Roboflow

Gemini 3 Flash vs Gemma 3 27B

Compare Gemini 3 Flash and Gemma 3 27B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

Compare Gemini 3 Flash vs Gemma 3 27B live

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

Extract and compare text from images across multiple models.

Open OCR in the full playground
GoogleGemini 3 Flash
Run to compare this model.
GoogleGemma 3 27B
Run to compare this model.

Models in this comparison

Gemini 3 Flash vs Gemma 3 27B: 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 3 27B

Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.

The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.

Gemini 3 Flash vs Gemma 3 27B Comparison Table

PropertyGemini 3 FlashGemma 3 27B
OrganizationGoogleGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Mar 2025
Context Window1.0M128K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500$0.080
Output $/1M$3.00$0.450
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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%
Visual Understanding
Overall Score
74.63%
58.21%
Avg Response Time9.85s33.60s
Median input tokensincl. image tokens1.1K
Median output tokens290
Est. cost / taskon this benchmark$0.0014
Defect Detection
73.3%(11/15)
60%(9/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
30%(3/10)
10%(1/10)
Object Understanding
85.7%(12/14)
71.4%(10/14)
Spatial Understanding
84.2%(16/19)
63.2%(12/19)
OCR
Overall Score
93.01%
Avg Response Time12.40s
Median input tokensincl. image tokens1.1K
Median output tokens160
Est. cost / taskon this benchmark$0.0010
Focused Scene OCR
94.9%(94/99)
Handwritten Math
100%(10/10)
License Plate Recognition
100%(30/30)
Text Recognition
86.7%(26/30)
VQA & Extraction
88.3%(53/60)

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