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GPT-5 vs GPT-5.4

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

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OpenAIGPT-5
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Models in this comparison

OpenAI
OpenAI

GPT-5 vs GPT-5.4: Overview

GPT-5

GPT-5, released by OpenAI in August 2025, is a multimodal large language model that advances beyond the GPT-4 family with a new “unified system” architecture. This design allows the model to dynamically choose between fast responses and extended reasoning depending on task complexity. It supports text, code, and images, alongside stronger tool use and agentic workflows, making it more adaptable for real-world problem solving. While its exact context window size is not disclosed, GPT-5 is optimized for long-horizon reasoning and multi-step tool chaining, indicating substantially expanded capacity over its predecessors.

The release introduced specialized variants: GPT-5 Pro, offering extended reasoning for complex workflows, and GPT-5 Codex, optimized for advanced coding tasks such as large-scale refactoring and code review. GPT-5 shows benchmark gains in coding, biomedical reasoning, multimodal analysis, and scientific tasks. Developers also gain new controls, such as verbosity and personalization parameters, for greater steerability. With these improvements, GPT-5 positions itself as OpenAI’s most capable and versatile model, suited for enterprise automation, research, healthcare, and sophisticated coding environments.

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.

GPT-5 vs GPT-5.4 Comparison Table

PropertyGPT-5GPT-5.4
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2025Mar 2026
Context Window1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$1.25$2.50
Output $/1M$10.00$15.00
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%
Visual Understanding
Overall Score
77.61%
Avg Response Time7.16s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
86.7%(13/15)
Document Understanding
88.9%(8/9)
Object Counting
40%(4/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
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