GPT-5.4 Pro

What is GPT-5.4 Pro?

GPT-5.4 Pro is OpenAI’s higher-compute reasoning model in the GPT-5.4 family. It is intended for difficult tasks where a more deliberate answer is worth additional latency and expense. OpenAI positions it for professional knowledge work, complex coding, long-context analysis, web research, computer use and agentic workflows that require several tool calls or multiple stages of reasoning.

The model is available in ChatGPT for Pro and Enterprise users and, for developers, through the Responses API. The canonical API alias is gpt-5.4-pro; the listed snapshot is gpt-5.4-pro-2026-03-05. The model’s documented release date is March 5, 2026.

GPT-5.4 Pro should not be confused with a general-purpose low-latency model. Its defining trade-off is that it uses more computation than the standard GPT-5.4 model. That can improve performance on demanding tasks, but it also means higher prices and response times that may extend to several minutes on difficult requests.

Key specifications

The context window is the amount of input and generated material the model can handle in one request. At 1.05 million tokens, GPT-5.4 Pro is suited to very large document sets, lengthy project histories and workflows that need to retain substantial context. The 128,000-token output limit is separate: it describes the maximum amount the model can generate, not the amount it can read.

Reasoning and performance

GPT-5.4 Pro supports three documented reasoning-effort settings: medium, high and xhigh. Higher effort gives the model more room to work through difficult problems, but generally increases latency and cost. The setting should therefore match the task rather than automatically being set to the maximum.

Medium effort may be more appropriate for moderately complex work where a faster response is useful. High or xhigh effort is better suited to problems involving competing constraints, long chains of logic, extensive code changes, difficult research synthesis or multi-step tool use. The supplied editorial assessment rates its reasoning at 10 out of 10, coding at 9 out of 10, speed at 4 out of 10 and cost at 3 out of 10. These are comparative editorial estimates, not scores published by OpenAI.

OpenAI recommends background mode for difficult requests to reduce the risk of client or request timeouts. This is an important operational consideration: GPT-5.4 Pro is not designed to behave like a rapid interactive completion model in every situation.

Input, output and supported tools

GPT-5.4 Pro accepts both text and image input. Images can therefore be considered alongside written instructions, documents or code-related material. Its direct model output is text; it does not produce audio, video or images as its native response in the supplied specification.

The model supports tool-assisted workflows through the Responses API. Documented tools and capabilities include:

• Web search for retrieving current information during a task.
• File search for working with uploaded or indexed content.
• Computer use for workflows that interact with a computer environment.
• Image generation as a supported Responses API tool, even though GPT-5.4 Pro itself returns text rather than image output.
• Apply patch for code or file modification workflows.
• Model Context Protocol (MCP) and tool search for connecting to and locating external capabilities.
• Function calling for invoking application-defined operations.
• Streaming for receiving generated output incrementally.

These tools do not mean that every deployment automatically has access to every capability. The application must configure the relevant Responses API tools, permissions and supporting systems. Web search can provide newer information during use, but it does not change the model’s underlying knowledge cutoff, which is documented as August 31, 2025.

Coding and agentic workflows

GPT-5.4 Pro is a strong fit for complex coding tasks that benefit from planning, repository-level context and repeated tool use. Examples include analyzing a large codebase, tracing a difficult defect across multiple files, proposing a coordinated refactor, reviewing implementation choices or applying a sequence of patches. Its large context window can help keep more project material available in a single workflow.

For agentic applications, the model can combine reasoning with function calls, web search, file search, computer use, MCP and tool search. In practical terms, an application can ask it to investigate a question, gather information, inspect files, call external services and then produce a written result. The model is most suitable when the workflow benefits from careful intermediate decisions rather than simply generating one short answer.

Tool use also introduces additional engineering responsibilities. Developers need to handle permissions, validate tool arguments, manage failures and review actions that affect external systems. GPT-5.4 Pro’s ability to call tools does not make its decisions automatically correct or safe.

Pricing and cost trade-offs

The standard listed API price is $30 per 1 million input tokens and $180 per 1 million output tokens. Input and output tokens are priced separately, and output is substantially more expensive than input. The price reflects the model’s higher-compute positioning and makes it a poor default for simple, high-volume generation.

OpenAI’s documented pricing notes also state that Batch and Flex pricing are available at half the standard rate. Regional processing endpoints add a 10% uplift. Requests exceeding 272,000 input tokens are charged at 2× the input rate and 1.5× the output rate for the full session. Large-context applications should account for this threshold when estimating costs.

For example, an application that routinely sends very large histories and generates long responses may pay considerably more than a short prompt-and-answer workload, even if both use the same model. Cost estimates should include repeated tool calls, accumulated context and generated output rather than considering only the initial user message.

Important limitations

• High latency: difficult requests can take several minutes, so the model is not ideal for time-sensitive interactive experiences.
• High price: standard API rates are much better suited to high-value tasks than to inexpensive bulk generation.
• Responses API only: the exact GPT-5.4 Pro model is documented for the Responses API, despite broader endpoint listings associated with the model page.
• No structured outputs: applications that require provider-supported schema-constrained responses should choose an option that supports that feature.
• No fine-tuning: the supplied specification does not list fine-tuning support.
• Text output only: it accepts images but does not directly return audio, video or image output.
• Knowledge cutoff: the underlying cutoff is August 31, 2025. Web search can supplement it but does not update the model itself.
• Reliability still requires review: more reasoning and a high editorial capability assessment do not guarantee factual accuracy, correct code or safe external actions.

When to choose GPT-5.4 Pro

Choose GPT-5.4 Pro when the cost of an incomplete, shallow or incorrect result is higher than the cost of additional computation. It is particularly appropriate for:

• Complex research that combines long documents with current web information.
• Professional analysis involving many constraints, assumptions or source materials.
• Large-context document review and synthesis.
• Complex coding, debugging, refactoring and repository-level planning.
• Agentic workflows that require several tools or carefully sequenced actions.
• Computer-use tasks where the model must reason about a changing environment.
• High-value outputs where a slower, more deliberate response is acceptable.

GPT-5.4 Pro is less appropriate when response time, predictable low cost or strict structured output is the main requirement. A faster or less expensive model may be preferable for routine classification, short summaries, simple drafting, high-volume requests and latency-sensitive user interfaces. If an application needs native audio or video interaction, GPT-5.4 Pro is also not the right modality choice.

Compared with the standard GPT-5.4, Pro is positioned as the higher-compute option. The practical choice is therefore not simply whether Pro is more capable, but whether the additional quality is worth its price and latency for the specific task. Users evaluating another high-compute family option can also compare its role with GPT-5.5 Pro, while remembering that the supplied research does not establish a direct benchmark comparison between the two models.

Bottom line

GPT-5.4 Pro is designed for demanding reasoning rather than inexpensive or instant generation. Its combination of a 1.05 million-token context window, 128,000-token output limit, image input, configurable reasoning effort and Responses API tools makes it suitable for complex professional, coding and agentic workflows. The trade-off is substantial: high token prices, potentially long waits, no structured outputs or fine-tuning, and text-only model output. It is best treated as a specialist high-quality option for tasks where careful work matters more than speed and budget.