Claude Opus 4.1 vs Grounded SAM

Compare Claude Opus 4.1 and Grounded SAM side-by-side.

Compare Claude Opus 4.1 vs Grounded SAM live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Claude Opus 4.1 vs Grounded SAM: Overview

Claude Opus 4.1

Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.

On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.

Grounded SAM

Grounded SAM is an open-vocabulary image segmentation model developed by IDEA Research, released in January 2024 under the Apache 2.0 license. It combines Grounding DINO, a zero-shot open-vocabulary object detector, with the Segment Anything Model to produce precise segmentation masks for objects identified through free-form text prompts. The two models are used sequentially: Grounding DINO localizes objects from a text query, and SAM generates the corresponding segmentation masks.

Grounded SAM enables zero-shot instance segmentation without task-specific training data, making it applicable to domains where labeled segmentation data is scarce. It supports arbitrary text queries and can segment objects not represented in standard training sets. The model is commonly used in automated labeling pipelines, robotic perception, and domain-specific vision applications requiring open-vocabulary segmentation.

Claude Opus 4.1 vs Grounded SAM Comparison Table

PropertyClaude Opus 4.1Grounded SAM
OrganizationAnthropicIDEA Research
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Jan 2024
Context Window200K
Parameters
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$15.00
Output $/1M$75.00
Vision Tasks
Vision Language
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Visual Question AnsweringDemo
Zero Shot Segmentation
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Zero-shot Detection
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
59.7%
Avg Response Time7.09s
Median input tokensincl. image tokens2.0K
Median output tokens140
Est. cost / taskon this benchmark$0.040
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
0%(0/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
63.2%(12/19)

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