Gemini 3.1 Pro vs Gemma 4 12B

Compare Gemini 3.1 Pro and Gemma 4 12B side-by-side.

Compare Gemini 3.1 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 3.1 Pro vs Gemma 4 12B: Overview

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

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 3.1 Pro vs Gemma 4 12B Comparison Table

PropertyGemini 3.1 ProGemma 4 12B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Jun 2026
Context Window1.0M
Parameters12B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00
Output $/1M$12.00
Vision Tasks
CaptioningDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
ClassificationDemo
Object DetectionDemo
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 66 prompts
Score key:≥75%40–74%<40%
Overall Score
75.76%
62.69%
Avg Response Time6.13s6.88s
Median input tokensincl. image tokens1.1K
Median output tokens11
Est. cost / taskon this benchmark$0.0024
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
44.4%(4/9)
10%(1/10)
Object Understanding
92.9%(13/14)
78.6%(11/14)
Spatial Understanding
73.7%(14/19)
57.9%(11/19)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology