Reducto is the complete agentic document platform for AI teams building on top of frontier models. If your team is weighing "spin up a document pipeline directly on Gemini" against "buy Reducto," the honest framing is that this is not an either/or choice. Reducto orchestrates 12+ models — including Gemini — and adds the layout parsing, structured extraction, citation regions, and cost controls that turn a powerful general-purpose model into production-grade document infrastructure.

This page is for engineering leaders who already trust Gemini for general AI work and are trying to figure out where the line is between "what Gemini gives you for free" and "what you still have to build."

What Gemini is genuinely strong at

Gemini 3.1 Pro and the broader Gemini family bring real strengths to document work. They require no template training, which makes them easy to point at a new document type and start experimenting the same afternoon. Growing language coverage is a meaningful advantage — for buyers with multilingual document corpora, Gemini handles a wider set of languages out of the box than most specialized document systems.

On straightforward digital text, Gemini performs well. The headroom on pure text understanding is small across modern frontier models, and Gemini holds its own there. Brand pull matters too: technical buyers are already evaluating Gemini for other workloads, so adding document tasks to an existing Gemini integration feels like a small step rather than a new vendor decision.

And there are specific document cases where Gemini is the right tool. Handwriting is one of them. Reducto deliberately calls frontier models for handwriting-heavy pages because that is where a general vision-language model often does the best work. Narrow intelligence on a hard, specific page region — interpreting a handwritten annotation, reasoning about an unusual chart label — is genuinely where frontier models shine.

If your team's goal is a quick model-first prototype, or you only need narrow intelligence on a hard page region, Gemini alone is a reasonable starting point.

Where the production gap shows up

The gap appears when "prototype that works on five PDFs" needs to become "system that runs on five million documents reliably, predictably, and at a defensible cost." Several specific limitations of frontier vision-language models surface at that boundary.

Reading order on dense pages. Frontier VLMs struggle with complex reading order when a single page contains many blocks — multi-column layouts, sidebars, footnotes, and embedded tables. The output looks plausible but doesn't faithfully reconstruct the document's logical structure.

Granular chart data. Models can describe trends in a chart, but extracting specific datapoints reliably — the value at point X, the gap between series A and B — is much harder. For document workflows that depend on those numbers, this is the difference between a usable answer and a wrong one.

Tables under token pressure. When output approaches the token budget, models tend to compress rows or drop detail rather than return the full table faithfully. This is a silent failure mode — the output looks complete, but rows are missing.

Coordinate-level citations. Out of the box, frontier models do not return sub-page coordinates or precise cited regions. For regulated workflows, citations users can click back to the source page are usually a hard requirement, not a nice-to-have.

Determinism. When near token limits, models make soft judgment calls. The same document run twice can produce different outputs. This breaks audit trails and complicates evaluation.

Cost when used naively. This is the one that surprises teams in production. Calling a frontier model directly on every document, at the resolution and token budget needed for hard pages, can cost tens of dollars per document. Reducto charges cents per page for the same workload because the orchestration layer routes simple pages to cheaper specialized models and reserves frontier-model calls for the cases that actually need them.

Latency under heavy budgets. Pushing image resolution and token budgets up to handle harder pages makes runs slower as well as more expensive.

How Reducto fits with Gemini

Reducto is not a replacement for Gemini. It is the production layer that sits between your application and the right model for each page.

The platform orchestrates 12+ models under the hood, including frontier models like Gemini. When a page is straightforward digital text, Reducto routes to a fast, cheap path. When a page contains handwriting or an unusual figure, Reducto can selectively call a frontier model. When the output needs to fit a strict schema, Reducto enforces it. When the workflow requires citations back to specific coordinates on the source page, Reducto provides them.

On top of model orchestration, Reducto adds:

Layout parsing built for complex reading order, multi-column pages, and dense structured content.
Schema-driven extraction that adapts to new document types without retraining or re-labeling.
Sub-page citation regions with bounding boxes — every extracted field traces back to a coordinate on the source.
Cost control via routing, configurable accuracy/latency/throughput trade-offs, and per-page pricing that's predictable in advance.
Multi-pass agentic VLM workflows for hard pages, with self-correction rather than single-shot guessing.
30+ filetypes beyond PDF, including spreadsheets, slides, and scanned formats.

The result is that you continue to benefit from Gemini's improvements — Reducto stays current with model releases so you don't have to chase the frontier — without inheriting the full cost and control tradeoffs that come with using a frontier model as your only tool.

When to reach for Gemini alone

There are real scenarios where Gemini alone is the better answer. Quick prototypes where the question is "is this feasible at all." Internal tools where occasional failure is acceptable. Narrow tasks on a small number of documents where the cost ceiling is low. Experimental research where the team wants direct model access without a layer in between.

If you're in one of those scenarios, the right move is probably to keep going with Gemini directly.

When to reach for Reducto

The pattern is different in production. AI workflows running at enterprise scale, where outputs need to be deterministic and citations are non-negotiable. Per-page cost that has to be predictable for budgeting and unit economics. Regulated environments that demand SOC 2, HIPAA, and zero data retention. Document corpora spanning 30+ filetypes, not just clean PDFs. Teams shipping AI features instead of maintaining ingestion infrastructure.

Reducto is trusted by Harvey, Scale AI, and Vanta for exactly this kind of work — production AI on messy real-world documents at scale.

On benchmarks

Every vendor publishes benchmarks that show their product winning, and Reducto is no exception. The honest stance is that vendor benchmarks carry bias, and the only evaluation that matters is the one run on your own documents. Reducto's free tier exists precisely so teams can do that head-to-head comparison against Gemini, against any other tool, on the documents they actually care about.

Reducto uses frontier models selectively, then adds layout parsing, structured extraction, citation regions, and cost control on top.