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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →OpenAI announced GPT-5.4 on March 5, 2026, positioning it as its most capable and efficient model for professional work at launch. It combines the coding capabilities of GPT-5.3-Codex with broader reasoning, spreadsheet and presentation work, long-document analysis, tool orchestration, and native computer use across ChatGPT, the API, and Codex.
That launch claim needs a date: reports say OpenAI released GPT-5.6 in July 2026, so GPT-5.4 should not be described as the company’s current top model without qualification. Its more durable significance is as a workflow model—one intended to gather information, use software, create an artifact, check it, and revise it.
What OpenAI announced
GPT-5.4 launched in three related forms:
- GPT-5.4 Thinking: the reasoning-oriented ChatGPT option.
gpt-5.4: the standard API model and a model available in Codex.gpt-5.4-pro: a higher-priced API and ChatGPT Pro option for especially difficult work.
OpenAI says the name reflects a consolidation of its mainline reasoning and GPT-5.3-Codex coding capabilities, simplifying model selection in Codex. The announcement is documented in OpenAI’s launch post.
Launch-period ChatGPT access included Thinking for Plus, Team, and Pro users, with Enterprise and Edu administrators able to enable early access. Pro was offered to Pro and Enterprise users. Those were launch arrangements, not a guarantee of the model picker or plan access in August 2026; current availability can vary by plan and region.
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Why this is a professional-work model
GPT-5.4 is aimed at completed, multi-stage deliverables rather than isolated answers. OpenAI’s examples include:
- financial models and accounting spreadsheets;
- sales presentations and other slide decks;
- legal analysis and long documents;
- software development, debugging, testing, and iteration;
- browser and desktop tasks; and
- agents that collect context, call tools, verify results, and produce a finished file.
OpenAI’s GDPval evaluation covers work products across 44 occupations in the nine industries contributing most to U.S. GDP. Examples include sales presentations, accounting spreadsheets, urgent-care schedules, manufacturing diagrams, and short videos. A high score means the output compared favorably in that evaluation; it does not mean the model can independently perform every job, accept legal or fiduciary responsibility, or replace professional review.
The largest technical changes
Native computer use
GPT-5.4 is OpenAI’s first general-purpose model with native computer-use capabilities. Through screenshots, keyboard and mouse actions, and libraries such as Playwright, an agent can operate software rather than merely describe what a person should click. Developers can set confirmation policies for risky actions. This matters for tasks such as moving data between applications, preparing a spreadsheet, or testing a web workflow—but an incorrect action can also edit a file, submit a form, or trigger a transaction.
Tool Search
Tool Search lets an agent find relevant tool definitions when needed instead of placing every available function in the prompt. In systems with many connectors, that can reduce context overhead and make tool-enabled agents easier to scale. It does not remove the need to design permissions, validate arguments, and log actions.
A 1.05-million-token context window
The API model page lists a 1,050,000-token context window, a maximum output of 128,000 tokens, and an August 31, 2025 knowledge cutoff: API model documentation. A million-token maximum is not a promise that every detail will be retrieved or reasoned about perfectly. OpenAI reports weaker results on some graph-reasoning tests when context expands from 256K toward 1M tokens.
There is also a cost and engineering trade-off. Inputs above 272,000 tokens are charged at twice the normal input rate and 1.5 times the normal output rate for the full session under standard, batch, and flex pricing. Indexing, retrieval, chunking, citations, and verification remain useful even with a very large context.
Reasoning controls
The API supports none (the default), low, medium, high, and xhigh reasoning effort. Higher effort can suit difficult analysis but may increase latency and token use. Choose the lowest setting that meets the quality requirement rather than paying maximum reasoning cost for routine extraction.
How much better is GPT-5.4?
The following are results reported by OpenAI in its launch evaluation, not independent universal rankings. Test conditions, tools, scaffolding, image resolution, and reasoning settings can differ.
| Evaluation | GPT-5.4 | GPT-5.3-Codex | GPT-5.2 |
|---|---|---|---|
| GDPval, wins or ties | 83.0% | 70.9% | 70.9% |
| SWE-Bench Pro | 57.7% | 56.8% | 55.6% |
| OSWorld-Verified | 75.0% | 74.0% | 47.3% |
| Toolathlon | 54.6% | 51.9% | 46.3% |
| BrowseComp | 82.7% | 77.3% | 65.8% |
OpenAI also reports an 87.3% mean score on an internal investment-banking modeling benchmark versus 68.4% for GPT-5.2, a 68.0% human preference rate for GPT-5.4 presentations over GPT-5.2 presentations, and OfficeQA results of 68.1% versus 63.1%. These are vendor-reported evaluations, not guarantees for a particular company’s data or workflow.
The results are uneven. On FinanceAgent v1.1, GPT-5.2 scored 59.5%, GPT-5.4 56.0%, and GPT-5.3-Codex 54.0% in OpenAI’s table. GPT-5.3-Codex also led GPT-5.4 on Terminal-Bench 2.0, 77.3% to 75.1%.
Computer-use and web results
- OSWorld-Verified: 75.0% for GPT-5.4 versus 47.3% for GPT-5.2.
- WebArena-Verified: 67.3% versus 65.4%.
- Online-Mind2Web: 92.8% with screenshot-only observations, versus 70.9% for ChatGPT Atlas Agent Mode.
OpenAI reports a 72.4% human comparison figure for OSWorld, but benchmark human baselines and model test conditions are not necessarily equivalent to professional human performance.
Coding and speed
GPT-5.4’s SWE-Bench Pro result is slightly above GPT-5.3-Codex, while Codex remains stronger on Terminal-Bench 2.0. OpenAI says GPT-5.4 can have lower latency than GPT-5.3-Codex at comparable reasoning efforts and claims Codex /fast mode can provide up to 1.5 times faster token velocity. Those are OpenAI’s measurements, not a universal speed guarantee.
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Accuracy improvements do not remove verification
Compared with GPT-5.2, OpenAI says individual claims in its internal, de-identified, user-error-flagged evaluation were 33% less likely to be false and complete responses were 18% less likely to contain errors. The figures describe a measured comparison, not a factuality guarantee.
For financial, legal, medical, tax, compliance, and operational work, verify calculations and source claims independently. Connect the model to current data when needed: the API’s listed knowledge cutoff is August 31, 2025, so GPT-5.4 is not automatically aware of events after that date.
GPT-5.4 versus GPT-5.4 Pro
Pro is not simply a universal “better” score. OpenAI’s published results show it ahead on some demanding tests and behind standard GPT-5.4 on others.
| Evaluation | GPT-5.4 | GPT-5.4 Pro |
|---|---|---|
| GDPval | 83.0% | 82.0% |
| FinanceAgent v1.1 | 56.0% | 61.5% |
| Investment-banking modeling | 87.3% | 83.6% |
| BrowseComp | 82.7% | 89.3% |
| FrontierMath Tier 4 | 27.1% | 38.0% |
| ARC-AGI-2 | 73.3% | 83.3% |
At the API prices listed on August 18, 2026, GPT-5.4 costs $2.50 per million input tokens, $0.25 per million cached input tokens, and $15 per million output tokens. GPT-5.4 Pro costs $30 per million input tokens and $180 per million output tokens; cached input pricing is not listed. For both models, sessions exceeding 272,000 input tokens incur the higher multipliers described in the API documentation. Regional processing endpoints carry a 10% uplift.
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Use Pro when a high-value, difficult task justifies a large premium and benchmark-specific gains matter. Standard GPT-5.4 is the more sensible default for repeated professional workflows.
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| Surface | Best suited to | Main trade-off |
|---|---|---|
| ChatGPT / GPT-5.4 Thinking | Individuals and teams handling files, analysis, and deliverables through a managed interface | Less programmatic control and reproducibility than an API integration |
| API / GPT-5.4 | Products and automated document, spreadsheet, browser, or agent workflows | Usage-based billing, integration work, and responsibility for security controls |
| API / GPT-5.4 Pro | Expensive, unusually difficult tasks where marginal capability has business value | Much higher token prices |
| Codex | Repository work, coding agents, testing, and software iteration | Primarily suited to software engineering rather than general productivity |
OpenAI’s launch materials also recommended ChatGPT for Excel for Enterprise customers. Confirm current add-in access, supported Excel versions, regional availability, and plan requirements before deployment. Enterprise and Edu controls can change; consult OpenAI’s business page.
Operational risks and safer deployment
Computer use makes failures more consequential than an incorrect paragraph. A model may misunderstand a visual state, permission, dialog, hidden application context, or partially completed action. OpenAI’s system card describes configurable confirmations for high-risk actions and improved attempts to revert operations and preserve user work, but it also documents long-horizon failures involving state tracking, missing context, destructive actions, and incorrect external actions: GPT-5.4 Thinking system card.
- Use least-privilege accounts and isolated environments.
- Require human approval before sending money, publishing, deleting, submitting, or changing permissions.
- Keep audit logs and make actions reversible where possible.
- Separate drafting from execution and require independent checks for totals, formulas, and citations.
- Do not send sensitive documents or tool data to a hosted service unless the organization’s policy permits it.
Who should use GPT-5.4?
Strong fit
- Finance, consulting, legal, operations, and analytics teams producing multi-step documents or models.
- Developers building agents that must combine retrieval, tools, coding, and verification.
- Teams working with large repositories or document collections, provided they manage long-context cost and retrieval quality.
- Organizations that value one model spanning professional knowledge work and software tasks.
Consider another option
- Use a smaller, cheaper model for routine classification, extraction, summarization, and short-form generation.
- Consider GPT-5.3-Codex where Terminal-Bench-style coding performance is the deciding metric.
- Prefer deterministic software, formulas, retrieval systems, or domain tools when repeatability, traceability, and auditability matter more than open-ended reasoning.
- Avoid computer-use automation when the organization cannot provide isolation, approval gates, and recovery procedures.
- Do not treat GPT-5.4 as a substitute for licensed professional judgment or guaranteed correctness.
Bottom line for personal-finance and business readers
GPT-5.4 is a meaningful shift toward AI that completes professional workflows across files, software, and tools—not merely a chatbot that answers harder questions. OpenAI’s evaluations show substantial gains over GPT-5.2 on several work-product, browser, and computer-use tests, but the model does not win every benchmark, and all headline results are vendor-reported.
For most users, start with GPT-5.4 or GPT-5.4 Thinking, keep human approval around consequential actions, and use Pro only when the value of a difficult task clearly exceeds its much higher API cost. Treat the March “most capable” description as a launch-time claim, not a current ranking after the reported GPT-5.6 release.
Official references: OpenAI’s GPT-5.4 announcement, API model documentation, and Axios’ report on the later GPT-5.6 release.
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