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Claude Opus 4 vs GPT-5.4

Compare Claude Opus 4 and GPT-5.4 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 Opus 4

Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.

OpenAIGPT-5.4
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Models in this comparison

Anthropic
OpenAI

Claude Opus 4 vs GPT-5.4: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

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 Opus 4 vs GPT-5.4 Comparison Table

PropertyClaude Opus 4GPT-5.4
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2025Mar 2026
Context Window200K1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$2.50
Output $/1M$75.00$15.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
56.72%
77.61%
Avg Response Time19.74s7.16s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
66.7%(10/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
0%(0/10)
40%(4/10)
Object Understanding
64.3%(9/14)
85.7%(12/14)
Spatial Understanding
57.9%(11/19)
78.9%(15/19)
OCR
Overall Score
79.48%
Avg Response Time3.98s
Median input tokensincl. image tokens105
Median output tokens95
Est. cost / taskon this benchmark$0.0017
Focused Scene OCR
75.8%(75/99)
Handwritten Math
60%(6/10)
License Plate Recognition
90%(27/30)
Text Recognition
83.3%(25/30)
VQA & Extraction
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