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Claude Sonnet 4.5 vs Claude Sonnet 4

Compare Claude Sonnet 4.5 and Claude Sonnet 4 side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.

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AnthropicClaude Sonnet 4.5
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AnthropicClaude Sonnet 4

Claude Sonnet 4 is deprecated and can no longer be run. Details and evals are still available on its model page.

Models in this comparison

Claude Sonnet 4.5 vs Claude Sonnet 4: Overview

Claude Sonnet 4.5

Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.

The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.

Claude Sonnet 4

Claude 4 Sonnet, released by Anthropic in May 2025, is the mid-tier model in the Claude 4 family, designed to balance capability, cost, and speed. It is multimodal, accepting both text and images, and extends beyond prior versions with improved “computer use” support, allowing API-driven interaction with desktop-like interfaces. By default, it supports 200,000 tokens of context, but as of August 2025, it also offers a 1 million-token context window in public beta—making it one of the most context-capable models available for processing entire codebases or large document sets in a single request.

Sonnet 4 is significantly cheaper than the flagship Opus while still demonstrating strong reasoning, coding, and instruction-following ability with reduced hallucinations. Its extended context capabilities and lower latency make it well-suited for enterprise-scale knowledge management, software development, research assistants, and productivity automation where both cost efficiency and high reliability are essential.

Claude Sonnet 4.5 vs Claude Sonnet 4 Comparison Table

PropertyClaude Sonnet 4.5Claude Sonnet 4
OrganizationAnthropicAnthropic
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2025May 2025
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$3.00
Output $/1M$15.00$15.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
59.7%
68.66%
Avg Response Time5.67s21.26s
Median input tokensincl. image tokens2.2K
Median output tokens182
Est. cost / taskon this benchmark$0.0092
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
64.3%(9/14)
78.6%(11/14)
Spatial Understanding
63.2%(12/19)
68.4%(13/19)
OCR
Overall Score
67.25%
Avg Response Time3.93s
Median input tokensincl. image tokens735
Median output tokens115
Est. cost / taskon this benchmark$0.0039
Focused Scene OCR
71.7%(71/99)
Handwritten Math
20%(2/10)
License Plate Recognition
53.3%(16/30)
Text Recognition
66.7%(20/30)
VQA & Extraction
75%(45/60)

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