Gemma 4 12B vs Qwen3 VL 235B A22B Instruct

Compare Gemma 4 12B and Qwen3 VL 235B A22B Instruct side-by-side.

Compare Gemma 4 12B vs Qwen3 VL 235B A22B Instruct live

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Models in this comparison

Gemma 4 12B vs Qwen3 VL 235B A22B 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.

Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.

The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.

Gemma 4 12B vs Qwen3 VL 235B A22B Instruct Comparison Table

PropertyGemma 4 12BQwen3 VL 235B A22B Instruct
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Sep 2025
Context Window256K
Parameters12B235B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.200
Output $/1M$0.880
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%
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)