Gemini 2.5 Flash vs SAM 3

Compare Gemini 2.5 Flash and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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GoogleGemini 2.5 Flash
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MetaSAM 3
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Models in this comparison

Meta

Gemini 2.5 Flash vs SAM 3: Overview

Gemini 2.5 Flash

Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.

Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.

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 2.5 Flash vs SAM 3 Comparison Table

PropertyGemini 2.5 FlashSAM 3
OrganizationGoogleMeta
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Nov 2025
Context Window1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.300
Output $/1M$2.50
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
55.22%
Avg Response Time24.91s
Median input tokensincl. image tokens294
Median output tokens171
Est. cost / taskon this benchmark$0.0005
Defect Detection
60%(9/15)
Document Understanding
88.9%(8/9)
Object Counting
0%(0/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
52.6%(10/19)

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