Gemini 3 Flash vs SAM 3

Compare Gemini 3 Flash and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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GoogleGemini 3 Flash
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MetaSAM 3
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Models in this comparison

Meta

Gemini 3 Flash vs SAM 3: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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.

Gemini 3 Flash vs SAM 3 Comparison Table

PropertyGemini 3 FlashSAM 3
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Nov 2025
Context Window1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500
Output $/1M$3.00
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%
Overall Score
74.63%
Avg Response Time9.85s
Median input tokensincl. image tokens1.1K
Median output tokens290
Est. cost / taskon this benchmark$0.0014
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
30%(3/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
84.2%(16/19)

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