Gemini 3 Flash vs Gemma 4 12B

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

Compare Gemini 3 Flash 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 Flash vs Gemma 4 12B: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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 Flash vs Gemma 4 12B Comparison Table

PropertyGemini 3 FlashGemma 4 12B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Jun 2026
Context Window1.0M
Parameters12B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.500
Output $/1M$3.00
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 · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
74.63%
62.69%
Avg Response Time9.85s6.88s
Median input tokensincl. image tokens1.1K
Median output tokens290
Est. cost / taskon this benchmark$0.0014
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
30%(3/10)
10%(1/10)
Object Understanding
85.7%(12/14)
78.6%(11/14)
Spatial Understanding
84.2%(16/19)
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