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Reducto vs Gemini
Reducto combines purpose-built document models with extraction and verification, adding the reading order, citations, and predictable cost at volume that raw model calls don't provide. Gemini is an excellent frontier LLM.
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Apply for migration creditsIf you're using another document processor, we'll give you up to $5,000 in credits to migrate your workload to Reducto.
At a glance
How Reducto and Gemini compare
Reducto is built for production document pipelines and uses frontier models as one layer of the stack. Gemini is built for general language and vision understanding.
| Dimension | Reducto | Gemini |
|---|---|---|
| Category | Document platform: Parse r-1, Extract, Split, Classify, and Edit. | 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: Merged cells and multi-level headers; 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: Optional per-field source text, bounding boxes, and confidence. | No: Coordinates can be prompted; citation and review workflows require implementation. |
| 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 $0.01/page pay-as-you-go; $150 in free credits. | Token-based; scales with document length, hard to forecast. |
| Deployment & compliance | Yes: SOC 2 Type II; HIPAA/BAA and 24-hour API data expiry on Growth+; VPC 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.
The comparison in depth
Where the differences actually show up
- Extraction accuracy, measured
- An independent benchmark commissioned by Reducto and conducted by micro1 evaluated extraction systems on 225 real, human-validated documents. Reducto Deep Extract ranked #1 on all four dimensions (100% coverage, 99.6% precision, 99.6% recall, 99.3% leaf accuracy) and completed every document with zero failures. In the same benchmark, Gemini 3.1 Pro completed 112/225 documents, with 95.8% precision and 48.6% recall on completed documents. Without layout-aware preprocessing and verification, accuracy varies with document complexity and prompt design. Benchmarks are a starting point, not a verdict: the numbers that matter are the ones on your own documents, which is why we encourage head-to-head evals.
- Reading order for downstream pipelines
- Frontier VLMs, Gemini included, have documented trouble with reading order on multi-column pages and mixed-content layouts: text, sidebars, footnotes, and tables interleave in ways a general-purpose model can misorder. Parse r-1 reads text, tables, figures, and layout together in one pass, with source grounding. In preview, r-1 is faster than Reducto's previous agentic pipeline, with 20% lower error. Legacy parsing and custom agentic processing remain available. If your downstream LLM or extraction job depends on text arriving in the right sequence, this is the failure mode that silently corrupts everything after it.
- Dense tables, figures, and hallucination control
- Under token pressure, general LLMs tend to paraphrase tables rather than faithfully reconstruct them: a cell shifts a column, a row gets summarized, and the output still looks plausible. Parse r-1 handles merged cells, multi-level headers, and figure summaries natively; optional advanced chart extraction produces structured data with labeled points rather than qualitative descriptions. On documents where a transposed number is a real cost, faithful reconstruction beats fluent approximation.
- Citations, confidence, and determinism
- A JSON schema shapes raw LLM output but does not verify each value against the source. Gemini can return prompted spatial coordinates; a field-level review and validation workflow still requires implementation. With citations enabled, Reducto links extracted values to their bounding-box positions in the source document, carries confidence signals, and is schema-bound, with Deep Extract adding iterative self-correction. That's what makes human review, audit trails, and automated exception handling buildable instead of aspirational.
- Cost predictability at volume
- Sending full documents to a frontier model prices every page at frontier-token rates, and costs scale with document length in ways that are hard to forecast. Parse r-1 is $0.01/page, Extract is $0.02/page, and Deep Extract is $0.04/page, with parsing included in both extraction prices. You can budget by page count instead of estimating variable credit usage, with no separate parsing bill for extraction. Reducto includes $150 in free credits and has processed 5B+ pages. See processing rates.
- Enterprise deployment and compliance
- Reducto is SOC 2 Type II, with HIPAA/BAA on Growth and Enterprise and documented data-retention policies, and deploys hosted, in your VPC, on-prem, or fully air-gapped. Gemini runs within Google's cloud stack. Teams at Harvey, Scale AI, and Vanta run Reducto in production.
- Using frontier models with Reducto
- This isn't Reducto versus frontier models — Reducto is built on them. The platform combines purpose-built parsing with schema-driven extraction, citations, and Deep Extract verification. Most teams pair the two layers: Reducto handles parsing, extraction, and citations, and the clean structured output feeds Gemini-powered agents, RAG systems, and reasoning steps. You keep the frontier model's intelligence and gain the document infrastructure raw calls don't provide.
Which fits your team
Who should pick which
Different tools fit different jobs. Here's the honest split.
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 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, documented data-retention controls, 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.
FAQ
Common questions
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Read the comparisonFurther reading
Research before you choose
- BenchmarkLVM OCR Accuracy: Mistral vs Gemini
Explore how vision-language models compare on OCR accuracy for challenging documents.
- BenchmarkComplex Document Extraction Benchmark
See how Deep Extract performed on micro1’s independent benchmark of complex document extraction.
- GuideIDP Enterprise Evaluation
Use a practical framework to evaluate document processing platforms for enterprise workloads.
Document work starts here
See the difference
Sign up with $150 in free credits, run your hardest documents through Reducto and a raw model call, and compare the output side by side.
