Roboflow

Gemini 3 Pro vs Gemma 4 12B

Compare Gemini 3 Pro and Gemma 4 12B side-by-side.

Compare Gemini 3 Pro vs Gemma 4 12B live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Gemini 3 Pro vs Gemma 4 12B: Overview

Gemini 3 Pro

Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.

The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.

Gemma 4 12B

Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.

This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.

Gemini 3 Pro vs Gemma 4 12B Comparison Table

PropertyGemini 3 ProGemma 4 12B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2025Jun 2026
Context Window1.0M
Parameters12B
LicenseProprietaryApache 2.0
Vision Tasks
Captioning
OCR
Vision Language
Visual Question Answering
Classification
Object Detection
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
62.69%
Avg Response Time6.88s
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
57.9%(11/19)