Llama 4 Scout vs SAM 3
Compare Llama 4 Scout and SAM 3 side-by-side.
Compare Llama 4 Scout vs SAM 3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Llama 4 Scout vs SAM 3: Overview
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.
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
Llama 4 Scout vs SAM 3 Comparison Table
| Property | Llama 4 Scout | SAM 3 |
|---|---|---|
| Organization | Meta | Meta |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2025 | Nov 2025 |
| Context Window | 10.0M | — |
| Parameters | 109B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | |
| Output $/1M | $0.300 | |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| Instance Segmentation | ||
| OCR | Demo | |
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Zero-shot Detection | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 67.16% | |
| Avg Response Time | 43.93s | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 71.4%(10/14) | |
| Spatial Understanding | 73.7%(14/19) | |
| OCR | ||
| Overall Score | 70.74% | |
| Avg Response Time | 0.74s | |
| Median input tokensincl. image tokens | 472 | |
| Median output tokens | 12 | |
| Est. cost / taskon this benchmark | $0.0001 | |
| Focused Scene OCR | 56.6%(56/99) | |
| Handwritten Math | 70%(7/10) | |
| License Plate Recognition | 93.3%(28/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 78.3%(47/60) | |