Reducto vs Gemini | Agentic Document Platform vs Raw LLM

Reducto Leads Independent Benchmark on Structured Extraction with Deep Extract

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

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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.

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.

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.