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Gemma 4 26B A4B vs Qwen3.6 Plus

Compare Gemma 4 26B A4B and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemma 4 26B A4B
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QwenQwen3.6 Plus
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Models in this comparison

Gemma 4 26B A4B vs Qwen3.6 Plus: Overview

Gemma 4 26B A4B

Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.

For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.

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 26B A4B vs Qwen3.6 Plus Comparison Table

PropertyGemma 4 26B A4BQwen3.6 Plus
OrganizationGoogleQwen
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Apr 2026
Context Window256K1.0M
Parameters25.2B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.100$0.325
Output $/1M$0.300$1.95
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
classificationDemo
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
68.66%
68.66%
Avg Response Time30.23s34.17s
Median input tokensincl. image tokens2941.2K
Median output tokens21447
Est. cost / taskon this benchmark$0.0001$0.0005
Defect Detection
80%(12/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
85.7%(12/14)
78.6%(11/14)
Spatial Understanding
68.4%(13/19)
68.4%(13/19)
OCR
Overall Score
83.84%
58.52%
Avg Response Time12.05s5.49s
Median input tokensincl. image tokens290124
Median output tokens4218
Est. cost / taskon this benchmark<$0.0001$0.0001
Focused Scene OCR
85.9%(85/99)
76.8%(76/99)
Handwritten Math
50%(5/10)
80%(8/10)
License Plate Recognition
93.3%(28/30)
13.3%(4/30)
Text Recognition
80%(24/30)
50%(15/30)
VQA & Extraction
83.3%(50/60)
51.7%(31/60)

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

Gemma 4 26B A4B vs Qwen3.6 Plus: Vision Model Comparison