Gemma 4 26B A4B vs Qwen3 VL 235B A22B Instruct

Compare Gemma 4 26B A4B and Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct
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Gemma 4 26B A4B vs Qwen3 VL 235B A22B Instruct: 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 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 26B A4B vs Qwen3 VL 235B A22B Instruct Comparison Table

PropertyGemma 4 26B A4BQwen3 VL 235B A22B Instruct
OrganizationGoogleQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateApr 2026Sep 2025
Context Window256K256K
Parameters25.2B235B
LicenseApache 2.0Apache 2.0
Pricing per 1M tokens
Input $/1M$0.060$0.200
Output $/1M$0.330$0.880
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%
Overall Score
68.66%
Avg Response Time30.23s
Median input tokensincl. image tokens294
Median output tokens214
Est. cost / taskon this benchmark$0.0001
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
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