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GPT-5.6 Luna vs SAM 3

Compare GPT-5.6 Luna and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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OpenAIGPT-5.6 Luna
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MetaSAM 3
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Models in this comparison

Meta

GPT-5.6 Luna vs SAM 3 Comparison Table

Evals updated July 24, 2026Pricing updated July 25, 2026

PropertyGPT-5.6 LunaSAM 3
OrganizationOpenAIMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2026Nov 2025
Context Window1.5M
Parameters
LicenseProprietaryCustom
Pricing per 1M tokens
Input $/1M$1.00
Output $/1M$6.00
Vision Tasks
Object DetectionDemoDemo
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Instance Segmentation
Multi-Label Classification
OCRDemo
Open Vocabulary Object Detection
Promptable Concept SegmentationDemo
Video Object Tracking
Vision Language
Visual Question AnsweringDemo
Zero Shot Segmentation
Model Features
Foundation Vision
Multimodal Vision
LLMs with Vision Capabilities
Zero-shot Detection
Vision Evalsground-truth scores across 6 vision tasks
Overall
76.8%
Not evaluated
Object Detection
59.9%
Counting
66.2%
Identification
78.1%
OCR
88.4%
Data Extraction
81.4%
Reasoning
87.0%
Avg cost / sample$0.0052
Avg speed / sample6.9s

GPT-5.6 Luna vs SAM 3: Overview

GPT-5.6 Luna

GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.

GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.

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.

Frequently Asked Questions

SAM 3 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

GPT-5.6 Luna is released under Proprietary, while SAM 3 uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.

Yes. The comparison demo on this page runs both models on the same image side by side for object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.