---
title: "GPT-5.4 for Agents: Computer Use, MCP Tool Calls, and Real Pricing"
slug: "openai-gpt-5-4-complete-guide"
description: "GPT-5.4's native computer use and MCP tool calls are the real upgrade for agents. What holds up in a loop, what the 1M context costs, and how Pro compares."
publishDate: "2026-03-06"
updatedDate: "2026-08-06"
author: Umesh Malik
canonical: "https://umesh-malik.com/blog/openai-gpt-5-4-complete-guide"
category: "LLM Engineering"
tags:
- AI
- OpenAI
- ChatGPT
- GPT-5.4
- API
- AI Agents
- LLMs
- Machine Learning
keywords: "OpenAI GPT-5.4, GPT-5.4 complete guide, GPT-5.4 vs GPT-5.2, GPT-5.4 vs GPT-5.3-Codex, GPT-5.4 pricing, GPT-5.4 API, GPT-5.4 Pro, GPT-5.4 computer use, GPT-5.4 benchmarks, GPT-5.4 context window"
primaryKeyword: GPT-5.4
secondaryKeywords:
- OpenAI GPT-5.4
- GPT-5.4 pricing
- GPT-5.4 API
- GPT-5.4 vs GPT-5.2
- GPT-5.4 vs GPT-5.3-Codex
- GPT-5.4 Pro
- GPT-5.4 benchmarks
geoHooks:
- TL;DR
- GPT-5.4 benchmarks
- GPT-5.4 use cases
- GPT-5.4 vs GPT-5.4 Pro
- FAQ
image: "/blog/gpt-5-4-cover.svg"
imageAlt: "OpenAI GPT-5.4 overview showing professional work, coding, computer use, and 1M context"
featured: true
published: true
readingTime: "12 min read"
faq:
- q: "What is GPT-5.4 best at?"
a: "Complex professional workflows spanning analysis, coding, browser control, tool orchestration, and long document reasoning simultaneously."
- q: "Should I use GPT-5.4 or GPT-5.3-Codex for coding?"
a: "Use GPT-5.4 for coding mixed with analysis and tool orchestration. Use GPT-5.3-Codex for mostly pure coding inside an agent loop."
- q: "Is the 1M context window reliable for retrieval?"
a: "Not at the far end. Retrieval quality drops sharply past 256K tokens. At 512K-1M, success rates fall to 36.6%. Use it for synthesis, not precise far-edge retrieval."
- q: "How do I enable computer use in GPT-5.4?"
a: "Enable the computer tool in your API request, send a task with a screenshot, and the model returns actions for you to execute and feed back as updated screenshots."
---
OpenAI released **GPT-5.4 on March 5, 2026**, and this is the first GPT release in a while that feels less like a narrow benchmark bump and more like a model-line reset.
The reason is simple: **GPT-5.4 is the first mainline OpenAI reasoning model that combines frontier professional-work quality, frontier coding from GPT-5.3-Codex, native computer use, and 1.05M-context API support in the same default model.** That matters a lot if your real workload is not "one perfect answer in one shot," but messy multi-step work spread across documents, spreadsheets, web apps, codebases, and tool chains.
The short answer: **GPT-5.4 is now OpenAI's best all-around model for serious professional work.** If you need one model that can research, write, analyze, code, use tools, drive browsers, and survive large contexts, this is the new default. If you need the highest ceiling and can tolerate much higher latency and price, GPT-5.4 Pro is the step-up.
## TL;DR
- **GPT-5.4 launched on March 5, 2026** as OpenAI's new mainline reasoning model for professional work.
- OpenAI says it is the **first mainline reasoning model** to absorb the frontier coding capabilities of **GPT-5.3-Codex**.
- On **GDPval**, GPT-5.4 reaches **83.0%**, up from **70.9%** for GPT-5.2.
- On OpenAI's internal **investment banking modeling tasks**, GPT-5.4 scores **87.3%** versus **68.4%** for GPT-5.2.
- On **SWE-Bench Pro**, GPT-5.4 posts **57.7%**, slightly ahead of **GPT-5.3-Codex at 56.8%**.
- On **OSWorld-Verified**, GPT-5.4 hits **75.0%**, above **GPT-5.2 at 47.3%** and even above the human baseline OpenAI cites at **72.4%**.
- The API model supports a **1,050,000 token context window** and **128,000 max output tokens**, but benchmark results show quality still drops sharply at the far end of that window.
- **GPT-5.4 costs more per token than GPT-5.2**: `$2.50` input, `$0.25` cached input, and `$15.00` output per 1M tokens.
- **GPT-5.4 Pro** costs much more at `$30` input and `$180` output per 1M tokens, and is for the hardest tasks only.
- In ChatGPT, **GPT-5.4 Thinking replaces GPT-5.2 Thinking** for Plus, Team, and Pro users. **GPT-5.2 Thinking retires on June 5, 2026**.
## What GPT-5.4 Actually Is
OpenAI's own positioning is unusually clear here.
GPT-5.4 is:
- the new default frontier model for **complex professional work**
- the first mainline reasoning model that **inherits GPT-5.3-Codex-level coding ambition**
- OpenAI's first **general-purpose model with native computer use**
- a model with **1.05M context** in the API and experimental 1M-context support in Codex
- a model that supports the full modern agent stack: **web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search**
That last point is the real story.
Previous OpenAI model choices were easier to split into buckets:
- use the reasoning model for analysis
- use the coding model for coding
- use special tools for browser or desktop automation
GPT-5.4 makes those boundaries much blurrier.
OpenAI says GPT-5.4 is the first mainline reasoning model that incorporates the frontier coding capabilities of GPT-5.3-Codex. That is why this release is named GPT-5.4 instead of staying on the GPT-5.2 line with another minor update.
## 1. Professional Work Is the Real Headline
Most model launches still center on coding, math, or abstract reasoning. GPT-5.4 is different. OpenAI's release materials repeatedly frame it around **real office work**: spreadsheets, presentations, documents, legal analysis, and research-heavy deliverables.
That is not marketing fluff. The public numbers back it up.
This is where GPT-5.4 becomes more than a "better chatbot."
It is now credible for:
- board update outlines and narrative memos
- spreadsheet modeling and sanity-checking
- presentation draft generation with stronger visual variety
- long document comparison and synthesis
- contract-heavy diligence work
- finance, strategy, and operations research that needs both writing and structured reasoning
OpenAI also says human raters preferred GPT-5.4-generated presentations **68.0% of the time over GPT-5.2** due to stronger aesthetics, more visual variety, and better use of image generation.
That matters because a lot of "knowledge work" is not just about factual recall. It is about **producing work products that look usable**.
## 2. GPT-5.4 Turns Coding Into a First-Class Default Capability
The coding section is where this launch gets more subtle.
OpenAI says GPT-5.4 combines the coding strengths of GPT-5.3-Codex with leading knowledge-work and computer-use capabilities, especially for longer-running tasks where the model can use tools, iterate, and keep pushing with less manual intervention.
For a concrete look at what Codex-class coding does when pointed at real UI work, I walked through [building frontend UIs with Codex and Figma](/blog/figma-codex-react-2026) — the same iterate-with-tools loop, applied to design-to-code.
The official comparison table supports that claim, but with nuance.
Here is the practical read:
- **GPT-5.4 is now the best default if your coding work is mixed with analysis, docs, browser steps, and tool orchestration.**
- **GPT-5.3-Codex remains very relevant if your workload is mostly pure coding inside a Codex-style environment.**
- **GPT-5.2 is now mostly a legacy comparison target.**
That second point is my inference from OpenAI's own tables. GPT-5.4 edges GPT-5.3-Codex on SWE-Bench Pro, but GPT-5.3-Codex still leads on Terminal-Bench 2.0. So the cleaner way to think about this is:
- GPT-5.4 = strongest all-around engineering model
- GPT-5.3-Codex = still a very sharp specialist for terminal-heavy coding loops
If your task is not just "write code," but "understand the repo, search docs, inspect a browser, edit files, and finish the workflow," GPT-5.4 is the better strategic default. If the task lives almost entirely inside a coding agent loop, GPT-5.3-Codex may still be the tighter fit in some environments.
## 3. Native Computer Use Is One of the Biggest Practical Upgrades
This is the part many people will underrate at first.
OpenAI calls GPT-5.4 its **first general-purpose model with native computer-use capabilities**. That is a big shift because it means the mainline reasoning model can now operate on screenshots, return UI actions, and participate directly in browser or desktop workflows.
The benchmark jump is not small.
OpenAI's docs describe three practical ways to use this capability:
- a built-in `computer` tool loop for screenshot-based UI actions
- a custom browser or VM harness with Playwright, Selenium, VNC, or MCP
- a code-execution harness where the model writes and runs scripts for UI work
That opens up a long list of real product use cases:
- browser QA and acceptance testing
- reproducing UI bugs from screenshots or step lists
- support workflows across admin panels and dashboards
- CRM or ERP task automation that still needs human supervision
- accessibility and regression walkthroughs
- research agents that move between tabs, forms, downloads, and screenshots
The built-in loop is also straightforward. OpenAI's computer-use docs describe it as:
1. send a task with the `computer` tool enabled
2. inspect the returned `computer_call`
3. execute the returned actions in order
4. send back an updated screenshot as `computer_call_output`
5. repeat until the model stops asking for computer actions
### Minimal computer-use example
```ts
import OpenAI from 'openai';
const client = new OpenAI();
const response = await client.responses.create({
model: 'gpt-5.4',
tools: [{ type: 'computer' }],
input:
'Check whether the Filters panel is open. If it is not open, click Show filters. Then type penguin in the search box. Use the computer tool for UI interaction.'
});
console.log(response.output);
```
OpenAI's computer-use guide explicitly says confirmation policy should be part of product design, especially for actions like posting, sending data, deleting information, confirming financial actions, or following suspicious on-screen instructions. Treat computer use like a privileged workflow, not a novelty demo.
## 4. Tool Use and MCP Workloads Are Where GPT-5.4 Starts Feeling Like an Agent Model
GPT-5.4 is not just stronger at single-model reasoning. It is stronger at **deciding what tools to call and when**.
OpenAI's official evals show:
- **82.7% on BrowseComp** for GPT-5.4
- **89.3% on BrowseComp** for GPT-5.4 Pro
- **67.2% on MCP Atlas** for GPT-5.4
- **54.6% on Toolathlon** for GPT-5.4
- **98.9% on Tau2-bench Telecom** for GPT-5.4
That matters for teams building agents across big internal tool surfaces.
The most interesting supporting feature here is **tool search**.
According to OpenAI's tool-search docs, tool search lets the model dynamically search for and load tools into the context only when needed. The point is not just convenience. It can reduce token usage, preserve the model cache better, and avoid dumping a huge tool catalog into the prompt up front.
That is especially useful when you have:
- large internal tool catalogs
- namespaced function sets
- tenant-specific tool inventories
- MCP servers with many functions
- agent systems where most tools are irrelevant on most turns
### Minimal tool-search pattern
```ts
const response = await client.responses.create({
model: 'gpt-5.4',
input: 'List open orders for customer CUST-12345.',
tools: [crmNamespace, { type: 'tool_search' }],
parallel_tool_calls: false
});
```
In OpenAI's docs, the deferred tools live inside a namespace or MCP server and are loaded only when the model decides it needs them.
That is a major design improvement for enterprise agents because it moves you away from the old pattern of shoving 50 JSON schemas into every request.
## 5. The 1M Context Window Is Real, but It Is Not Magic
This is one of the most important practical caveats in the whole release.
Yes, GPT-5.4 supports a **1,050,000 token context window** in the API, with **128,000 max output tokens**. OpenAI also says GPT-5.4 in Codex has experimental support for the 1M window, and requests above the standard **272K** context threshold incur higher usage rates.
But you should not read "1M context" as "perfect 1M recall."
OpenAI's own long-context evals show a very clear pattern:
Another important API detail from OpenAI's reasoning docs: reasoning tokens are not visible in the raw response, but they still take up space inside the context window and are billed as output tokens. OpenAI recommends leaving at least **25,000 tokens** of headroom for reasoning and outputs while you are learning how your prompts behave.
That is an easy thing to miss, and it will absolutely affect real cost and truncation behavior.
## 6. Steerability Finally Feels Productive Instead of Cosmetic
OpenAI also improved the actual ChatGPT interaction pattern around GPT-5.4 Thinking.
For longer and more complex prompts, the model now gives a **preamble** describing how it plans to approach the task. Users can also redirect it mid-response without fully restarting.
This sounds small, but it is a real usability upgrade for messy work:
- "keep the thesis but make the deck more investor-facing"
- "same structure, less legal language"
- "stop summarizing and switch into recommendation mode"
- "use the spreadsheet, not the PDF, as the source of truth"
That is the kind of interaction pattern that makes a reasoning model more practical for long professional workflows.
## Every Practical Use Case Where GPT-5.4 Makes Sense
If you want the simplest high-level rule, it is this:
**GPT-5.4 is strongest when the task spans multiple modes of work at once.**
Not just writing.
Not just coding.
Not just tool calling.
Not just browser control.
All of them together.
## GPT-5.4 vs GPT-5.4 Pro vs GPT-5.3-Codex vs GPT-5.2
If you are choosing inside the current OpenAI lineup, this is the comparison that matters most.
### The simplest decision rule
- Choose **GPT-5.4** if you want the new default and your work spans multiple task types.
- Choose **GPT-5.4 Pro** if the task is hard enough that extra minutes and extra money are justified.
- Choose **GPT-5.3-Codex** if you are optimizing mostly for coding-agent behavior.
- Keep **GPT-5.2** only for regression testing, temporary fallbacks, or side-by-side migration checks.
## How To Use GPT-5.4 Well in the API
The model is strong, but the implementation details still matter.
### Use background mode for long tasks
OpenAI explicitly recommends background mode for GPT-5.4 Pro because hard tasks can take several minutes.
```ts
import OpenAI from 'openai';
const client = new OpenAI();
let resp = await client.responses.create({
model: 'gpt-5.4-pro',
input: 'Analyze these diligence memos and produce a ranked acquisition recommendation.',
background: true
});
while (resp.status === 'queued' || resp.status === 'in_progress') {
await new Promise((resolve) => setTimeout(resolve, 2000));
resp = await client.responses.retrieve(resp.id);
}
console.log(resp.output_text);
```
One detail that matters for enterprise teams: OpenAI's background-mode docs say background mode stores response data for roughly 10 minutes to enable polling, so it is **not Zero Data Retention compatible**.
## Pricing, Rollout, and Migration Details
Here are the exact release mechanics that matter.
### Availability
- In the API, GPT-5.4 is available as **`gpt-5.4`**.
- In the API, GPT-5.4 Pro is available as **`gpt-5.4-pro`**.
- In ChatGPT, GPT-5.4 Thinking started rolling out on **March 5, 2026** to **Plus, Team, and Pro** users.
- **Enterprise and Edu** can enable early access through admin settings.
- **GPT-5.4 Pro** is available to **Pro and Enterprise** plans.
- GPT-5.2 Thinking remains for paid users in the Legacy Models section until **June 5, 2026**.
### Pricing
For GPT-5.4:
- `$2.50` input / 1M tokens
- `$0.25` cached input / 1M tokens
- `$15.00` output / 1M tokens
For GPT-5.4 Pro:
- `$30.00` input / 1M tokens
- `$180.00` output / 1M tokens
OpenAI also says:
- Batch and Flex pricing are available at **half** the standard rate
- Priority processing is available at **2x** the standard rate
- prompts above **272K** input tokens on GPT-5.4 and GPT-5.4 Pro are billed at **2x input** and **1.5x output** for the full session
- regional processing endpoints add a **10% uplift** for GPT-5.4 and GPT-5.4 Pro
## What GPT-5.4 Still Does Not Solve
This release is strong, but teams will make mistakes if they read only the headline and skip the tradeoffs.
### 1. The knowledge cutoff is still August 31, 2025
GPT-5.4 is better at professional work, but it still needs web search for truly current facts. If you ask it about fast-moving topics without web access, you are still leaning on a pre-September-2025 internal cutoff.
### 2. 1M context does not remove retrieval discipline
OpenAI's own MRCR and Graphwalks numbers show that extremely large-context retrieval remains meaningfully weaker than short- and mid-context performance.
### 3. It is text output only
GPT-5.4 accepts text and image inputs, but outputs text. Audio and video are not supported on the model page.
### 4. GPT-5.4 Pro is not a universal upgrade
Pro gives you a higher performance ceiling, but it drops some useful platform features:
- no structured outputs
- no distillation
- no code interpreter
- no hosted shell
- no skills
So even though Pro is stronger on some benchmarks, the default GPT-5.4 model may be the better product fit.
### 5. Computer use still needs product-level safeguards
A model that can click, type, and navigate is powerful. It is also a bigger operational and safety surface. Human confirmation, scope limits, logging, and tool-specific permissions matter more, not less.
### 6. Safety controls can still create false positives
OpenAI says GPT-5.4 is treated as **High cyber capability** under its Preparedness Framework, with monitoring, trusted access controls, and asynchronous blocking for certain higher-risk requests on Zero Data Retention surfaces. That is sensible, but it also means some production setups should still expect friction and false positives in higher-risk domains.
## FAQ
## Final Take
The most important thing to understand about GPT-5.4 is that it is not just "GPT-5.2 but better."
It is OpenAI's attempt to collapse several previously separate model choices into one serious default:
- office-work reasoning
- coding
- browser and desktop interaction
- tool-heavy orchestration
- large-context analysis
That is a more important shift than a single benchmark number.
If you build products where users need actual work done, not just polished chat responses, **GPT-5.4 is the new model to evaluate first**. If your task is expensive enough that every extra point of accuracy matters, evaluate **GPT-5.4 Pro** too. But do it with clean eyes: measure cost, latency, long-context failure modes, structured-output needs, and safety friction before you roll it into production.
The labs are now competing on who can finish longer workflows with less supervision.
GPT-5.4 is OpenAI's strongest evidence yet that this is the product battle that matters.
## Sources
- [OpenAI: Introducing GPT-5.4](https://openai.com/index/introducing-gpt-5-4/)
- [OpenAI API model page: GPT-5.4](https://developers.openai.com/api/docs/models/gpt-5.4)
- [OpenAI API model page: GPT-5.4 Pro](https://developers.openai.com/api/docs/models/gpt-5.4-pro)
- [OpenAI API model page: GPT-5.3-Codex](https://developers.openai.com/api/docs/models/gpt-5.3-codex)
- [OpenAI API model page: GPT-5.2](https://developers.openai.com/api/docs/models/gpt-5.2)
- [OpenAI API guide: Reasoning models](https://developers.openai.com/api/docs/guides/reasoning)
- [OpenAI API guide: Computer use](https://developers.openai.com/api/docs/guides/tools-computer-use)
- [OpenAI API guide: Tool search](https://developers.openai.com/api/docs/guides/tools-tool-search)
- [OpenAI API guide: Background mode](https://developers.openai.com/api/docs/guides/background)
**Explore more:** [LLM Engineering — RAG, Fine-Tuning & Production LLMs](/topics/llm-engineering)
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Umesh Malik is a software engineer specialising in AI engineering: RAG pipelines, agentic workflows, and Model Context Protocol servers. He works as a Software Development Engineer 2 at Expedia Group on workflow orchestration infrastructure, has 5+ years of production engineering experience across Expedia Group, Tekion Corp and BYJU'S, and writes a technical blog on LLM engineering, Claude Code and the agentic web at umesh-malik.com.
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| --- | --- | --- |
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