GPT-5.4

What is GPT-5.4?

GPT-5.4 is OpenAI's flagship model for complex reasoning, coding, and professional knowledge work. OpenAI positions it as a frontier model that unifies recent advances in reasoning and agentic workflows. In practice, GPT-5.4 is meant for tasks where accuracy, planning depth, and multi-step execution matter more than raw latency. It is a strong default when you need long-context reasoning and reliable professional output.

The model is available in the OpenAI API as `gpt-5.4` and supports configurable `reasoning.effort` levels, enabling developers to trade off depth and speed depending on the task. This makes GPT-5.4 flexible for both quick analysis and deep, long-horizon planning within a single model family.

Official model ID, context window, and output limits

GPT-5.4 is designed for long-context work. OpenAI documents a 1,050,000 token context window and a 128,000 token maximum output. This makes GPT-5.4 viable for tasks like multi-document analysis, long research traces, and complex agent workflows that must keep large task state in memory.

Reasoning control and agentic workflow support

GPT-5.4 supports configurable reasoning effort, which lets you control how much the model “thinks” before answering. Lower settings are faster and cheaper, while higher settings can produce more thorough analysis and better long-horizon planning. This makes the model flexible: it can power both rapid iterations and deep research-style workflows using the same base model.

OpenAI documents multiple reasoning effort levels for GPT-5.4, which makes it easier to standardize performance across tasks. For routine work, lower effort can keep latency and cost down. For complex projects, higher effort improves consistency by allowing the model to spend more compute on long-horizon planning.

Endpoints and modalities

The GPT-5.4 model page lists support across multiple OpenAI endpoints, including Chat Completions, Responses, Realtime, Assistants, and Batch. This means GPT-5.4 can be used in interactive chat, agent-style responses, streaming experiences, and background batch processing depending on your application needs.

GPT-5.4 accepts text and image inputs and produces text output only. Image, audio, and video outputs are not supported. When designing workflows, keep this modality boundary in mind and route media generation tasks to specialized models where necessary.

Latency, batching, and production deployment

GPT-5.4 is available across both real-time and batch endpoints. For interactive experiences, use streaming or realtime endpoints to reduce perceived latency. For large workloads, batch processing can provide a more predictable cost profile and is useful for offline analysis, document processing, or scheduled research jobs.

In production systems, it is often effective to pair GPT-5.4 with a lighter model for quick tasks and reserve GPT-5.4 for high-value steps. This reduces cost while keeping reliability where it matters most. The long context window is powerful but should be used selectively because it increases cost and latency.

Multimodal input guidance

GPT-5.4 accepts image inputs alongside text. This enables workflows like screenshot understanding, UI analysis, diagram interpretation, and document reading. For best results, keep image prompts specific: identify the part of the image you want analyzed and ask for a structured output such as a checklist, summary table, or extracted values.

When using images in professional contexts, include relevant context in text to reduce ambiguity. For example, describe the goal of the analysis, such as "summarize the risks in this architecture diagram" or "extract line items from this invoice." The model's reasoning effort can also be set higher for complex visual inputs.

Pricing snapshot and long-context considerations

OpenAI lists GPT-5.4 at $2.50 per 1M input tokens, $0.25 per 1M cached input tokens, and $15 per 1M output tokens. For prompts above 272K input tokens, GPT-5.4 uses a higher pricing tier (2x input and 1.5x output) for the full session. This means that very long-context requests should be planned carefully to avoid unexpected cost spikes.

If you are building applications that routinely exceed a few hundred thousand tokens, it is best to segment tasks and use context management techniques like summarization or compaction between steps. The model’s long context is powerful, but it is still expensive compared to shorter-context models.

Enterprise and data governance considerations

The GPT-5.4 model page lists support for zero data retention and data residency options with regional processing. If you operate in regulated environments, these options can be important for compliance. It is a good idea to align your deployment configuration with your organization's data policies before rolling out GPT-5.4 at scale.

OpenAI's pricing documentation notes a 10% uplift when data residency is enabled. If your use case demands residency or strict data retention guarantees, plan for those cost impacts and include them in your overall budgeting model.

Where GPT-5.4 fits in the GPT-5 family

GPT-5.4 is the strongest general-purpose model in the GPT-5 family for reasoning-heavy work. It sits below GPT-5.4 Pro in raw compute intensity, while GPT-5.3 Codex remains the most specialized coding agent. GPT-5.3 Chat is optimized for conversational speed. GPT-5.4 is the balanced choice when you need long-context reasoning without the higher cost of Pro.

Common use cases and workflow patterns

GPT-5.4 is well suited to high-value professional tasks: compliance analysis, contract review, complex debugging, architecture design, and multi-document synthesis. In research settings, it can ingest large reading lists and produce structured findings. In engineering, it can plan multi-step changes across repositories while using tools like file search or code interpreter to validate outputs.

A common pattern is a staged workflow: first, ask GPT-5.4 to analyze and outline the approach at medium effort; then run a focused execution at high effort, optionally with tool use. This keeps costs predictable while still leveraging the model's deep reasoning for critical steps.

Prompting guidance for GPT-5.4

GPT-5.4 performs best when tasks are specified with clear goals, constraints, and success criteria. For complex projects, define expected outputs (reports, spreadsheets, code changes) and provide structured inputs, such as bullet lists or tables. When using tools, ask the model to explicitly state which tools it intends to use and why. This improves transparency and reduces wasted tool calls.

Reasoning effort should be selected based on task difficulty. For long research or multi-step planning, start at high or xhigh. For simple summarization or quick edits, low or medium is usually sufficient. In iterative workflows, it can be useful to start with medium effort for exploratory drafts and then re-run critical steps at higher effort for final output.

Reliability tips and debugging prompts

GPT-5.4 is strong, but it can still make mistakes when instructions are ambiguous. To improve reliability, ask it to list assumptions before answering, or to produce a short plan first. For data-heavy tasks, request explicit citations to source sections within the provided context. This reduces the chance of ungrounded output and makes review easier.

For coding tasks, ask the model to propose a diff and explain the changes in plain language. When using tools, request a short tool-use log describing what it searched or executed. These small guardrails can significantly improve trust and make automated workflows safer.

Workflow patterns for complex projects

GPT-5.4 is well suited to multi-step workflows where the model must keep track of constraints across multiple stages. A common pattern is to start with a structured plan, then execute each step with explicit success criteria. This keeps the model focused and makes it easier to verify intermediate results.

For larger projects, it is often useful to alternate between reasoning and execution phases. For example, you can ask GPT-5.4 to synthesize a plan at medium effort, then run each major section at high effort. This reduces cost while preserving reliability where it matters most.

Operational guidance for long-context tasks

The 1.05M context window enables large-scale tasks such as multi-document synthesis, full codebase analysis, or extended research sessions. However, longer prompts cost more and can slow response times. A practical pattern is to chunk work into phases: ingest and summarize sources, synthesize intermediate notes, then produce a final report with the model’s reasoning effort set to a higher level.

This staged workflow keeps costs predictable and reduces the risk of losing key details. GPT-5.4’s context capacity is a safety net, but careful context management still matters for real-world production.

Safety, accuracy, and limitations

GPT-5.4 is positioned as OpenAI’s most capable reasoning model, but it is still a probabilistic system. For high-stakes use cases, outputs should be reviewed by domain experts. The model can be paired with verification workflows, but it remains the developer’s responsibility to validate final results.

Even so, it is important to monitor usage, enforce rate limits, and build safeguards into production pipelines. Long-context tasks can be expensive and should be reserved for cases where the extra context provides clear value.