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Claude 3.5 Haiku vs SAM 3

Compare Claude 3.5 Haiku and SAM 3 side-by-side.

Compare Claude 3.5 Haiku 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

Claude 3.5 Haiku vs SAM 3: Overview

Claude 3.5 Haiku

Claude 3.5 Haiku, released by Anthropic in October 2024, is the fastest member of the Claude 3.5 family, optimized for low-latency, high-throughput applications. It is a multimodal model that handles both text and image inputs and supports a large ~200,000-token context window. Haiku is designed to balance efficiency with intelligence, outperforming even Claude 3 Opus on several reasoning benchmarks while maintaining its hallmark speed.

Typical applications include real-time chatbots, code completion, large-scale data extraction, and content moderation—scenarios where rapid response and scalability are essential.

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 3.5 Haiku vs SAM 3 Comparison Table

PropertyClaude 3.5 HaikuSAM 3
OrganizationAnthropicMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 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