Gemma 4 12B vs Qwen2.5 VL 7B Instruct

Compare Gemma 4 12B and Qwen2.5 VL 7B Instruct side-by-side.

Compare Gemma 4 12B vs Qwen2.5 VL 7B Instruct 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 Qwen2.5 VL 7B Instruct: 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.

Qwen2.5 VL 7B Instruct

Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.

Gemma 4 12B vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyGemma 4 12BQwen2.5 VL 7B Instruct
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Jan 2025
Context Window33K
Parameters12B7B
LicenseApache 2.0Apache 2.0
Vision Tasks
CaptioningDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Object Detection
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
62.69%
52.24%
Avg Response Time6.88s47.64s
Defect Detection
73.3%(11/15)
60%(9/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
0%(0/10)
Object Understanding
78.6%(11/14)
57.1%(8/14)
Spatial Understanding
57.9%(11/19)
57.9%(11/19)