GPT-5.4 vs GPT-5.5

Compare GPT-5.4 and GPT-5.5 side-by-side. See how these vision models stack up in OCR, Image Captioning, Classification, Object Detection, and Open Prompt.

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OpenAIGPT-5.4
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OpenAI
OpenAI

GPT-5.4 vs GPT-5.5: Overview

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-5.5

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

GPT-5.4 vs GPT-5.5 Comparison Table

PropertyGPT-5.4GPT-5.5
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Apr 2026
Context Window1.1M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.50$5.00
Output $/1M$15.00$30.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
77.61%
77.61%
Avg Response Time7.16s30.12s
Median input tokensincl. image tokens1.4K1.4K
Median output tokens108138
Est. cost / taskon this benchmark$0.0052$0.011
Defect Detection
86.7%(13/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
40%(4/10)
30%(3/10)
Object Understanding
85.7%(12/14)
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
78.9%(15/19)

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