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Claude Opus 4.1 vs Claude Sonnet 5

Compare Claude Opus 4.1 and Claude Sonnet 5 side-by-side. See how these vision models stack up in Open Prompt, Classification, Object Detection, OCR, and Image Captioning.

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AnthropicClaude Opus 4.1
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AnthropicClaude Sonnet 5
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Models in this comparison

Claude Opus 4.1 vs Claude Sonnet 5: 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.

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

Claude Opus 4.1 vs Claude Sonnet 5 Comparison Table

PropertyClaude Opus 4.1Claude Sonnet 5
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2025Jun 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$2.00
Output $/1M$75.00$10.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
59.7%
70.15%
Avg Response Time7.09s3.90s
Median input tokensincl. image tokens2.0K2.1K
Median output tokens14061
Est. cost / taskon this benchmark$0.040$0.0048
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
88.9%(8/9)
66.7%(6/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
64.3%(9/14)
92.9%(13/14)
Spatial Understanding
63.2%(12/19)
78.9%(15/19)
OCR
Overall Score
68.56%
83.84%
Avg Response Time5.08s2.77s
Median input tokensincl. image tokens552642
Median output tokens9764
Est. cost / taskon this benchmark$0.016$0.0019
Focused Scene OCR
73.7%(73/99)
88.9%(88/99)
Handwritten Math
30%(3/10)
50%(5/10)
License Plate Recognition
53.3%(16/30)
90%(27/30)
Text Recognition
80%(24/30)
80%(24/30)
VQA & Extraction
68.3%(41/60)
80%(48/60)

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