What is GPT-5.4?
GPT-5.4 is OpenAI’s proprietary model for professional work, computer-use agents, coding and large-context analysis. It was released March 5, 2026. This guide separates documented specifications from vendor benchmark claims and gives teams a practical way to decide whether the model belongs in a real evaluation.
QUICK VERDICT: GPT-5.4 is no longer OpenAI’s newest frontier model, but it remains a cost-relevant baseline with native computer use and a million-token context window.
Field | Verified value |
|---|---|
Provider | OpenAI |
Release date | March 5, 2026 |
Availability | Available |
License | Proprietary |
Context window | 1,050,000 tokens |
Maximum output | 128,000 tokens |
Modalities | Text and image input; text output |
API pricing | $2.50 input, $0.25 cached input and $15 output per million tokens; long-context multipliers apply above 272K input tokens |
Access | ChatGPT as GPT-5.4 Thinking, OpenAI API and Codex |
Best fit | professional work, computer-use agents, coding and large-context analysis |
Last verified | September 2, 2026 |
What changed with GPT-5.4
GPT-5.4 should not be evaluated as a name change. The important differences are the model’s reasoning controls, tool behavior, context policy, access route and economic profile. Those details determine whether a benchmark result transfers to production.
Area | What changed or matters |
|---|---|
Computer use | First OpenAI general-purpose model released with native computer-use capabilities |
Context | Expanded to 1.05M tokens with 128K maximum output |
Reasoning | Supports none, low, medium, high and xhigh effort |
Tooling | Responses API supports web search, file search, hosted shell, code interpreter, MCP and other tools |
The correct comparison baseline is GPT-5.2. Teams planning a new deployment should also include GPT-5.5 and GPT-5.6 where access allows. Testing only the newest model or only the incumbent hides the migration cost, output-style changes and tool-use regressions that often matter more than a small benchmark gap.
GPT-5.4 capabilities
The clearest fit is professional work, computer-use agents, coding and large-context analysis. That does not mean every task in those categories should use the model. A production system combines the base model with prompts, retrieval, tools, permissions, memory, retry logic and human review. The model card describes only one layer of that system.
Capability | Practical implication |
|---|---|
Long context | 1,050,000 tokens. Test retrieval accuracy at realistic lengths rather than assuming every token receives equal attention. |
Output capacity | 128,000 tokens. Long output is useful only when the verification process can keep up. |
Modalities | Text and image input; text output. Confirm format-specific accuracy with your own files. |
Agent use | Use explicit tool schemas, narrow permissions, approval gates and recoverable operations. |
Reasoning | Record the exact effort level because cost, latency and quality can change materially. |
For coding, evaluate repository navigation, test creation, regression rate, review burden and recovery after a failed tool call. For research, measure citation correctness, source coverage and whether the model distinguishes evidence from inference. For computer or browser use, record every unsafe click, wrong field, lost state and unapproved action.
Benchmarks and evidence
The evidence below comes from OpenAI or named launch partners. It is useful for identifying intended strengths, but it is not equivalent to an independent head-to-head test. Prompts, tools, reasoning budgets, sampling, infrastructure and scoring rules can differ.
Evidence | Reported result | How to interpret it |
|---|---|---|
Professional work | OpenAI positioned it as its most capable professional model at launch | Vendor evaluation suite |
Computer use | Launch emphasized cross-application agent workflows | First-party demonstrations and benchmarks |
Large context | 1.05M context is documented in the API model card | Specification, not a recall guarantee |
Treat benchmark results as a shortlist signal. Before purchasing or migrating, reproduce representative work under one frozen protocol. Keep model snapshots, reasoning effort, tool access and token budgets constant. Report repeated-run pass rates and total cost per accepted result, not a single best attempt.
Pricing, access and deployment
ChatGPT as GPT-5.4 Thinking, OpenAI API and Codex. The current pricing reference is $2.50 input, $0.25 cached input and $15 output per million tokens; long-context multipliers apply above 272K input tokens. Provider pricing changes frequently and can include cache rates, batch discounts, regional premiums, long-context multipliers, priority processing and tool-call fees. Recheck the official pricing page before budgeting.
Cost driver | What to measure |
|---|---|
Input tokens | Prompt, retrieved context, tool results and repeated history |
Output tokens | Visible answer plus any billable reasoning or generated artifacts |
Caching | Eligible repeated prefixes, cache-read price and expiration policy |
Tools | Search, computer use, code execution and third-party API fees |
Retries | Failed runs, verifier loops and human rework |
Success-adjusted cost | Total spend divided by deliverables that pass review |
A cheaper token price can lose to a more expensive model if it takes more steps, retries more often or produces work that needs heavy correction. Conversely, a frontier model can be wasteful when a smaller model already passes the task rubric. Route by measured task difficulty rather than brand prestige.
GPT-5.4 limitations
- GPT-5.5 and GPT-5.6 are newer.
- Inputs above 272K tokens trigger higher pricing for the full session.
- Realtime and fine-tuning are not supported on the model card.
- Tool calls can add separate fees and risks.
- Aliases can change unless a snapshot is pinned.
High-stakes medical, legal, financial, security and scientific work requires qualified review. Store the exact model identifier, prompt version, tools, source documents and approvals for each consequential run. Build a rollback path before granting write access to repositories, browsers, databases or cloud infrastructure.
Risk | Minimum control |
|---|---|
Hallucination | Require source checks or executable tests |
Prompt injection | Separate untrusted content from instructions and restrict tools |
Over-permission | Use least privilege and approval gates |
Silent model change | Pin snapshots where possible and run regression tests |
Data exposure | Review retention, regional processing and provider terms |
Runaway cost | Set token, time, tool-call and retry budgets |
How to evaluate GPT-5.4
Create 20 to 50 tasks from real work. Freeze the tasks and rubric before testing. Include easy tasks, normal tasks, edge cases and adversarial inputs. Give each model equivalent tools and enough budget to finish, but cap time and retries. Repeat non-deterministic runs so one lucky result does not decide the winner.
Test area | Record |
|---|---|
Task completion | Pass or fail plus rubric score |
Reliability | Repeated-run success and variance |
Quality | Factuality, instruction adherence and usefulness |
Tool use | Wrong calls, retries, recovery and permission errors |
Efficiency | Wall time, tokens, cache use, tool fees and human review |
Safety | Unsafe actions, injection response and sensitive-data handling |
Migration | Prompt changes, integration work and output-style regressions |
Compare GPT-5.4 with GPT-5.2 and GPT-5.5 and GPT-5.6. Choose the least expensive configuration that meets the acceptance threshold with an adequate safety margin. Re-run the suite after any model snapshot, system prompt, retrieval or tool change.
Frequently asked questions
Is GPT-5.4 available now?
Available. The documented access routes are ChatGPT as GPT-5.4 Thinking, OpenAI API and Codex. Availability can differ by region, plan and partner platform.
Is GPT-5.4 open source?
No. GPT-5.4 is proprietary. Access is controlled by OpenAI and supported distribution partners.
How much does GPT-5.4 cost?
$2.50 input, $0.25 cached input and $15 output per million tokens; long-context multipliers apply above 272K input tokens. Budget with measured end-to-end workloads because token rates alone omit retries, tools, caching, long-context multipliers and review time.
What is GPT-5.4 best used for?
Its strongest documented fit is professional work, computer-use agents, coding and large-context analysis. Start with a supervised pilot and keep human sign-off for consequential work.
Should I migrate from GPT-5.2?
Only after a side-by-side evaluation. Measure pass rate, total cost, latency, output style, tool reliability and the engineering work required to migrate. A newer model is not automatically the better operational choice.
Related model guides
Start with the broader Best AI Models directory.
Compare with the GPT 5.5 guide.
Compare with the GPT 5.6 Sol guide.
Official sources and update policy
Checked September 2, 2026. We update this guide when the provider changes the model specification, pricing, access, licensing or safety documentation. Vendor benchmarks are attributed and are not presented as independent testing. No vendor payment or affiliate relationship determined inclusion.