# Reducto Leads Independent Benchmark on Structured Extraction with Deep Extract

Helping everyone from startups to Fortune 10 enterprises unlock their data.

- Harvey
- Scale AI
- Newfront
- Medallion
- Vanta
- Legora
- Rogo
- Levelpath
- JLL
- Vise
- Laurel
- Toast
- Mercor
- Zip
- Anterior
- Supio

- Harvey
- Scale AI
- Newfront
- Medallion
- Vanta
- Legora
- Rogo
- Levelpath
- JLL
- Vise
- Laurel
- Toast
- Mercor
- Zip
- Anterior
- Supio

\+ many more

| Dimension | Reducto | Gemini |
| --- | --- | --- |
| Category | Document pipeline orchestrating 12+ models, including frontier VLMs. | General-purpose frontier LLM; document work means custom pipeline engineering. |
| Reading order & complex layouts | Yes: Layout-aware pipeline preserves reading order on multi-column pages. | Partial: Documented reading-order errors on multi-column and mixed layouts. |
| Tables & figures | Yes: 0.90 on RD-TableBench; charts convert to structured data. | Partial: Tables degrade under token pressure; figures described, not extracted. |
| Handwriting & multilingual | Yes: Built-in handwriting recognition; 100+ languages with structured output. | Yes: Genuine strength: handwriting and broad multilingual understanding. |
| Citations & verification | Yes: Bounding-box citation on every extracted field. | No: No sub-page spatial citations on output. |
| Determinism & confidence | Yes: Schema-bound output with confidence signals and self-correction. | No: Non-deterministic run to run; no built-in confidence scores. |
| Cost at volume | From $0.015/page; selective frontier-model routing; 15,000 free credits. | Token-based; scales with document length, hard to forecast. |
| Deployment & compliance | Yes: SOC 2 Type II, HIPAA, zero data retention; hosted to air-gapped. | Partial: Google Cloud compliance framework; tied to Google's cloud stack. |
| Document toolkit | Yes: Parse, Extract, Split, Classify, Edit; MCP server, CLI, SDKs. | No: Raw inference only; document tooling is custom engineering. |

Run one of your hardest documents through Studio and compare the output to a raw model call.

### Choose Reducto if…

- You're building a production pipeline where reading order errors on multi-column or complex layouts would corrupt downstream extraction or LLM output.
- High-stakes extraction requires deterministic, schema-bound output with per-field bounding-box citations for audit or human review.
- You're processing documents at volume and per-document frontier-model token costs would be prohibitive or unpredictable.
- You need the full document toolkit (parse, extract, split, classify, edit) with an MCP server, CLI, and SDKs, not raw inference plus custom glue.
- You're in a regulated industry where HIPAA, zero data retention, or VPC/on-prem/air-gapped deployment are non-negotiable.

### Gemini may be a starting point if…

- You're prototyping and the goal is understanding document content, not building a production extraction pipeline.
- Your documents are simple and single-column, and qualitative understanding is sufficient; table fidelity and reading order aren't concerns.
- Handwriting recognition or broad multilingual understanding is the primary requirement and structured, verifiable output is not critical.
- You need general reasoning over document content (summarization, Q&A, drafting) where an LLM's language strength is the whole job.