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Posted on Sep 12

What Is GPT-5.4? Complete Guide to OpenAI's Most Capable Model

TL;DR / Quick Answer

GPT-5.4 is OpenAI's most advanced frontier model for professional work, released March 5, 2026. It combines industry-leading coding capabilities from GPT-5.3-Codex with enhanced reasoning, computer use, and tool integration. The model achieves an 83% win rate on knowledge work tasks, 75% on computer-use benchmarks, and uses significantly fewer tokens than GPT-5.2. It is available through the API at $2.50 per million input tokens and $15 per million output tokens, with a Pro version priced at $30/$180 for complex tasks.

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Introduction

OpenAI released GPT-5.4 on March 5, 2026, targeting professional workflows that require reasoning, coding, computer interaction, and tool use.

The model is designed to address several common problems with AI-powered applications:

Hallucinated or inaccurate facts
Slow multi-step workflows
High token usage
Limited browser and desktop automation
Context limitations when working with large codebases
High overhead from loading large tool definitions

GPT-5.4 reports an 83% win rate against industry professionals on real-world knowledge work tasks. It is also 33% less likely to make factual errors and completes computer-use tasks up to 3x faster than earlier models.

For developers building AI-powered applications, API integration testing is essential. Tools such as Apidog can help you design, debug, and test API endpoints while integrating GPT-5.4 or building supporting services. Apidog combines API design, debugging, testing, and mocking in a single interface.

This guide covers:

Performance improvements over GPT-5.2 and GPT-5.3-Codex
Coding, computer-use, vision, and knowledge-work benchmarks
GPT-5.4's native computer-use capabilities
Tool search and MCP integration
Pricing and context-window options
How to choose between GPT-5.4, GPT-5.4 Pro, GPT-5.3-Codex, and GPT-5.2
API integration considerations for developers

What Is GPT-5.4?

GPT-5.4 is OpenAI's first general-purpose model with native computer-use capabilities. It combines the coding performance of GPT-5.3-Codex with enhanced reasoning, visual perception, and tool integration.

The model targets three main professional scenarios.

Knowledge Work

GPT-5.4 can create and analyze spreadsheets, presentations, documents, and other business artifacts across 44 occupations.

It matches or exceeds industry professionals in 83% of comparisons on GDPval, up from 70.9% for GPT-5.2.

Computer Use and Agents

GPT-5.4 can operate computers through:

Mouse and keyboard commands
Browser automation
Screenshot interpretation
Multi-step workflows across applications
Custom safety policies and confirmation requirements

It achieves a 75% success rate on OSWorld-Verified, compared with a reported human performance of 72.4%.

Coding and Development

GPT-5.4 supports writing, debugging, and iterating on code. It scores 57.7% on SWE-Bench Pro and supports context windows of up to 1 million tokens in experimental Codex workflows.

GPT-5.4 is available in two variants:

GPT-5.4 — Standard model for most professional tasks
GPT-5.4 Pro — Higher-cost model for complex reasoning tasks

Key Improvements Over GPT-5.2

GPT-5.4 improves on GPT-5.2 in four areas that matter when building production AI workflows.

1. Fewer Factual Errors

False claims dropped 33% at the individual-claim level, while complete responses contain 18% fewer errors overall.

This is particularly relevant for applications that generate:

Legal documents
Financial models
Technical documentation
Research summaries
Operational recommendations

You should still validate model output before using it in high-impact workflows. Model-level improvements reduce risk, but they do not replace application-level checks.

2. Lower Token Usage

GPT-5.4 uses significantly fewer tokens than GPT-5.2 for comparable tasks.

In tool-heavy workflows measured with the MCP Atlas benchmark, token usage dropped 47% while maintaining accuracy. For high-volume API users, lower token consumption can offset GPT-5.4's higher per-token price.

Track both cost and task completion when evaluating a migration:

Record input and output tokens for the existing model.
Run the same workload with GPT-5.4.
Compare successful task completions.
Calculate cost per successful task rather than cost per request.

3. Native Computer Use

Earlier workflows often required separate computer-use models. GPT-5.4 handles computer interaction natively by:

Interpreting screenshots
Clicking UI elements using coordinates
Entering text through keyboard actions
Automating browsers with Playwright
Navigating desktop environments
Executing mouse movements and drag operations

Developers can configure safety behavior through system messages, including when the model must request confirmation before taking an action.

4. Tool Search

Tool search allows the model to retrieve tool definitions only when they are needed.

Without tool search, an application may load thousands of tool definitions into every request. That increases input-token usage and can reduce the available context for the actual task.

With tool search:

Provide a lightweight list of available tools.
Let the model identify the tool it needs.
Retrieve the full definition on demand.
Append the definition only when the model is ready to use it.

On the Toolathlon benchmark, GPT-5.4 achieves 54.6% accuracy compared with 45.7% for GPT-5.2. On the MCP Atlas benchmark, GPT-5.4 also completes tasks with fewer tool yields, which is a proxy for lower latency.

GPT-5.4 Performance Benchmarks

The following results show where GPT-5.4 performs well and where previous models remain competitive.

Knowledge Work: GDPval

Model	Win Rate vs. Professionals
GPT-5.4	83.0%
GPT-5.4 Pro	82.0%
GPT-5.2 Pro	74.1%
GPT-5.2	70.9%

GDPval evaluates well-specified knowledge-work tasks across 44 occupations from the nine industries contributing most to US GDP.

Example tasks include:

Sales presentations
Accounting spreadsheets
Urgent-care schedules
Manufacturing diagrams
Short videos

Spreadsheet and Document Creation

On internal investment-banking modeling tasks:

GPT-5.4: 87.3% mean score
GPT-5.2: 68.4% mean score

For presentation evaluation, human raters preferred GPT-5.4 outputs 68% of the time. The reported improvements were associated with stronger aesthetics, more visual variety, and more effective use of image generation.

Coding: SWE-Bench Pro

Model	Accuracy	Estimated Latency
GPT-5.4	57.7%	~1,000 seconds
GPT-5.3-Codex	56.8%	~1,200 seconds
GPT-5.2	55.6%	~1,500 seconds

GPT-5.4 matches or exceeds GPT-5.3-Codex on SWE-Bench Pro while reporting lower latency across reasoning efforts.

The /fast mode in Codex provides up to 1.5x faster token velocity with GPT-5.4.

Computer Use: OSWorld-Verified

OSWorld-Verified measures whether a model can navigate desktop environments using screenshots and keyboard or mouse actions.

GPT-5.4: 75.0%
GPT-5.3-Codex: 74.0% with the API parameter that preserves original image resolution
GPT-5.2: 47.3%
Human performance: 72.4%

The benchmark includes workflows such as:

Email and calendar management
Bulk data entry
File operations
Cross-application tasks

Web Browsing: BrowseComp

BrowseComp evaluates persistent web research for hard-to-locate information.

GPT-5.4 Pro: 89.3%
GPT-5.4: 82.7%
GPT-5.2 Pro: 77.9%
GPT-5.2: 65.8%

The 17% absolute improvement over GPT-5.2 reflects better multi-source synthesis and more persistent search strategies.

Visual Understanding

MMMU Pro, without tools:

GPT-5.4: 81.2%
GPT-5.2: 79.5%

OmniDocBench document parsing:

GPT-5.4: 0.109 normalized edit distance
GPT-5.2: 0.140 normalized edit distance

For OmniDocBench, lower error is better.

Computer Use and Vision Capabilities

GPT-5.4's computer-use capabilities are important for developers building agents that interact with existing web and desktop software.

How Computer Use Works

The model interprets screenshots from a browser or desktop interface and produces actions such as:

Coordinate-based clicks
Keyboard input
Playwright browser commands
Mouse movements
Drag-and-drop operations

A production implementation should define which actions require confirmation. For example:

Allow navigation and read-only actions automatically.
Request confirmation before submitting forms.
Require confirmation before sending messages or making purchases.
Restrict access to sensitive applications and data.

Real-World Computer-Use Example

Mainstay tested GPT-5.4 across approximately 30,000 HOA and property-tax portals.

Reported results:

GPT-5.4: 95% first-attempt success
GPT-5.4: 100% success within three attempts
Previous CUA models: 73–79% success
Session completion: 3x faster with GPT-5.4
Token usage: 70% fewer tokens per session

The model navigated portal interfaces, extracted data from different layouts, handled authentication flows, and managed edge cases such as captchas and multi-step forms.

Enhanced Visual Perception

GPT-5.4 supports an original image-input detail level with:

Up to 10.24 million total pixels
A 6,000-pixel maximum dimension
Full-fidelity perception for dense, high-resolution images

The high detail level supports up to 2.56 million total pixels or a 2,048-pixel maximum dimension.

Early API user testing showed improvements in:

UI element localization
Image understanding
Click accuracy

Document Parsing Improvements

The improved visual perception supports parsing of:

Multi-page PDFs containing tables and figures
Scanned documents with varied layouts
Screenshots containing text and interface elements
Technical diagrams and charts

The OmniDocBench result improved from 0.140 error for GPT-5.2 to 0.109 for GPT-5.4.

Coding and Development Features

GPT-5.4 inherits GPT-5.3-Codex's coding performance and adds computer-use features for integrated development workflows.

Frontend Development

Internal evaluations found GPT-5.4 effective for complex frontend tasks, including work requiring both visual quality and functional behavior.

The experimental Playwright Interactive skill in Codex demonstrates this type of workflow.

For example, a single prompt generated an isometric theme-park simulation with:

Tile-based path placement
Ride and scenery construction
Guest pathfinding and queueing
Park metrics for money, guests, happiness, and cleanliness
Browser playtesting through Playwright
Image generation for isometric assets

The model generated the application, then used Playwright to automate playtests and verify:

Placement behavior
Navigation
Guest reactions
UI stability across multiple rounds

Fast Mode for Developers

GPT-5.4 in Codex supports /fast mode with up to 1.5x faster token velocity.

API developers can access equivalent speeds through priority processing. This preserves the same intelligence while reducing iteration time during debugging and development.

Context Window Support

GPT-5.4 Codex includes experimental support for a 1-million-token context window.

The relevant configuration parameters are:

model_context_window
model_auto_compact_token_limit

Requests exceeding the standard 272K-token context count against usage limits at twice the normal rate.

This extended context can support analysis of:

Entire codebases
Large documentation sets
Multi-file projects
Long technical specifications

When integrating GPT-5.4 into a large codebase, keep your API documentation synchronized with the implementation. Apidog can import OpenAPI or Swagger specifications, generate interactive documentation, and synchronize documentation with your codebase as you integrate GPT-5.4 features.

Tool Integration and Search

Tool search changes how models interact with external tools and MCP servers.

How Tool Search Works

The traditional approach loads every tool definition into each request. In systems with many tools, this can add thousands or tens of thousands of input tokens.

The tool-search approach is more selective:

Send a lightweight catalog of available tools.
Allow the model to search the catalog.
Retrieve the schema for the selected tool.
Execute the tool and return its result.
Continue the workflow with only the necessary definitions in context.

This approach reduces upfront token usage and helps preserve context for task-specific information.

Token Savings Example

Scale's MCP Atlas benchmark tested 250 tasks with all 36 MCP servers enabled.

Without tool search, the token breakdown included:

65,320 upfront input tokens for tool definitions
Additional tokens from tool outputs
Output tokens

Tool search removes the upfront definition cost while preserving cache efficiency.

MCP Atlas Performance

On the MCP Atlas benchmark:

GPT-5.4: 67.2% accuracy
GPT-5.2: 60.6% accuracy

The results indicate that GPT-5.4 can work with larger tool ecosystems without sacrificing accuracy or overwhelming the context window.

Agentic Tool Calling

Toolathlon evaluates multi-step tool workflows such as:

Reading emails
Extracting attachments
Uploading files
Grading results
Recording outcomes

Tool yields—the number of rounds waiting for tool responses—are a more useful latency indicator than raw tool-call counts because they account for parallelization. GPT-5.4 completes these tasks in fewer rounds.

GPT-5.4 vs. GPT-5.3-Codex vs. GPT-5.2

Choose a model based on the capabilities your application actually needs.

Use GPT-5.4 When You Need

Native computer use
Browser automation
Spreadsheets, presentations, and documents
Tool-heavy workflows
MCP servers and external APIs
Better token efficiency at scale
Long-context analysis
A single model for coding and computer interaction

Use GPT-5.3-Codex When

Your workload is focused on pure coding
You already have an established Codex integration
You do not need computer-use capabilities
Its pricing is more suitable for your workload

GPT-5.3-Codex remains competitive on SWE-Bench Pro, scoring 56.8% compared with 57.7% for GPT-5.4.

Use GPT-5.2 When

You only need simple question answering
You are summarizing or generating straightforward content
Your workflow is single-turn and non-agentic
Minimizing per-token price is more important than newer capabilities

Pricing Comparison

Model	Input Price	Cached Input	Output Price
GPT-5.2	$1.75/M	$0.175/M	$14/M
GPT-5.4	$2.50/M	$0.25/M	$15/M
GPT-5.2 Pro	$21/M	—	$168/M
GPT-5.4 Pro	$30/M	—	$180/M

Batch and Flex pricing are available at 50% of standard rates. Priority processing is priced at 200% of standard rates.

When comparing models, measure the cost per successful workflow:

cost_per_successful_task =
  total_tokens_used * token_price / completed_tasks

A higher per-token price may still produce a lower cost per completed task if the model uses fewer tokens or requires fewer retries.

Availability and Access Options

GPT-5.4 began rolling out on March 5, 2026 across ChatGPT, Codex, and the API.

ChatGPT Access

GPT-5.4 Thinking is available to:

ChatGPT Plus subscribers
ChatGPT Team subscribers
ChatGPT Pro subscribers

GPT-5.4 Pro is available to:

ChatGPT Pro subscribers
ChatGPT Enterprise subscribers

GPT-5.2 Thinking remains available under Legacy Models for three months and is scheduled to retire on June 5, 2026.

Enterprise and Education users can access the model early through admin settings.

Codex Access

GPT-5.4 is the default model in Codex with:

Experimental 1-million-token context support
Playwright Interactive for browser playtesting
/fast mode for up to 1.5x token velocity

API Access

The API model names are:

gpt-5.4 — Standard model
gpt-5.4-pro — Pro model for complex tasks

Context-window options:

Standard: 272K tokens
Extended: Up to 1M tokens experimentally, with a 2x usage rate

Pricing:

Standard: $2.50/M input, $0.25/M cached input, $15/M output
Pro: $30/M input, $180/M output
Batch/Flex: 50% discount
Priority: 2x standard rate

For a production integration, validate the following before switching models:

The model name is available in your API account.
Your request and response schemas are compatible.
Your token-budget calculations include cached input where applicable.
Your tool definitions work with the model's tool-search behavior.
Your retry and timeout policies account for multi-step workflows.
Your tests cover both successful and rejected tool calls.

Deprecation Timeline

GPT-5.2 Thinking is scheduled to retire on June 5, 2026. Migrate and validate existing workflows before this date to avoid disruption.

Conclusion

GPT-5.4 delivers measurable improvements across knowledge work, computer use, and coding:

83% GDPval win rate
75% OSWorld-Verified score
57.7% SWE-Bench Pro accuracy
33% fewer false claims
18% fewer errors in complete responses
47% lower token usage in tool-heavy workflows
Native computer operation
On-demand tool search
Experimental 1-million-token context support

For developers integrating GPT-5.4, API testing and debugging should be part of the migration plan. Apidog provides API design, debugging, testing, mocking, and documentation capabilities that can help validate AI-agent integrations and supporting services.

Key Takeaways

GPT-5.4 reduces factual errors and token usage compared with GPT-5.2.
It supports native computer operation through mouse and keyboard actions.
It combines coding, reasoning, vision, and tool use in one general-purpose model.
Tool search reduces the cost of working with large MCP and API ecosystems.
The standard API price is $2.50 per million input tokens and $15 per million output tokens.
Experimental Codex workflows support context windows of up to 1 million tokens.
GPT-5.4 Pro is intended for complex reasoning tasks but costs substantially more.

When to Adopt GPT-5.4

Consider GPT-5.4 when:

Your application needs computer use or browser automation.
Token efficiency matters for high-volume workloads.
Factual accuracy is important.
You work with large tool ecosystems or MCP servers.
You need to analyze large codebases or document collections.
Your existing model requires too many retries or tool-calling rounds.

When to Wait

GPT-5.2 or GPT-5.3-Codex may remain sufficient when:

Your application only handles simple questions.
Budget constraints prioritize the lowest per-token price.
Your current workflow does not require computer use.
Existing GPT-5.2 or GPT-5.3-Codex integrations already meet reliability and latency requirements.

FAQ

What is the difference between GPT-5.4 and GPT-5.2?

GPT-5.4 achieves an 83% win rate on knowledge work compared with 70.9% for GPT-5.2. It uses significantly fewer tokens, supports native computer use, and reduces factual errors by 33%.

GPT-5.4 costs more per token: $2.50/$15 per million input/output tokens compared with $1.75/$14 for GPT-5.2. However, lower token usage may reduce total workflow costs.

How much does the GPT-5.4 API cost?

GPT-5.4 costs:

$2.50 per million input tokens
$0.25 per million cached input tokens
$15 per million output tokens

GPT-5.4 Pro costs:

$30 per million input tokens
$180 per million output tokens

Batch and Flex pricing offer 50% discounts.

Does GPT-5.4 have a context-window limit?

The standard context window is 272K tokens.

Experimental 1-million-token context support is available in Codex through the model_context_window and model_auto_compact_token_limit parameters. Requests exceeding 272K tokens count at twice the normal usage rate.

What is GPT-5.4 Pro used for?

GPT-5.4 Pro targets maximum performance on complex reasoning tasks.

It scores 89.3% on BrowseComp compared with 82.7% for standard GPT-5.4. On GDPval, GPT-5.4 Pro scores 82.0% compared with 83.0% for the standard model.

The Pro model costs $30/$180 per million input/output tokens compared with $2.50/$15 for standard GPT-5.4.

When did GPT-5.4 release?

GPT-5.4 was released on March 5, 2026, with a gradual rollout across ChatGPT, Codex, and the API.

GPT-5.2 Thinking remains available under Legacy Models until June 5, 2026.

Can GPT-5.4 use computers and browsers?

Yes. GPT-5.4 is OpenAI's first general-purpose model with native computer-use capabilities.

It can:

Interpret screenshots
Issue mouse and keyboard commands
Automate browsers through Playwright
Navigate desktop environments
Complete multi-step workflows

Applications should define confirmation requirements for higher-risk actions.

What is tool search in GPT-5.4?

Tool search allows GPT-5.4 to retrieve tool definitions on demand rather than loading every definition into the initial request.

This reduces upfront token usage and supports workflows with large tool ecosystems. In tool-heavy benchmarks, GPT-5.4 used 47% fewer tokens than GPT-5.2.

How does GPT-5.4 compare with GPT-5.3-Codex for coding?

GPT-5.4 scores 57.7% on SWE-Bench Pro compared with 56.8% for GPT-5.3-Codex. It also adds native computer-use capabilities and reports lower latency.

GPT-5.3-Codex may still be suitable for established, coding-focused workflows that do not need computer interaction.

Is GPT-5.4 available in ChatGPT?

Yes. GPT-5.4 Thinking is available to Plus, Team, and Pro subscribers.

GPT-5.4 Pro is available to Pro and Enterprise subscribers. GPT-5.2 Thinking remains available under Legacy Models until June 5, 2026.

What are the safety considerations for GPT-5.4?

GPT-5.4 is treated as having High cyber capability under OpenAI's Preparedness Framework.

Reported protections include:

An expanded cyber safety stack
Monitoring systems
Trusted access controls
Asynchronous blocking for higher-risk requests on Zero Data Retention surfaces

False positives may occur as classifiers continue to improve. Developers should also implement application-level authorization, confirmation flows, logging, and output validation for production systems.