Gemma 4 12B vs Mistral Medium 3.1

Compare Gemma 4 12B and Mistral Medium 3.1 side-by-side.

Compare Gemma 4 12B vs Mistral Medium 3.1 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 Mistral Medium 3.1: 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.

Mistral Medium 3.1

Mistral Medium 3.1, released in August 2025 as the mistral-medium-2508 update, is a proprietary frontier model from Mistral AI positioned between smaller open models and high-end closed LLMs. It is multimodal, handling both text and image inputs, with a context window of ~128K tokens.

Compared to Mistral Medium 3.0, the 3.1 release introduces improvements in reasoning, coding, STEM, and enterprise workflows, along with better tone control for conversational and business applications. It is designed for scalable enterprise deployments, including hybrid cloud and on-premises VPC setups. As part of Mistral’s Premier line, Medium 3.1 is a commercial-only offering: while it delivers strong accuracy and performance, trade-offs include higher costs than open-weight models, restricted fine-tuning access, and increased latency/cost for very large contexts.

Gemma 4 12B vs Mistral Medium 3.1 Comparison Table

PropertyGemma 4 12BMistral Medium 3.1
OrganizationGoogleMistral
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2026Aug 2025
Context Window128K
Parameters12B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.400
Output $/1M$2.00
Vision Tasks
CaptioningDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
62.69%
Avg Response Time6.88s
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
57.9%(11/19)