Gemma 4 12B vs Gemma 4 31B

Compare Gemma 4 12B and Gemma 4 31B side-by-side.

Compare Gemma 4 12B vs Gemma 4 31B 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

Gemma 4 12B vs Gemma 4 31B: Overview

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.

Gemma 4 31B

Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.

For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.

Gemma 4 12B vs Gemma 4 31B Comparison Table

PropertyGemma 4 12BGemma 4 31B
OrganizationGoogleGoogle
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Apr 2026
Context Window256K
Parameters12B31B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.120
Output $/1M$0.350
Vision Tasks
CaptioningDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
classificationDemo
Object DetectionDemo
Model Features
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
62.69%
67.16%
Avg Response Time6.88s34.59s
Median input tokensincl. image tokens294
Median output tokens169
Est. cost / taskon this benchmark$0.0001
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
10%(1/10)
10%(1/10)
Object Understanding
78.6%(11/14)
71.4%(10/14)
Spatial Understanding
57.9%(11/19)
73.7%(14/19)

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