GPT 5.4 Complete Guide 2026: Features, Pricing, Benchmarks & How to Use | NxCode

NxCode Team

2026-03-29•17 min read

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Key Takeaways

OpenAI's most capable model ever : GPT 5.4 scores 57.7% on SWE-bench Pro, 75% on OSWorld (surpassing the 72.4% human expert baseline), and 83% on GDPval knowledge work -- all in one unified model.
Five variants for every workload : Standard ($2.50/$15 per MTok), Thinking (interactive reasoning), Pro ($30/$180 per MTok), Mini (~$0.40/$1.60), and Nano (edge/embedded) cover everything from budget prototyping to premium enterprise.
1M token context window : Analyze entire codebases or document collections in a single request, with a 128K max output. Input pricing doubles above the 272K mark.
First AI to exceed human desktop performance : 75% on OSWorld means GPT 5.4 can control desktop applications, fill forms, and navigate browsers more accurately than human expert testers (72.4%).
Absorbs GPT-5.3-Codex : This is the first mainline model to include frontier coding capabilities -- Codex users can upgrade without losing performance, while gaining computer use and knowledge work.

GPT 5.4 Complete Guide: Features, Pricing, Benchmarks & How to Use

March 29, 2026 -- OpenAI released GPT 5.4 on March 5, 2026, and it represents a fundamental shift in what a single model can do. Rather than offering separate specialist models for coding, reasoning, and computer use, GPT 5.4 rolls everything into one unified architecture. It scores 57.7% on SWE-bench Pro (coding), 75% on OSWorld (computer use), and 83% on GDPval (knowledge work) -- making it the first model that credibly handles all three domains at frontier level.

The headline number: GPT 5.4's 75% OSWorld score surpasses the human expert baseline of 72.4%. No other model has crossed that threshold. Whether you are a developer, a business operator, or a researcher evaluating your next AI stack, this guide covers everything you need to know -- benchmarks, pricing, API details, model variants, and honest limitations.

What Is GPT 5.4?

GPT 5.4 is OpenAI's flagship model family, announced March 5, 2026. It is the first mainline model to incorporate GPT-5.3-Codex's frontier coding capabilities directly, meaning you no longer need a separate code-specialist model to get top-tier programming performance.

Why GPT 5.4 Matters

Previous OpenAI releases forced tradeoffs. GPT-5.2 was strong at general reasoning but lagged on coding. GPT-5.3-Codex was a coding powerhouse but narrowly focused. GPT 5.4 eliminates that choice by combining coding, computer use, and knowledge work in one model -- at a lower price than either predecessor.

Key context for the release:

GPT-5.2 is being retired June 5, 2026 -- GPT 5.4 is its designated replacement
GPT-5.3-Codex is being phased out -- its capabilities are now absorbed into GPT 5.4 Standard
33% fewer factual errors than GPT-5.2, according to OpenAI's claim accuracy testing
Tool Search , a new API feature, dramatically reduces token usage for tool-heavy agent workflows

OpenAI describes GPT 5.4 as the model where coding, computer use, and knowledge work "come together in a single system for the first time" -- and the benchmarks largely support that claim.

GPT 5.4 Model Variants

GPT 5.4 ships in five variants. Choosing the right one depends on your workload, budget, and latency requirements.

Variant Comparison Table

Variant| Best For| Input / Output (per MTok)| Context| Key Stat

GPT-5.4 Standard

The default and most widely used variant. At $2.50 per million input tokens and $15 per million output tokens , it offers frontier performance at a price point that undercuts most competitors. The 272K standard context window expands to 1M tokens via the API and Codex, though input pricing doubles to $5.00/MTok above the 272K mark.

This is the variant most developers should start with. It handles coding, general Q&A, document analysis, and computer use natively.

GPT-5.4 Thinking

The reasoning-enhanced variant uses advanced chain-of-thought processing. What makes it unique is Interactive Thinking : the model shows you an upfront plan of its approach and lets you adjust course mid-response. Instead of waiting for a full answer and hoping the model interpreted your intent correctly, you can steer it while it works.

ChatGPT Plus users get 80 Thinking messages per 3-hour window. ChatGPT Pro users get higher limits. Thinking is particularly valuable for complex math problems, multi-step scientific reasoning, and architectural coding decisions where the approach matters as much as the output.

GPT-5.4 Pro

The premium tier at $30/$180 per million tokens -- 12x the cost of Standard. Pro is designed for tasks where accuracy and depth justify the premium: legal analysis, medical reasoning, complex financial modeling, and enterprise-grade code generation.

ChatGPT Pro subscribers ($200/month) get a dedicated GPU slice for GPT-5.4 Pro, meaning no shared-compute latency spikes. This is the only variant with guaranteed compute allocation.

GPT-5.4 Mini

Released March 17, 2026 -- twelve days after the main launch. Mini scores 54.38% on SWE-bench Pro , which is remarkably close to Standard's 57.7%, at roughly 6x lower cost (~$0.40/$1.60 per MTok). For teams running high-volume, latency-sensitive workloads -- chat support, content generation, lightweight code completion -- Mini is the clear choice.

Free-tier ChatGPT users get limited access to GPT-5.4 Mini.

GPT-5.4 Nano

Also released March 17, Nano is the smallest variant, designed for edge and embedded use cases. Think on-device mobile assistants, IoT applications, and scenarios where network latency or bandwidth makes API calls impractical. Detailed benchmarks and pricing are still rolling out.

Benchmark Performance

Comprehensive Comparison

Benchmark| GPT-5.4| GPT-5.3 Codex| GPT-5.2| Claude Opus 4.6

SWE-bench Pro| 57.7%| 55.6%| ~48%| N/A SWE-bench Verified| ~80%| ~80%| ~72%| 80.8%OSWorld (Computer Use)| 75%| 64%| 47.3%| 72.5% GDPval (Knowledge Work)| 83%| N/A| ~70%| N/A Claim Accuracy vs 5.2| 33% fewer errors| --| baseline| -- Human Expert (OSWorld)| --| --| --| 72.4%

Sources: OpenAI announcement, Build Fast with AI benchmarks review, Digital Applied analysis.

What the Benchmarks Mean in Practice

SWE-bench Pro (57.7%) -- This is the harder, less gameable variant of SWE-bench that tests real-world software engineering on novel codebases. GPT 5.4's 57.7% represents a 2.1-point improvement over GPT-5.3-Codex (55.6%) and a roughly 10-point jump over GPT-5.2 (~48%). This is the benchmark that best predicts how well a model handles unfamiliar code.

SWE-bench Verified (~80%) -- On the standard version, GPT 5.4 matches GPT-5.3-Codex at approximately 80%, while Claude Opus 4.6 holds a slight edge at 80.8%. The gap is narrow enough that real-world performance depends more on your prompting strategy than on raw model capability.

OSWorld (75%) -- The standout number. OSWorld tests desktop automation: clicking buttons, filling forms, navigating file systems, using web browsers. GPT 5.4 scores 75%, exceeding the 72.4% human expert baseline -- a threshold no other model has crossed. Claude Opus 4.6 is close at 72.5%, but GPT 5.4's 2.5-point lead is meaningful for production automation workflows.

GDPval (83%) -- This knowledge-work benchmark tests research, analysis, summarization, and synthesis tasks. GPT 5.4's 83% is a 13-point improvement over GPT-5.2's ~70%. No competing model has published comparable GDPval numbers, making direct comparison difficult, but the improvement from GPT-5.2 is clear.

Claim Accuracy -- OpenAI reports 33% fewer factual errors compared to GPT-5.2. While self-reported metrics deserve scrutiny, independent reviewers at Interconnects and The Zvi have noted measurable improvements in factual reliability during real-world testing.

Key Features Deep Dive

1M Token Context Window

GPT 5.4 supports up to 1 million tokens of input context via the API and Codex, with a 128K token maximum output. The standard context window is 272K tokens -- anything beyond that triggers a pricing surcharge (input cost doubles from $2.50 to $5.00 per MTok).

What 1M tokens means in practice:

An entire medium-sized codebase (50,000+ lines) in one prompt
15-20 full-length research papers analyzed simultaneously
Complete legal contracts with all appendices and referenced documents
Multi-quarter financial reports with supporting data

The tradeoff is cost. Processing a full 1M-token prompt costs approximately $5.00 just for input (since tokens above 272K are billed at $5.00/MTok). For most workloads, staying within the 272K standard window and using strategic document chunking is more cost-effective.

Native Computer Use

GPT 5.4's 75% OSWorld accuracy makes it the first AI model to exceed human expert performance on desktop automation tasks. This is not a plugin or third-party integration -- computer use is built directly into the model.

Capabilities include:

Controlling desktop applications (spreadsheets, text editors, design tools)
Browsing the web and extracting information from dynamic pages
Filling out forms and navigating multi-step workflows
Managing file systems, including creating, moving, and editing files
Interacting with terminal/command-line interfaces

The improvement trajectory is striking : GPT-5.2 scored 47.3% on OSWorld, GPT-5.3-Codex reached 64%, and GPT 5.4 jumps to 75%. That is a 28-point improvement in approximately nine months.

For developers building AI agents that interact with desktop environments, GPT 5.4 is now the strongest available backbone. Applying AI notes that this capability "propels AI agents forward" by eliminating the need for custom screen-scraping and UI automation pipelines.

Tool Search

Tool Search is a new API feature that addresses one of the biggest cost problems in agentic AI: tool descriptions consuming tokens. In typical agent setups, you pass descriptions of every available tool (API endpoints, function signatures, parameter lists) in the system prompt. For agents with dozens of tools, this can consume tens of thousands of tokens per request -- before the model even starts working.

Tool Search solves this by giving the model a lightweight list of tool names. The model then looks up full definitions on-demand, only loading the tools it actually needs. According to OpenAI's developer documentation, this dramatically reduces token usage for tool-heavy workflows -- often by 50% or more.

This matters for production systems where you are paying per token and running thousands of agent requests daily. A 50% reduction in system-prompt tokens translates directly to a 50% reduction in input costs for that portion of the request.

Interactive Thinking

Available in the GPT-5.4 Thinking variant, Interactive Thinking changes how you interact with reasoning models. Instead of the model producing a complete chain-of-thought silently and then delivering a final answer, it shows you an upfront plan and lets you adjust course mid-response.

Practical example : You ask the model to debug a complex race condition in a distributed system. The model starts by outlining its plan: "I'll first check the lock ordering, then examine the message queue consumer, then trace the timeout handler." If you see it heading in the wrong direction -- say, the issue is clearly in the consumer -- you can redirect it before it wastes tokens analyzing the lock ordering.

This is especially valuable for expensive, long-running reasoning tasks where the first attempt might explore the wrong hypothesis. Rather than regenerating an entire response, you course-correct in real time.

Codex Integration

GPT-5.3-Codex was OpenAI's dedicated coding model, and its capabilities are now fully absorbed into GPT 5.4. This means:

No separate model needed for coding -- Standard handles it
SWE-bench Pro improved from 55.6% to 57.7% even after integrating into a general-purpose model
Terminal and agentic coding workflows transfer directly
GPT-5.3-Codex at $1.25/$10 per MTok is being phased out in favor of GPT 5.4 at $2.50/$15 -- a price increase, but with significantly broader capabilities

Interconnects describes this as "a big step for Codex" -- the coding specialization is no longer siloed but integrated into the model that also handles computer use and knowledge work.

GPT-5.4 Spark

Spark is the real-time streaming variant, delivering 1,000+ tokens per second for latency-sensitive applications. This makes it suitable for:

Live conversational interfaces that feel instantaneous
Streaming code completion in IDEs
Real-time content generation for interactive applications
Voice-to-text-to-response pipelines where latency matters

Spark trades some quality for speed. It is not the variant you would choose for complex reasoning or high-stakes code generation, but for applications where responsiveness is the primary concern, it fills a clear gap.

Pricing: Every Plan and API Tier

ChatGPT Subscription Plans

Plan| Monthly Cost| GPT 5.4 Access| Key Limits

Source: ChatGPT pricing page.

API Pricing

Model| Input (per MTok)| Output (per MTok)| Notes

GPT-5.4 Standard| $2.50| $15.00| 272K context GPT-5.4 ( >272K context)| $5.00| $15.00| Extended context surcharge GPT-5.4 Pro| $30.00| $180.00| Premium reasoning GPT-5.4 Mini| ~$0.40| ~$1.60| Budget option GPT-5.3-Codex| $1.25| $10.00| Being phased out

Source: OpenAI API pricing.

Hidden Costs and Gotchas

Context surcharge : The most common surprise. Standard context is 272K tokens. Any input tokens above that mark are billed at $5.00/MTok instead of $2.50 -- a 2x multiplier. If you are routinely pushing past 272K, your effective input cost is significantly higher than the headline price.

Plus plan message limits : The 80 Thinking messages per 3-hour window sounds generous, but heavy users burn through it quickly during complex debugging sessions. Once you hit the cap, you wait or switch to Standard (which has no Thinking capability). There is no way to buy additional Thinking messages a la carte.

Pro pricing is steep for API : At $30/$180 per MTok, GPT-5.4 Pro costs 12x more than Standard on input and 12x more on output. Unless your use case genuinely requires the premium tier's depth, Standard handles the vast majority of tasks.

GPT-5.2 retirement : Scheduled for June 5, 2026. If your application depends on GPT-5.2, you have roughly two months to migrate. Test GPT 5.4 Standard as a drop-in replacement now -- most workloads will see improved results.

Cost Optimization Tips

Stay under 272K tokens whenever possible. Chunk large documents instead of sending the full 1M context.
Use GPT-5.4 Mini for high-volume, low-complexity tasks -- at ~$0.40/$1.60, it is 6x cheaper than Standard with 94% of the coding performance (54.38% vs 57.7% SWE-bench Pro).
Use Tool Search for agent workflows. Reducing tool descriptions from the system prompt can cut input tokens by 50%+.
Reserve Pro for tasks that justify the 12x premium -- legal review, medical analysis, complex financial modeling.
Cache results aggressively. The 1M context window is powerful but expensive. If you are re-analyzing the same codebase, cache intermediate results instead of reprocessing.

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

Upgrade Decision Guide

Dimension| GPT 5.4| GPT-5.3-Codex| GPT-5.2

If you are on GPT-5.2 : Upgrade immediately. GPT 5.4 is better on every benchmark at the same price. GPT-5.2 retires June 5, 2026 -- do not wait.

If you are on GPT-5.3-Codex : GPT 5.4 is the natural successor. You get slightly better coding (57.7% vs 55.6% SWE-bench Pro), dramatically better computer use (75% vs 64%), plus knowledge work capabilities Codex never had. The tradeoff is price: $2.50/$15 vs $1.25/$10. For most developers, the broader capabilities justify the increase.

Who Should Stick with GPT-5.3-Codex

There are legitimate reasons to stay on Codex for now:

Pure coding workloads at high volume : If you are running thousands of code-generation requests daily and never need computer use or knowledge work, Codex's $1.25/$10 pricing is half the cost. The 2.1-point SWE-bench Pro gap may not justify 2x higher input costs for your specific use case.
Existing Codex-tuned pipelines : If you have extensively prompt-engineered for Codex's behavior patterns, migration requires testing. GPT 5.4 handles the same tasks but may respond differently to Codex-optimized prompts.
Budget constraints : For startups running tight on API spend, staying on Codex until it is officially deprecated buys time to optimize your GPT 5.4 integration.

However, Codex is being phased out. Building new projects on Codex at this point is not advisable. Plan your migration now and execute it before deprecation.

GPT 5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro

Competitor Comparison

Dimension| GPT 5.4| Claude Opus 4.6| Gemini 3.1 Pro

SWE-bench Verified| ~80%| 80.8%| 80.6% SWE-bench Pro| 57.7%| N/A| N/A OSWorld| 75%| 72.5%| N/A GDPval| 83%| N/A| N/A GPQA Diamond| N/A| 91.3%| 94.3% ARC-AGI-2| N/A| N/A| 77.1%Context Window| 1M (API)| 200K (1M beta)| 1MMax Output| 128K| 64K| 64K Input Price (per MTok)| $2.50| $15.00| $2.00Output Price (per MTok)| $15.00| $75.00| $12.00Computer Use| 75% OSWorld| 72.5% OSWorld| N/A Consumer Plan| $20/mo (Plus)| $20/mo (Pro)| $19.99/mo (AI Pro)

Sources: OpenAI pricing, TechCrunch coverage, Turing College model comparison.

When to Choose Each

Choose GPT 5.4 when:

You need one model for coding, computer use, and knowledge work
Desktop/browser automation is a primary use case (75% OSWorld)
You want 128K output tokens for long-form generation
Tool Search matters for your agent architecture
Budget is moderate (cheaper than Opus, slightly more than Gemini)

Choose Claude Opus 4.6 when:

Deep multi-file code refactoring is your primary task
You need Agent Teams for parallel multi-agent workflows
PhD-level scientific reasoning matters (91.3% GPQA Diamond)
You prefer Anthropic's safety and instruction-following characteristics
You are already invested in the Claude Code or Claude ecosystem

Choose Gemini 3.1 Pro when:

Cost is the primary concern ($2/$12 per MTok -- cheapest of the three)
You need native 1M context without pricing surcharges
You are in the Google ecosystem (Vertex AI, Android Studio, Cloud)
Configurable thinking levels for cost-quality tradeoffs matter
ARC-AGI-2 performance (77.1%) is relevant to your reasoning tasks

Practical Use Cases

Software Development

GPT 5.4 Standard handles most coding tasks a professional developer encounters. With 57.7% SWE-bench Pro and the absorbed Codex capabilities, it can:

Generate full-stack application scaffolding
Debug complex issues across multi-file codebases (with 1M context)
Write and maintain test suites
Perform code reviews with contextual understanding

For teams that want rapid prototyping without hand-writing every line, GPT 5.4 is a strong backbone. If you would rather build apps visually instead of writing prompts, NxCode lets you describe your idea and ship a working application -- powered by GPT, Claude, and other frontier models under the hood.

Recommended variant : Standard for most tasks. Thinking for architectural decisions and complex debugging. Mini for high-volume code completion.

Business Process Automation

The 75% OSWorld score makes GPT 5.4 the strongest model for automating desktop workflows. Real-world applications include:

Data entry automation : GPT 5.4 can navigate web forms, CRM interfaces, and spreadsheets to input data with higher accuracy than previous models
Report generation : Pull data from multiple sources, synthesize it, and produce formatted reports
Email and communication management : Draft, categorize, and respond to routine correspondence
Multi-application workflows : Chain actions across different desktop applications without custom integrations

Recommended variant : Standard for most automation. Pro for high-stakes workflows where errors are costly (financial data entry, compliance reporting).

Research and Analysis

The combination of 1M context, 83% GDPval, and 33% fewer factual errors makes GPT 5.4 particularly strong for research:

Literature reviews : Load 15-20 papers into context and synthesize findings
Patent analysis : Process entire patent families with supporting documentation
Market research : Analyze quarterly reports, earnings calls, and industry data in a single session
Legal document review : Compare contract versions, identify deviations, summarize terms

Recommended variant : Standard with extended context for document-heavy work. Thinking for analytical tasks requiring multi-step reasoning. Pro for legal and medical analysis where accuracy premium is justified.

OpenAI's Full Model Lineup (March 2026)

Model| Role| Status

For new projects, GPT-5.4 Standard is the default starting point. Use Mini for budget-sensitive high-volume work, Thinking for complex reasoning, and Pro only when the premium is justified.

Limitations and Honest Assessment

GPT 5.4 is impressive, but it is not without tradeoffs:

Context surcharge is real : The 1M window is powerful but expensive. Routine use above 272K tokens will significantly inflate your API bill.
Plus plan Thinking cap : 80 messages per 3 hours is constraining for power users during intensive work sessions.
Pro pricing is extreme : $30/$180 per MTok puts Pro out of reach for most individual developers and small teams.
Coding is not clearly ahead of Claude on Verified : At ~80% vs 80.8% SWE-bench Verified, Claude Opus 4.6 still has a slight edge on the standard coding benchmark.
GDPval and claim accuracy are self-reported : Independent verification is ongoing. Treat these numbers as directional, not absolute.
Nano and Spark details are sparse : Full benchmarks and pricing for the smallest and fastest variants are still rolling out.

The Bottom Line

GPT 5.4 is the most complete AI model available as of March 2026. No other single model combines frontier coding (57.7% SWE-bench Pro), superhuman computer use (75% OSWorld), and strong knowledge work (83% GDPval) at $2.50/$15 per million tokens. The five-variant lineup -- Standard, Thinking, Pro, Mini, Nano -- means there is a GPT 5.4 for virtually every budget and use case.

It does not win every benchmark. Claude Opus 4.6 still leads on SWE-bench Verified and PhD-level reasoning. Gemini 3.1 Pro is cheaper and matches on several benchmarks. But GPT 5.4's breadth is unmatched: if you need one model that does everything reasonably well to extremely well, this is it.

For developers still on GPT-5.2 or GPT-5.3-Codex, the migration path is clear. GPT-5.2 retires in June, Codex is being phased out, and GPT 5.4 is better at the tasks both models specialized in. Start testing now.

Sources

OpenAI: Introducing GPT 5.4 -- official announcement, benchmark claims, feature overview
OpenAI API Documentation: GPT-5.4 -- technical specifications, context window details, Tool Search
OpenAI API Pricing -- current pricing for all model variants
ChatGPT Pricing -- subscription plans and limits
TechCrunch: OpenAI launches GPT-5.4 -- launch coverage, Pro and Thinking variant details
DataCamp: GPT-5.4 Overview -- educational breakdown and practical guidance
Applying AI: GPT-5.4 Computer Use and Context Window -- agent implications, computer use analysis
Build Fast with AI: GPT-5.4 Benchmarks Review -- independent benchmark verification
Digital Applied: GPT-5.4 Computer Use, Tool Search, Benchmarks -- pricing and feature analysis
GLB GPT: GPT-5.4 Pricing Guide -- detailed pricing tiers and cost breakdowns
Get AI Perks: OpenAI Pricing -- subscription plan comparison
Turing College: GPT-5.4 vs GPT-5.3-Codex -- model comparison and upgrade analysis
Interconnects: GPT-5.4 Is a Big Step for Codex -- expert analysis of Codex integration
The Zvi: GPT-5.4 Is a Substantial Upgrade -- independent review and factual accuracy assessment
FluxHire: ChatGPT GPT-5.4 Mini and Nano Guide -- Mini and Nano variant details

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