Gemma 4 12B vs Qwen3 VL 30B A3B Instruct

Compare Gemma 4 12B and Qwen3 VL 30B A3B Instruct side-by-side.

Compare Gemma 4 12B vs Qwen3 VL 30B A3B Instruct live

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Models in this comparison

Gemma 4 12B vs Qwen3 VL 30B A3B 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 30B A3B Instruct

Qwen3 VL 30B A3B Instruct is an open-weight multimodal large language model developed by Alibaba as part of the Qwen family, built for instruction-following tasks that unify text generation with visual and video understanding. Released around October 2025 under the Apache-2.0 license, it targets efficient, high-fidelity vision-language reasoning across very long contexts.

The model accepts text and image inputs and produces text outputs, with strong performance in OCR, spatial reasoning, long-video understanding, and agentic or GUI-centric visual tasks. It uses a Mixture-of-Experts (A3B) design with ~31.1B total parameters and ~3B active per token, paired with Qwen3-VL’s unified multimodal stack (including Interleaved-MRoPE and DeepStack fusion) to process text, images, and video in a single architecture. OCR support expands to 32 languages, enhancing document workflows. With a native ~262K token context window (extendable further), it stands out today for its balance of scale, efficiency, long-context support, and open accessibility in multimodal systems.

Gemma 4 12B vs Qwen3 VL 30B A3B Instruct Comparison Table

PropertyGemma 4 12BQwen3 VL 30B A3B Instruct
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Oct 2025
Context Window262K
Parameters12B31B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.130
Output $/1M$0.520
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)