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Claude Sonnet 4.5 vs GPT-5.4 Mini

Compare Claude Sonnet 4.5 and GPT-5.4 Mini 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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OpenAIGPT-5.4 Mini
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Claude Sonnet 4.5 vs GPT-5.4 Mini: 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.

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

Claude Sonnet 4.5 vs GPT-5.4 Mini Comparison Table

PropertyClaude Sonnet 4.5GPT-5.4 Mini
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2025Mar 2026
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$0.750
Output $/1M$15.00$4.50
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
59.7%
74.63%
Avg Response Time5.67s7.87s
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)
30%(3/10)
Object Understanding
64.3%(9/14)
85.7%(12/14)
Spatial Understanding
63.2%(12/19)
78.9%(15/19)
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