Roboflow

Gemma 4 26B A4B vs Llama 3.2 Vision 11b+ 1 other

Compare Gemma 4 26B A4B, Llama 3.2 Vision 11b, and 1 other vision model side-by-side. Test these models on Image Captioning, OCR, and Open Prompt in the Playground.

Compare these vision models live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Compare image classification labels and confidence scores side-by-side.

Open Classification in the full playground
GoogleGemma 4 26B A4B
Run to compare this model.
MetaLlama 3.2 Vision 11b
Run to compare this model.
MistralPixtral 12B

Pixtral 12B doesn't have a Classification demo set up here yet.

Models in this comparison

Model Overviews

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.

Gemma 4 26B A4B vs Llama 3.2 Vision 11b Comparison Table + 1 other

PropertyGemma 4 26B A4BLlama 3.2 Vision 11bPixtral 12B
OrganizationGoogleMetaMistral
Categoryopenopenopen
Modalitymultimodalmultimodalmultimodal
Release DateApr 2026Sep 2024Sep 2024
Context Window256K128K128K
Parameters25.2B11B12B
LicenseApache 2.0ProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.060$0.345
Output $/1M$0.330$0.345
Vision Tasks
CaptioningDemoDemoDemo
OCRDemoDemoDemo
Vision Language
Visual Question AnsweringDemoDemoDemo
ClassificationDemoDemo
Object DetectionDemo
Model Features
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
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)
OCR
Overall Score
83.84%
Avg Response Time12.05s
Median input tokensincl. image tokens290
Median output tokens42
Est. cost / taskon this benchmark<$0.0001
Focused Scene OCR
85.9%(85/99)
Handwritten Math
50%(5/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
80%(24/30)
VQA & Extraction
83.3%(50/60)

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