Gemma 4 12B vs Qwen3.5 397B A17B

Compare Gemma 4 12B and Qwen3.5 397B A17B side-by-side.

Compare Gemma 4 12B vs Qwen3.5 397B A17B live

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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 Qwen3.5 397B A17B: 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.

Qwen3.5 397B A17B

Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.

As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.

Gemma 4 12B vs Qwen3.5 397B A17B Comparison Table

PropertyGemma 4 12BQwen3.5 397B A17B
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateJun 2026Feb 2026
Context Window262K
Parameters12B397B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.385
Output $/1M$2.45
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%
58.21%
Avg Response Time6.88s56.61s
Median input tokensincl. image tokens1.1K
Median output tokens54
Est. cost / taskon this benchmark$0.0006
Defect Detection
73.3%(11/15)
66.7%(10/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
78.6%(11/14)
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
57.9%(11/19)

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