Reducto offers SaaS plus hybrid VPC, full VPC on AWS, GCP, or Azure, and on-prem or air-gapped deployments on its Enterprise tier; SOC 2 Type II on all tiers, HIPAA with a BAA and zero data retention on Growth and Enterprise.

LlamaParse runs as SaaS in US and EU regions with single-tenant, BYOC, and self-hosting on Enterprise; SOC 2 Type II, GDPR, and a HIPAA BAA for Enterprise. Parse source files and cache are deleted after 48 hours.

Nutrient Data Extraction is a hosted API with SOC 2 Type 2 audited controls (reports under NDA) and HTTPS only; plans without retention discard inputs after processing. Nutrient runs the same engine three ways: the hosted API; self-hosted in the Python and Java Data Extraction SDKs, where OCR and ICR run on your infrastructure with no external API calls and the VLM stage can run locally through Ollama, LM Studio, or vLLM; and on a Mac in Extract Studio, where documents never leave the machine when the model runs locally. No BAA statement was found on the public pages, so ask before relying on HIPAA.

## Published benchmark numbers, with who ran them

Nutrient's published benchmark results, as recorded on 16 October 2026: on LlamaIndex's ParseBench the public leaderboard row for Nutrient agentic is 76.4. Nutrient's own nightly run of 15 October 2026 scores 82.8 on the same test and is not on parsebench.ai yet; in that nightly run, understand mode scores 64.0. LlamaParse Agentic Plus leads the public leaderboard at 90.2. Nutrient is not on Datalab's OmniParseBench or OmniExtractBench public leaderboards. In Nutrient's own run on the opendataloader-bench corpus the API scores 0.933 in understand mode and 0.930 in agentic; on the public opendataloader.org leaderboard Nutrient's row (the free CLI) scores 0.885, behind opendataloader hybrid at 0.907.

Datalab's OmniParseBench (October 8, 2026, run by Datalab) scores Reducto 90.8 and LlamaParse 90.4; its OmniExtractBench (September 16, 2026) scores Reducto 93.47 and LlamaExtract 84.9. LlamaIndex's own ParseBench leaderboard (15 October 2026) scores LlamaParse Agentic Plus 90.2 and LlamaParse Agentic 87.0, lists Nutrient agentic at 76.4 on its public row; Nutrient's own nightly run of the same day scores 82.8, where understand mode scores 64.0. Extend's RealDoc-Bench, run by Extend, scores Reducto Agentic Q&A at 91.1 percent. None of these is independent of a document-API vendor; treat them as directional and run your own sample.

## Where Nutrient differs

The other products on this page are parsers or extraction APIs. Nutrient's Data Extraction API is the same kind of product, built by a company that has shipped document SDKs to enterprises since 2011. The facts below come from Nutrient's own public pages.

- Founded as PSPDFKit in 2011, and the SDKs developers knew as PSPDFKit continue as Nutrient SDKs. Thousands of organizations in 80-plus countries, including Fortune 500s and governments, use them ([about page](https://www.nutrient.io/company/about/)).
- 3,000-plus organizations and 15 percent of Global 500 companies rely on Nutrient, with over a billion document interactions a year ([nutrient.io](https://www.nutrient.io/)).
- One engine, four deployments: hosted API in Nutrient's cloud; the Python and Java Data Extraction SDKs, self-hosted with no network required and no external API calls for OCR and ICR, with a local VLM through Ollama, LM Studio, or vLLM ([Python SDK](https://www.nutrient.io/sdk/python/pdf-data-extraction/)); Extract Studio on Apple silicon, where documents never leave the Mac when the model runs locally ([Extract Studio](https://www.nutrient.io/api/data-extraction-api/macos-desktop-app/)); and the wider platform in the browser with WebAssembly, on your own servers with Document Engine, or air-gapped ([nutrient.io](https://www.nutrient.io/)).
- Extraction is one credit pool with parse, classify, and split, and every extracted field carries a bounding box and a match label, so a reviewer can check the source instead of trusting the model. SOC 2 Type 2 audited.

## What to test before choosing

- Run the same 20 documents through each API: five clean born-digital PDFs, five scans, five with tables that span pages, five with the fields your downstream system needs. Score reading order, table cells, and the fields you will actually consume.
- Count the per-page cost at the mode or tier that passed your sample, not the cheapest tier on the pricing page. Every vendor here prices accuracy upward.
- Check what the API returns when it is unsure: a confidence number, a bounding box, a `not_found` label, or nothing. That decides whether you can build a review queue.
- Confirm retention and deployment in writing for the plan you will buy. Free and pay-as-you-go tiers often exclude BAAs, zero-retention, and private deployment.

## Frequently asked questions

### Is Reducto or LlamaParse better for RAG ingestion?

For born-digital documents at volume, LlamaParse Cost-effective returns Markdown at about $3.75 per 1,000 pages (our arithmetic); its Fast tier (about $1.25) returns spatial text without Markdown. For scans and forms where you need a confidence signal on every extracted value, Reducto's citations give you more to route on. Nutrient's text mode is 1 credit per page for born-digital PDFs and its structure mode adds OCR at 1.5 credits, which makes it a reasonable third option to price out.

### Which is cheaper per page, Reducto or LlamaParse?

At list, Reducto Parse r-1 is $10 per 1,000 pages. LlamaParse Fast is about $1.25 per 1,000 pages and Agentic about $12.50 per 1,000 pages, from $1.25 per 1,000 credits. Nutrient understand parse is 9 credits per page: $25.20 per 1,000 pages at the Starter pay-as-you-go rate ($0.0028 per credit) or $10.80 at the $500-a-month Pro plan's rate ($0.0012); our arithmetic. Compare at the tier that passes your sample, not the cheapest one.

### Do Reducto and LlamaParse support on-prem deployment?

Reducto lists hybrid VPC, full VPC, on-prem, and air-gapped options on its Enterprise tier. LlamaParse lists BYOC on Azure, AWS, and GCP, single-tenant SaaS, and self-hosting for Enterprise. Nutrient runs the same engine three ways: the hosted API; self-hosted in the Python and Java Data Extraction SDKs, where OCR and ICR run on your infrastructure with no external API calls and the VLM stage can run locally through Ollama, LM Studio, or vLLM; and on a Mac in Extract Studio, where documents never leave the machine when the model runs locally.

### Which API gives per-field confidence scores?

All three, differently. Reducto returns a high or low confidence per citation and granular confidence in Extract v3. LlamaParse returns a 0 to 1 confidence per field when `confidence_scores` is enabled, except in spreadsheet mode. Nutrient returns a composite relative confidence plus a match label per field and says the number is not a probability or a percentage.

### Does Nutrient win any of the published benchmarks?

No public leaderboard ranks Nutrient first. Its best results are its own runs: 0.933 on the opendataloader-bench corpus (the public opendataloader.org row for its free CLI is 0.885), 82.8 on ParseBench in its 15 October nightly run against a public row of 76.4 and LlamaParse Agentic Plus at 90.2. It is not on Datalab's OmniExtractBench leaderboard.

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