Claude Sonnet 4 vs GPT-5 Mini

Compare Claude Sonnet 4 and GPT-5 Mini side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.

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AnthropicClaude Sonnet 4
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OpenAIGPT-5 Mini
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Claude Sonnet 4 vs GPT-5 Mini: Overview

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.

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

Claude Sonnet 4 vs GPT-5 Mini Comparison Table

PropertyClaude Sonnet 4GPT-5 Mini
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2025Aug 2025
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$0.250
Output $/1M$15.00$2.00
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%
Overall Score
68.66%
73.13%
Avg Response Time21.26s11.72s
Median input tokensincl. image tokens1.4K
Median output tokens143
Est. cost / taskon this benchmark$0.0006
Defect Detection
80%(12/15)
80%(12/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
20%(2/10)
10%(1/10)
Object Understanding
78.6%(11/14)
85.7%(12/14)
Spatial Understanding
68.4%(13/19)
89.5%(17/19)

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