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Claude 3.7 Sonnet vs SAM 3

Compare Claude 3.7 Sonnet and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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AnthropicClaude 3.7 Sonnet

Claude 3.7 Sonnet is deprecated and can no longer be run. Details and evals are still available on its model page.

MetaSAM 3
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Meta

Claude 3.7 Sonnet vs SAM 3: Overview

Claude 3.7 Sonnet

Claude 3.7 Sonnet, released by Anthropic in February 2025, is the company’s first hybrid reasoning model, combining fast response generation with an optional “extended thinking mode” that reveals longer, step-by-step reasoning. Like its predecessors, it is multimodal, handling both text and images, but expands its usability with up to 200,000 input tokens and up to 128,000 output tokens (64K generally available, 128K in beta). This makes it well-suited for analyzing large documents, codebases, or multi-turn conversations.

Typical applications include software development, research workflows, extended reasoning tasks, and enterprise-scale knowledge work where a trade-off between speed and visible reasoning is valuable.

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.

Claude 3.7 Sonnet vs SAM 3 Comparison Table

PropertyClaude 3.7 SonnetSAM 3
OrganizationAnthropicMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2024Nov 2025
Context Window200K
Parameters
LicenseProprietaryProprietary
Vision Tasks
Object DetectionDemo
Captioning
Classification
Instance Segmentation
OCR
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question Answering
Zero Shot Segmentation
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Zero-shot Detection