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Claude Sonnet 4.5 vs GPT-5.4 Nano

Compare Claude Sonnet 4.5 and GPT-5.4 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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Claude Sonnet 4.5 vs GPT-5.4 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-5.4 Nano

GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.

While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.

Claude Sonnet 4.5 vs GPT-5.4 Nano Comparison Table

PropertyClaude Sonnet 4.5GPT-5.4 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2025Mar 2026
Context Window200K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$0.200
Output $/1M$15.00$1.25
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%
62.69%
Avg Response Time5.67s3.72s
Median input tokensincl. image tokens2.2K1.4K
Median output tokens182105
Est. cost / taskon this benchmark$0.0092$0.0004
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
10%(1/10)
30%(3/10)
Object Understanding
64.3%(9/14)
64.3%(9/14)
Spatial Understanding
63.2%(12/19)
57.9%(11/19)
OCR
Overall Score
67.25%
62.45%
Avg Response Time3.93s2.59s
Median input tokensincl. image tokens735105
Median output tokens11587
Est. cost / taskon this benchmark$0.0039$0.0001
Focused Scene OCR
71.7%(71/99)
55.6%(55/99)
Handwritten Math
20%(2/10)
20%(2/10)
License Plate Recognition
53.3%(16/30)
83.3%(25/30)
Text Recognition
66.7%(20/30)
70%(21/30)
VQA & Extraction
75%(45/60)
66.7%(40/60)

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