GPT-5.4 nano is a small model in the OpenAI GPT-5.4 family focused on speed and cost efficiency, suitable for classification, data extraction, candidate ranking, and simple coding subtasks. It supports text and image understanding and can organize well-defined inputs into concise text or results that are easy for programs to process. It is particularly well suited for embedding in high-frequency business workflows rather than handling all complex planning and final decisions.
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-nano",
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, inputs and outputs, and invocation methods before choosing a model.
nano's native task positioning and this platform's interactive features are listed separately; the scope of entry parameters is not equivalent to model capacity or all native tool capabilities.
Learn what gpt-5.4-nano can bring to your work.
Classification, extraction, and ranking are core uses of nano. After providing label definitions, field rules, and a few examples, you can have it determine ticket categories, organize text attributes, or compare candidates. The clearer the task boundaries, the more suitable it is to deliver brief results and then have a program check fields, enum values, and missing items.
nano is suitable for supporting work in programming workflows, such as reading specified code snippets, explaining errors, proposing local changes, or locating relevant logic. Providing the goal, relevant file contents, and acceptance criteria together is more appropriate than asking it to handle an entire project independently; generated changes still need to pass testing and review.
In addition to text, nano can also answer questions about images. Placing an image and clear requirements in the same message can be used to identify image content, assist with categorization, or explain screenshots. The output focuses on textual understanding results; for dense interfaces and small text, it is best to first crop the key areas and retain human review.
Start with specific tasks to find where the model can be effective.
Provide customer feedback, business tag descriptions, and priority rules, and have nano output issue categories, affected products, and handling recommendations. It is suitable for initial organization before manual processing; for cross-category or incomplete content, you can require it to return a pending confirmation status to avoid forcing an assignment to a fixed tag.
Provide product descriptions, notification text, or candidate items, clearly specify the names, times, conditions, and ranking criteria to extract, and deliver structured field results or ranked lists. When integrating with a program, validate the format and require missing fields to be left blank, avoiding treating model inferences as facts already provided in the original text.
Provide relevant functions, failure logs, and expected behavior, and have nano list possible causes, suggested modification locations, and validation steps. It is suitable for narrow-scope analysis and draft modifications; project-level solutions, cross-module impacts, and final merge decisions can be assessed collectively by a stronger model or developers.
Choose based on task complexity, input materials, and expected results.
For high-volume tasks with clear rules that require only a small number of decisions per instance, consider GPT-5.4 nano first. If tasks involve more complex terminal operations, intensive interface understanding, or multi-step coordination, GPT-5.4 mini is more worth considering. You can first compare accuracy and rework volume using real samples, then decide whether nano should handle preprocessing.
GPT-5.4 nano is a separate new version, not a compatible alias for GPT-5 nano. OpenAI positions it as a significant upgrade, suitable for evaluating existing classification, extraction, and coding-assistance workflows. During migration, retain the same test set and check label distribution, field completeness, and exception handling. Do not simply replace the name and directly overwrite production tasks.
Based on the characteristics of gpt-5.4-nano, first validate small tasks whose results can be checked.
You can ask directly: Rank candidates according to the given rules, and output only the ID, relevance, and supporting original text. Items that do not meet the conditions must not enter the recommendation list.
Provide allowed fields, ranking rules, and positive and negative examples; use the results only after the program validates the structure and business conditions.
Use the full model ID gpt-5.4-nano, first confirm the public request format and available parameters on the API page, then connect your application. Keep result parsing, exception handling, and supporting evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand output quality and capability boundaries.
Answers to common questions about using gpt-5.4-nano.
It is best suited for clearly defined and frequent classification, field extraction, candidate ranking, and simple programming assistance. Prompts should clearly specify decision criteria, allowed outputs, and how to handle missing information. If a task requires complex planning or final judgment, consider mini or a larger model.
You can use images together with text prompts for visual understanding and assisted classification. This describes a workflow with image-and-text input and text output, not an image creation entry point; if you need to generate or edit images, choose a dedicated image model and the corresponding API.
If you already have messages history management and choices parsing logic, you can use Chat Completions; if you use a response-object workflow, you can choose Responses and submit content through input. Both use gpt-5.4-nano, but their request structures and result parsing cannot be mixed.
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 material, modification goals, and key constraints in each turn; for longer tasks, retain interim summaries and a final version that can be checked independently.
You can require JSON output in the prompt and clearly specify fields, types, and rules for missing values. The Chat Completions endpoint provides a response_format setting; when the selected mode applies to this model, it can be used together, but JSON format settings should not be treated as a guarantee that all structural constraints will be met. Business applications still need to parse and validate results, check field completeness, value ranges, and whether content matches the input, and handle formatting errors or incomplete responses.
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 modelInput
$0.04per 1M tokens
Official $0.20
Output
$0.25per 1M tokens
Official $1.25
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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