Claude Opus 4.5 vs Grounded SAM

Compare Claude Opus 4.5 and Grounded SAM side-by-side.

Compare Claude Opus 4.5 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 4.5 vs Grounded SAM: Overview

Claude Opus 4.5

Claude Opus 4.5 is Anthropic’s most advanced large language model in the Claude Opus family, designed for high-end reasoning, coding, and autonomous agent workflows. Released in late 2025, it targets developers and enterprises that need reliable long-context understanding and strong multi-step problem solving in production environments.

The model supports text and code natively, with reported multimodal capabilities for documents and images, and offers an exceptionally large context window of up to roughly 200,000 tokens. Claude Opus 4.5 emphasizes long-horizon task execution, complex code generation and refactoring, and sustained reasoning over large inputs. In the current landscape, it positions itself as a premium, accuracy- and reasoning-focused alternative to faster or cheaper peers, trading cost for depth and contextual fidelity. Typical applications include advanced coding assistants, research analysis, agentic automation, and enterprise knowledge workflows deployed via Anthropic’s API or major cloud platforms.

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 4.5 vs Grounded SAM Comparison Table

PropertyClaude Opus 4.5Grounded SAM
OrganizationAnthropicIDEA Research
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2025Jan 2024
Context Window200K
Parameters
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00
Output $/1M$25.00
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