GPT-5.4 is OpenAI's general-purpose reasoning model for professional work, combining complex analysis, coding, and visual understanding, with key improvements for documents, spreadsheets, presentations, and multi-step tool tasks. It is suited to work that requires sustained understanding of materials and the creation of reviewable deliverables, rather than just brief Q&A. Applications can integrate it using the public request format in this page's API section.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
api.acedata.cloudgpt-5.4import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.responses.create(
model="gpt-5.4",
input="Hello!",
)
print(response.output_text)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity, input and output, and invocation methods before choosing a model.
Native specifications describe model capabilities; this platform's input organization, tool configuration, and output control depend on the selected invocation endpoint.
Learn what gpt-5.4 can bring to your work.
GPT-5.4 focuses not only on summarizing materials, but also on creating and modifying documents, spreadsheets, and presentation content. After providing business context, data definitions, and delivery requirements, it can be used to organize analytical structures, explain calculation logic, and draft body text and presentation outlines, bringing content closer to deliverables ready for review and further refinement.
It combines the coding capabilities of GPT-5.3-Codex with stronger visual understanding, making it suitable for handling code, error messages, and interface screenshots at the same time. Developers can ask the model to propose fixes, generate revised code, and continue iterating based on runtime results; after connecting testing tools, they can also conduct checks around the page's actual behavior.
GPT-5.4 natively supports computer use and Tool Search, enabling it to determine actions from screenshots and look up tool definitions as needed. For tasks that require reading information, calling business functions, and organizing results, it is well suited as the planning and decision-making core; the execution program completes actions and returns the results to the model for continued processing.
Start with specific tasks to find where the model can make an impact.
Input business data text, metric definitions, and chart screenshots, and ask the model to explain changes, distinguish facts from assumptions, and draft an executive summary. Deliverables can include an anomaly list, analysis paragraphs, and a presentation outline; if downloadable spreadsheets or presentation files are needed, connect file-creation tools to complete formatting and export.
Submit requirements, relevant code, error logs, and page screenshots together, and let GPT-5.4 analyze interaction issues and propose component changes and testing steps. It is suitable for tasks involving both code logic and visual layout, delivering fix code, explanations of causes, and an acceptance checklist, with real-browser testing used to confirm the final behavior.
Organize contracts, specifications, or project materials into text with section numbers, and clearly define the clauses to compare and the review objectives. The model can help organize definitions, identify inconsistencies before and after, and create comparison tables and follow-up question lists; retain the original text locations so reviewers can return to the materials to verify them rather than receiving only a general summary.
Choose based on task complexity, input materials, and expected results.
Compared with GPT-5.2, GPT-5.4 places greater emphasis on producing professional deliverables, visual understanding, and multi-step tool collaboration; compared with GPT-5.3-Codex, it incorporates coding capabilities into a more general-purpose work model. If a task includes code, business documents, and interface observation at the same time, GPT-5.4 is worth prioritizing for testing, but this should not be understood to mean that it is necessarily stronger for every coding task.
GPT-5.4 is the base model, while GPT-5.4 Pro is a separate variant for more complex tasks, not a settings switch for the same ID.
Based on the characteristics of gpt-5.4, first validate a small task whose results can be checked.
You can ask directly: Based on operating data, metric definitions, and reporting templates, generate an executive summary and recommendations for table structure, and identify items that need recalculation or additional explanation.
Provide templates and raw data; saving and calculating Office files require the appropriate tools, while text drafts should be reviewed separately.
Use the full model ID gpt-5.4, first confirm the public request format and available parameters on the API page, then connect your application. Keep the result parsing, exception handling, and relevant evidence, and use the same set of real samples to assess whether it is suitable for continued use.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using gpt-5.4.
GPT-5.4 Thinking is the ChatGPT product form of GPT-5.4; API calls use gpt-5.4. Experiences such as process prompts and interactive adjustments in ChatGPT cannot be directly regarded as part of an API response; gpt-5.4-pro is a separate variant.
When using Chat Completions, place text and image_url in the content array of the same message, and describe what you want recognized, compared, or explained. Images can be set to auto, low, or high detail; when analyzing small text and complex interfaces, clear screenshots are usually more important than vague questions.
The model has relevant planning and instruction-generation capabilities, but executing actions requires tools provided by the application. After a function call is returned, the program should execute it and send back the results; browser or desktop operations also require the corresponding environment. Simply sending an operational request will not automatically establish a complete execution and verification workflow.
GPT-5.4's official native evaluations used reasoning settings from none to xhigh. On this platform, Responses provides the reasoning object, and Chat Completions provides the reasoning_effort field, but the same set of levels cannot be directly applied to both: the current Chat Completions parameter enumeration does not include none. When configuring, use levels valid for gpt-5.4 in the selected endpoint, and compare response quality and wait time using real tasks; values such as minimal and max in shared enumerations should not be directly treated as settings supported by this model.
When using Chat Completions, put relevant history into messages; when using Responses, organize input and related conversation content according to the documentation. Provide the latest materials, revision goals, and key constraints in each turn; for longer tasks, retain interim summaries and a final version that can be independently reviewed.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples↗OpenAI Responses API
SDK installation, authentication and examples
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and modelThis is only a basic call example. See the full docs for more parameters and advanced usage.
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