Open Source Gemini 3 Pro: The Frontier Of Vision AI

Gemini 3 Pro delivers state-of-the-art performance across document, spatial, screen and video understanding.

Gemini 3 Pro is Google's most capable multimodal model that delivers state-of-the-art performance across document, spatial, screen and video understanding. You can use it for complex visual reasoning, document processing, and understanding spatial relationships. Check out the developer documentation or play with the model in Google AI Studio to get started.

Gemini 3 Pro represents a generational leap from simple recognition to true visual and spatial reasoning. It is our most capable multimodal model ever, delivering state-of-the-art performance across document, spatial, screen and video understanding.

This model sets new highs on vision benchmarks such as MMMU Pro and Video MMMU for complex visual reasoning, as well as use-case-specific benchmarks across document, spatial, screen and long video understanding.

Real-world documents are messy, unstructured, and difficult to parse — often filled with interleaved images, illegible handwritten text, nested tables, complex mathematical notation and non-linear layouts. Gemini 3 Pro represents a major leap forward in this domain, excelling across the entire document processing pipeline — from highly accurate Optical Character Recognition (OCR) to complex visual reasoning.

To truly understand a document, a model must accurately detect and recognize text, tables, math formulas, figures and charts regardless of noise or format.

A fundamental capability is "derendering" — the ability to reverse-engineer a visual document back into structured code (HTML, LaTeX, Markdown) that would recreate it. As illustrated below, Gemini 3 demonstrates accurate perception across diverse modalities including converting an 18th-century merchant log into a complex table, or transforming a raw image with mathematical annotation into precise LaTeX code.

Example 1: Handwritten Complex Table from 18th century Albany Merchant’s Handbook

Example 3: Reconstructing Florence Nightingale's original Polar Area Diagram into an interactive chart (with a toggle!)

Users can rely on Gemini 3 to perform complex, multi-step reasoning across tables and charts — even in long reports. In fact, the model notably outperforms the human baseline on the CharXiv Reasoning benchmark (80.5%).

Source: HackerNews