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GPT-4.1 nano vs SAM 3

Compare GPT-4.1 nano and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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OpenAIGPT-4.1 nano

GPT-4.1 nano 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

GPT-4.1 nano vs SAM 3: Overview

GPT-4.1 nano

GPT-4.1 nano, released by OpenAI in April 2025, is the smallest and most cost-efficient member of the GPT-4.1 family. It is multimodal, supporting both text and image inputs, and retains the family’s extended 1 million-token context window—allowing it to handle large documents or codebases despite its lightweight design. Its training knowledge extends to June 2024.

GPT-4.1 nano prioritizes speed and affordability over raw reasoning power. While less capable than GPT-4.1 and GPT-4.1 mini, it is well-suited for high-volume or latency-sensitive workloads such as classification, autocomplete, content moderation, and lightweight assistants. This makes it an attractive option for developers seeking scalable deployment where efficiency is more critical than deep reasoning.

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.

GPT-4.1 nano vs SAM 3 Comparison Table

PropertyGPT-4.1 nanoSAM 3
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2025Nov 2025
Context Window1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100
Output $/1M$0.400
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