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Claude Opus 4 vs Grounded SAM

Compare Claude Opus 4 and Grounded SAM side-by-side.

Compare Claude Opus 4 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

Anthropic

Claude Opus 4 vs Grounded SAM: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

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 vs Grounded SAM Comparison Table

PropertyClaude Opus 4Grounded SAM
OrganizationAnthropicIDEA Research
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMay 2025Jan 2024
Context Window200K
Parameters
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$15.00
Output $/1M$75.00
Vision Tasks
Vision Language
Captioning
Classification
Object Detection
OCR
Visual Question Answering
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%
Visual Understanding
Overall Score
56.72%
Avg Response Time19.74s
Defect Detection
66.7%(10/15)
Document Understanding
88.9%(8/9)
Object Counting
0%(0/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
57.9%(11/19)