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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

Meta

Llama 4 Scout vs SAM 3: Overview

Llama 4 Scout

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.

SAM 3

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

PropertyLlama 4 ScoutSAM 3
OrganizationMetaMeta
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2025Nov 2025
Context Window10.0M
Parameters109B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100
Output $/1M$0.300
Vision Tasks
Object DetectionDemo
CaptioningDemo
Instance Segmentation
OCRDemo
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question AnsweringDemo
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 Time43.93s
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 Time0.74s
Median input tokensincl. image tokens472
Median output tokens12
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)