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GPT-5 Nano vs SAM 3

Compare GPT-5 Nano and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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OpenAIGPT-5 Nano
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MetaSAM 3
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Models in this comparison

Meta

GPT-5 Nano vs SAM 3: Overview

GPT-5 Nano

GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.

GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.

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-5 Nano vs SAM 3 Comparison Table

PropertyGPT-5 NanoSAM 3
OrganizationOpenAIMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2025Nov 2025
Context Window400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.050
Output $/1M$0.400
Vision Tasks
Object DetectionDemoDemo
CaptioningDemo
ClassificationDemo
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
58.21%
Avg Response Time6.58s
Median input tokensincl. image tokens1.8K
Median output tokens591
Est. cost / taskon this benchmark$0.0003
Defect Detection
86.7%(13/15)
Document Understanding
66.7%(6/9)
Object Counting
0%(0/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
OCR
Overall Score
69%
Avg Response Time6.15s
Median input tokensincl. image tokens122
Median output tokens539
Est. cost / taskon this benchmark$0.0002
Focused Scene OCR
64.6%(64/99)
Handwritten Math
40%(4/10)
License Plate Recognition
83.3%(25/30)
Text Recognition
70%(21/30)
VQA & Extraction
73.3%(44/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology