Gemma 4 31B vs Llama 4 Scout
Gemma 4 31B: 57.7% average across four Vision Evals tasks (#44 of 68 models). We have not run Llama 4 Scout through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Gemma 4 31B vs Llama 4 Scout Comparison Table
Evals updated November 10, 2026Pricing updated November 10, 2026
| Property | Gemma 4 31B | Llama 4 Scout |
|---|---|---|
| Organization | Meta | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2025 |
| Context Window | 256K | 10.0M |
| Parameters | 31B | 109B |
| License | Apache 2.0 | Custom |
| Commercial use | free for commercial use | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.100 |
| Output $/1M | $0.400 | $0.300 |
| Vision Tasks | ||
| Classification | Demo | Supported |
| Object Detection | Demo | Supported |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks | ||
| Overall | 57.7%* | Not evaluated |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | – |
| Avg speed / sample | 34.36s | – |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | – |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | – |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | – |
| OCR | – | – |
| by category | ||
| Reasoning | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | – |
Gemma 4 31B vs Llama 4 Scout: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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.