Gemma 4 12B vs GPT-5 Mini

Compare Gemma 4 12B and GPT-5 Mini side-by-side.

Compare Gemma 4 12B vs GPT-5 Mini 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 GPT-5 Mini: 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.

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

Gemma 4 12B vs GPT-5 Mini Comparison Table

PropertyGemma 4 12BGPT-5 Mini
OrganizationGoogleOpenAI
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2026Aug 2025
Context Window400K
Parameters12B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.250
Output $/1M$2.00
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
62.69%
73.13%
Avg Response Time6.88s11.72s
Median input tokensincl. image tokens1.4K
Median output tokens143
Est. cost / taskon this benchmark$0.0006
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
10%(1/10)
Object Understanding
78.6%(11/14)
85.7%(12/14)
Spatial Understanding
57.9%(11/19)
89.5%(17/19)

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