Gemma 4 26B A4B vs Llama 3.2 Vision 11b
Compare Gemma 4 26B A4B and Llama 3.2 Vision 11b side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, and Classification.
Compare Gemma 4 26B A4B vs Llama 3.2 Vision 11b 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
Models in this comparison
Gemma 4 26B A4B vs Llama 3.2 Vision 11b: Overview
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.
Llama 3.2 Vision 11B, released by Meta on September 25, 2024, is the first mid-sized model in the Llama family with vision capabilities, supporting both text and image inputs with text-only outputs. It contains around 11 billion parameters (~10.6B) and features a 128,000-token context window, making it suitable for multimodal reasoning over long documents and image-text tasks. The model was trained on ~6 billion image–text pairs and has a knowledge cutoff of December 2023.
The model is available in a base and an instruction-tuned (“Vision-Instruct”) version, optimized for tasks like captioning, visual question answering, and image reasoning. It leverages Group-Query Attention (GQA) for improved inference efficiency and scalability. While text tasks officially support multiple languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai), multimodal (image+text) tasks are supported primarily in English. Llama 3.2 Vision 11B is accessible through Hugging Face, Amazon Bedrock, Azure AI Foundry, NVIDIA NIM, and OCI, making it a widely deployable open-weight multimodal foundation model.
Gemma 4 26B A4B vs Llama 3.2 Vision 11b Comparison Table
| Property | Gemma 4 26B A4B | Llama 3.2 Vision 11b |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2024 |
| Context Window | 256K | 128K |
| Parameters | 25.2B | 11B |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.060 | $0.345 |
| Output $/1M | $0.330 | $0.345 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | 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