Gemma 3 27B vs Llama 4 Scout
We don't benchmark Gemma 3 27B or Llama 4 Scout on Vision Evals. Gemma 3 27B is open-weight under Custom. Llama 4 Scout is open-weight under Custom. Gemma 3 27B is cheaper per token ($0.08/M vs $0.10/M input). Test them side by side on your own images below.
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Gemma 3 27B vs Llama 4 Scout Comparison Table
Pricing updated November 10, 2026
| Property | Gemma 3 27B | Llama 4 Scout |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2025 | Apr 2025 |
| Context Window | 128K | 10.0M |
| Parameters | Unknown | 109B |
| License | Custom | Custom |
| Commercial use | See license terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.080 | $0.100 |
| Output $/1M | $0.450 | $0.300 |
| Vision Tasks | ||
| Object Detection | Not listed | Supported |
| Model Features | ||
Both support 3 more features | ||
Gemma 3 27B vs Llama 4 Scout: 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 4 Scout, released on April 5, 2025, is one of Meta AI’s first Llama 4 multimodal models, alongside Maverick. It accepts text + image inputs and produces text outputs, with a knowledge cutoff of August 2024. Scout is notable for its extremely large context window of 10 million tokens, making it well-suited for analyzing very long documents, extended conversations, or large codebases.
Architecturally, Scout uses a Mixture-of-Experts (MoE) system with 16 experts, activating ~17B parameters per inference from a pool of ~109B total parameters, balancing capacity with efficiency. It officially supports 12 languages (including English, Arabic, French, Hindi, and Spanish), while offering multimodal reasoning for images (captioning, Q&A, recognition). Meta highlights that Scout can run on a single Nvidia H100 GPU, making it more accessible than larger-scale Llama 4 models. However, its output token limit is far smaller than its 10M input window, image input support is still constrained, and license restrictions apply for large-scale commercial deployments.