Gemma 4 31B vs SAM 3

Compare Gemma 4 31B and SAM 3 side-by-side. See how these vision models stack up in Object Detection.

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GoogleGemma 4 31B
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MetaSAM 3
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Models in this comparison

Meta

Gemma 4 31B vs SAM 3: Overview

Gemma 4 31B

Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.

For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.

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.

Gemma 4 31B vs SAM 3 Comparison Table

PropertyGemma 4 31BSAM 3
OrganizationGoogleMeta
Categoryopenclosed
Modalitymultimodalmultimodal
Release DateApr 2026Nov 2025
Context Window256K
Parameters31B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.120
Output $/1M$0.350
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
Multimodal Vision
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
67.16%
Avg Response Time34.59s
Median input tokensincl. image tokens294
Median output tokens169
Est. cost / taskon this benchmark$0.0001
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
73.7%(14/19)

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