gpt-5.4-pro is the Pro model in OpenAI's GPT‑5.4 series for complex tasks, suited to scientific questions, mathematical derivations, research synthesis, and professional analysis requiring careful trade-offs. It supports text and image understanding, with a focus not merely on generating answers, but on organizing analysis and conclusions around constraints. When choosing it, prioritize problem difficulty and delivery quality rather than assuming Pro is better for every task.
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.
import 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-pro",
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/output, and invocation methods before choosing a model.
Evaluation figures are official research test results; this platform's input organization and control methods depend on the selected interface and do not guarantee success on actual tasks.
Learn what gpt-5.4-pro can bring to your work.
Pro's value lies in complex reasoning. Official scientific Q&A, advanced mathematics, and abstract reasoning tests demonstrate this positioning. When faced with problems involving interdependent constraints, you can ask it to list assumptions first, then compare solutions, check boundaries, and finally provide conclusions and items to verify, making the output easier for professionals to review.
Text-based questions can be submitted together with charts, page screenshots, or diagrams, allowing the model to explain and reason using visible information. This is suitable for asking specific questions about relationships, labels, and changes in an image. Image understanding ultimately delivers textual analysis; visual capabilities should not be understood as automatically generating images or operating software shown in an image.
The official BrowseComp test demonstrates Pro's performance when working with search tools to complete complex retrieval tasks. In practical research, you can build a question list around the materials, ask it to distinguish facts, inferences, and assumptions, and summarize conclusions by topic. When real-time information is needed, retrieval workflows should be explicitly configured rather than relying on the model to know the latest changes on its own.
Start with specific tasks to find where the model can be most effective.
Provide the problem, known conditions, existing derivations, and points of doubt, and ask the model to identify skipped steps in the argument, compare different solutions, and propose counterexamples or boundary conditions. Deliverables can include step-by-step solutions, an error list, and a verification plan; key proofs and calculations should still be checked through independent methods to avoid judging correctness solely by fluent presentation.
Provide business rules, report excerpts, and analysis objectives together, and ask the model to produce a decision memo, option comparison, or issue list. Prompts can specify evaluation dimensions, source material numbers, and conclusion formats, so that deliverables are built around traceable evidence rather than filling in numbers or business context not provided by the materials.
Submit chart images and related explanations, and define tasks around anomalous changes, metric relationships, or page issues. You can ask for observations, possible explanations, and recommended next verification steps, while specifying which information comes from the image and which is speculation. For dense tables or screenshots with small text, crop key areas first to improve input readability.
Choose based on task complexity, input materials, and expected results.
When a task leans toward advanced mathematics, scientific question answering, or abstract reasoning, you can try Pro first; if it mainly involves standard professional writing and modeling, there is no need to upgrade based on the name alone. In the official GDPval, Pro scored 82.0% and the standard version scored 83.0%, indicating that Pro does not lead on every task. Use real examples to compare correctness, rework required, and suitability for delivery.
Use Chat Completions or Responses and provide the full model ID. Chat Completions uses messages and choices, while Responses uses input and its corresponding response structure; handle history management, streaming events, and tool parameters separately for the selected interface, without mixing the two formats.
Based on the characteristics of gpt-5.4-pro, first validate a small task whose results can be checked.
You can ask directly: Review this model derivation for definitions, boundary conditions, and numerical stability; propose counterexamples and validation methods, then compare alternative approaches.
The question needs sufficient material and checkable conditions; prepare timeout handling for longer responses, and verify key derivations.
Use the full model ID gpt-5.4-pro, first confirm the public request format and available parameters on the API page, then connect your application. Retain result parsing, exception handling, and relevant evidence, and use the same set of real samples to evaluate whether it is suitable for ongoing use.
Before formal use, understand the scope of output quality and capabilities.
Answers to common questions about using gpt-5.4-pro.
No. GPT‑5.4 Thinking is the name of a related product in ChatGPT, while gpt-5.4-pro corresponds to the GPT‑5.4 Pro API model. You should choose based on Pro's task positioning, and not treat ChatGPT's interactive interface, subscription benefits, or mid-task adjustment features as API capabilities.
Not necessarily. In official professional work tests, the standard version outperformed Pro on some items, while Pro stood out in difficult mathematics, scientific Q&A, and abstract reasoning tasks. For office use, focus on the actual task: how complex the materials are, how high the cost of errors is, and whether the results reduce subsequent revisions.
With Chat Completions, you can combine text and image_url in message content, while clearly specifying the object to analyze and the output requirements. Provide clear images and necessary context, especially chart units, abbreviations, and time ranges; image input is for understanding and analysis, not the same as a drawing request.
No. Selecting Pro alone does not provide real-time retrieval by default. Official search evaluations were conducted in environments with search tools available. When you need up-to-date information, configure an available retrieval tool or submit the retrieved materials to the model, and ask the response to distinguish source conclusions from inferences while retaining verifiable citations.
When using Chat Completions, put relevant history into messages; when using Responses, organize input and related conversation content according to the documentation. In each turn, provide the latest materials, revision goals, and key constraints; for longer tasks, retain interim summaries and a final version that can be checked independently.
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
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Chat client setup · Base URL, API key and model
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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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