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.
Upload an image
Drag and drop an image here, or click to browse
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
| Property | Gemma 4 26B A4B | Llama 3.2 Vision 11b | Pixtral 12B |
|---|---|---|---|
| Organization | Meta | Mistral | |
| Category | open | open | open |
| Modality | multimodal | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2024 | Sep 2024 |
| Context Window | 256K | 128K | 128K |
| Parameters | 25.2B | 11B | 12B |
| License | Apache 2.0 | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | |||
| Input $/1M | $0.060 | $0.345 | |
| Output $/1M | $0.330 | $0.345 | |
| Vision Tasks | |||
| Captioning | Demo | Demo | Demo |
| OCR | Demo | Demo | Demo |
| Vision Language | |||
| Visual Question Answering | Demo | Demo | Demo |
| Classification | Demo | Demo | |
| Object Detection | Demo | ||
| Model Features | |||
| Multimodal Vision | |||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | |||
| Visual Understanding | |||
| Overall Score | 68.66% | ||
| Avg Response Time | 30.23s | ||
| Median input tokensincl. image tokens | 294 | ||
| Median output tokens | 214 | ||
| 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 Time | 12.05s | ||
| Median input tokensincl. image tokens | 290 | ||
| Median output tokens | 42 | ||
| 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