Gemma 3 27B vs Llama 3.2 Vision 11b
Compare Gemma 3 27B and Llama 3.2 Vision 11b side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Gemma 3 27B vs Llama 3.2 Vision 11b Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemma 3 27B | Llama 3.2 Vision 11b |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Sep 2024 |
| Context Window | 128K | 128K |
| Parameters | 11B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | |
| Output $/1M | $0.300 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| Multimodal Vision | ||
Gemma 3 27B vs Llama 3.2 Vision 11b: Overview
Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.
The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.
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.