Gemma 4 12B vs Qwen3.6 Plus

Compare Gemma 4 12B and Qwen3.6 Plus side-by-side.

Compare Gemma 4 12B vs Qwen3.6 Plus 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 Qwen3.6 Plus: 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.

Qwen3.6 Plus

Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.

Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.

Gemma 4 12B vs Qwen3.6 Plus Comparison Table

PropertyGemma 4 12BQwen3.6 Plus
OrganizationGoogleQwen
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateJun 2026Apr 2026
Context Window1.0M
Parameters12B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.325
Output $/1M$1.95
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%
68.66%
Avg Response Time6.88s34.17s
Median input tokensincl. image tokens1.2K
Median output tokens47
Est. cost / taskon this benchmark$0.0005
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
78.6%(11/14)
78.6%(11/14)
Spatial Understanding
57.9%(11/19)
68.4%(13/19)

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