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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 Comparison Table

Evals updated October 13, 2026Pricing updated October 24, 2026

PropertyGemma 4 12BGemma 4 31B
OrganizationGoogleGoogle
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Apr 2026
Context Window—256K
Parameters12B31B
LicenseApache 2.0Apache 2.0
Commercial usefree for commercial usefree for commercial use
Pricing per 1M tokens
Input $/1MNo published price$0.200
Output $/1MNo published price$0.400
Vision Tasks
CaptioningSupportedDemo
OCRSupportedDemo
Vision LanguageSupportedSupported
Visual Question AnsweringSupportedDemo
Chart Question AnsweringNot listedSupported
ClassificationNot listedDemo
Document Question AnsweringNot listedSupported
Image TaggingNot listedSupported
Multi-Label ClassificationNot listedSupported
Object DetectionNot listedDemo
Model Features
Multimodal VisionSupportedSupported
Foundation VisionNot listedSupported
LLMs with Vision CapabilitiesNot listedSupported
Vision Evalsground-truth scores across 5 vision tasks
OverallNot evaluated
57.7%
4/5 tasks
Quantizationsself-hosted
BF1657.7%FP857.7%QAT-W4A1657.6%hardware →
Avg cost / sample–$0.0015
Avg speed / sample–34.36s
By task
Object Detection–
47.5%
±0.6, Mean of 3 runs, range 46.8 to 48.0
$0
Counting–
51.4%
±2.7, Mean of 3 runs, range 48.6 to 54.0
$0
Identification–
79.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0
OCR––
by category
Reasoning–
52.8%
±1.3, Mean of 3 runs, range 51.7 to 54.3
$0

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.