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Claude Opus 4.7 vs Claude Sonnet 4.6

Compare Claude Opus 4.7 and Claude Sonnet 4.6 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.

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AnthropicClaude Opus 4.7
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AnthropicClaude Sonnet 4.6
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Models in this comparison

Claude Opus 4.7 vs Claude Sonnet 4.6: Overview

Claude Opus 4.7

Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.

Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.

Claude Sonnet 4.6

Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.

Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.

Claude Opus 4.7 vs Claude Sonnet 4.6 Comparison Table

PropertyClaude Opus 4.7Claude Sonnet 4.6
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Feb 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$3.00
Output $/1M$25.00$15.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
67.16%
70.15%
Avg Response Time4.85s4.24s
Median input tokensincl. image tokens2.4K2.2K
Median output tokens110105
Est. cost / taskon this benchmark$0.015$0.0080
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
30%(3/10)
Object Understanding
85.7%(12/14)
71.4%(10/14)
Spatial Understanding
68.4%(13/19)
78.9%(15/19)
OCR
Overall Score
86.9%
81.66%
Avg Response Time4.19s3.42s
Median input tokensincl. image tokens969736
Median output tokens8185
Est. cost / taskon this benchmark$0.0069$0.0035
Focused Scene OCR
88.9%(88/99)
85.9%(85/99)
Handwritten Math
80%(8/10)
50%(5/10)
License Plate Recognition
93.3%(28/30)
90%(27/30)
Text Recognition
86.7%(26/30)
86.7%(26/30)
VQA & Extraction
81.7%(49/60)
73.3%(44/60)

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