Claude Sonnet 4.5 vs GPT-5 Nano

Compare Claude Sonnet 4.5 and GPT-5 Nano 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 Nano
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Claude Sonnet 4.5 vs GPT-5 Nano: 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 Nano

GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.

GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.

Claude Sonnet 4.5 vs GPT-5 Nano Comparison Table

PropertyClaude Sonnet 4.5GPT-5 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2025Aug 2025
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$0.050
Output $/1M$15.00$0.400
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%
58.21%
Avg Response Time5.67s6.58s
Median input tokensincl. image tokens2.2K1.8K
Median output tokens182591
Est. cost / taskon this benchmark$0.0092$0.0003
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
66.7%(6/9)
Object Counting
10%(1/10)
0%(0/10)
Object Understanding
64.3%(9/14)
64.3%(9/14)
Spatial Understanding
63.2%(12/19)
57.9%(11/19)
OCR
Overall Score
67.44%
69%
Avg Response Time3.58s6.15s
Median input tokensincl. image tokens701122
Median output tokens114539
Est. cost / taskon this benchmark$0.0038$0.0002
Focused Scene OCR
71.7%(71/99)
64.6%(64/99)
Handwritten Math
40%(4/10)
License Plate Recognition
53.3%(16/30)
83.3%(25/30)
Text Recognition
70%(21/30)
VQA & Extraction
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