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Claude Sonnet 5 vs GPT-5.4

Compare Claude Sonnet 5 and GPT-5.4 side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.

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AnthropicClaude Sonnet 5
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OpenAIGPT-5.4
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OpenAI

Claude Sonnet 5 vs GPT-5.4: Overview

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

GPT-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

Claude Sonnet 5 vs GPT-5.4 Comparison Table

PropertyClaude Sonnet 5GPT-5.4
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Mar 2026
Context Window1.0M1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$2.50
Output $/1M$10.00$15.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
70.15%
77.61%
Avg Response Time3.90s7.16s
Median input tokensincl. image tokens2.1K1.4K
Median output tokens61108
Est. cost / taskon this benchmark$0.0048$0.0052
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
66.7%(6/9)
88.9%(8/9)
Object Counting
20%(2/10)
40%(4/10)
Object Understanding
92.9%(13/14)
85.7%(12/14)
Spatial Understanding
78.9%(15/19)
78.9%(15/19)
OCR
Overall Score
83.84%
79.48%
Avg Response Time2.77s3.98s
Median input tokensincl. image tokens642105
Median output tokens6495
Est. cost / taskon this benchmark$0.0019$0.0017
Focused Scene OCR
88.9%(88/99)
75.8%(75/99)
Handwritten Math
50%(5/10)
60%(6/10)
License Plate Recognition
90%(27/30)
90%(27/30)
Text Recognition
80%(24/30)
83.3%(25/30)
VQA & Extraction
80%(48/60)
81.7%(49/60)

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