GPT-5.4 mini is a small model from OpenAI designed for high-frequency professional tasks, with a focus on balancing coding, reasoning, multimodal understanding, and tool use. It is well suited for targeted code modifications, interface screenshot analysis, and sub-agent collaboration: it can handle clear standalone tasks and work with larger models to divide work, keeping daily development and document processing on a tight iteration cycle.
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-5.4-miniimport 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-mini",
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.
400K is the native context specification for GPT-5.4 mini. Platform multimodal and tool inputs are handled according to public protocols, while relevant history and execution results are organized by the application and passed into subsequent requests.
Learn what gpt-5.4-mini can bring to your work.
GPT-5.4 mini's programming strengths focus on clearly defined iterations: checking related functions based on error messages, modifying local logic, generating frontend implementations, or organizing code locations. Provide reproduction steps, relevant code, and acceptance criteria together to get reviewable modification suggestions rather than generic programming explanations.
It can interpret user interface screenshots together with textual requirements, helping analyze page layouts, visible controls, and the current state. It is suitable for turning screenshots into issue lists, operational recommendations, or implementation notes; if the task involves actual clicking and environment operations, tools with execution capabilities and feedback loops are also needed.
In collaborative systems, it is suitable for repository searches, large-file reviews, and supporting document organization. Larger models can handle overall planning and final judgment, while clear subtasks are assigned to mini. Defining the input scope and delivery format for each task makes it easier to consolidate results, verify differences, and track omissions.
Start with specific tasks to find where the model can be effective.
Provide error logs, relevant code, and expected behavior to let the model identify concerns, propose local changes, and explain the scope of impact. Deliverables can include fix code, review comments, and testing recommendations. It is suitable for developers to continuously validate and ask follow-up questions; actual execution and test results are still provided by the development environment.
Submit page screenshots and design requirements, and ask the model to organize component structures, identify visible states, and generate corresponding frontend code or adjustment plans. Chat Completions can combine text and image_url content blocks in the same message, keeping visual information aligned with implementation goals.
Prepare document text, table data, or clear page screenshots relevant to the issue, and specify whether you need summaries, comparisons, or certain information extracted. Submit according to the content formats supported by the selected public API; PDF addresses cannot be used as image_url. Require results to retain original locations, field sources, and unconfirmed items, and verify key figures against the source materials.
Choose based on task complexity, input materials, and expected results.
For simple classification, data extraction, and sorting, GPT-5.4 nano can be considered first; when tasks also require explaining code, understanding screenshots, analyzing causes, or using tools to complete multiple steps, mini is better suited as the primary model. Compared with GPT-5 mini, it focuses on improvements in coding, reasoning, multimodal understanding, and tool use, making it suitable for evaluating upgrades to existing development assistants.
For localized fixes, clear documentation issues, and assisted reviews, choose mini first; for cross-module planning, complex cross-retrieval of long documents, or important final judgments, GPT-5.4 is more suitable. mini's long context window does not mean that every relationship in long materials can be captured with equal accuracy. When dividing work, have mini provide supporting evidence and items to confirm, then review them consistently.
Based on the characteristics of gpt-5.4-mini, first validate small tasks whose results can be checked.
You can ask directly: Find where deprecated APIs are called in this module, provide modification suggestions and compatibility checks for each location, and do not expand the scope of refactoring.
Provide version information and a single task; validate localized changes separately from global architecture judgments.
Use the full model ID gpt-5.4-mini, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, exception handling, and relevant 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-mini.
No. It is a small model in the GPT-5.4 series and differs from GPT-5 mini. It focuses more on improved coding, reasoning, image and text understanding, and tool use. Use gpt-5.4-mini when calling it; when migrating existing applications, compare results using your original task set.
Existing messages-based chat clients can continue using Chat Completions; choose Responses when organizing response tasks, tools, and reasoning settings with input. Both can select this model, but their request and response structures differ, and streaming clients should parse each according to its respective format.
It excels at understanding dense interface screenshots and also has native computer-use capabilities. Submitting only screenshots usually yields analysis or operational recommendations; actual operations require execution tools and environment feedback. For tasks involving submission, deletion, or writing, set permission boundaries and verify execution results.
When using Chat Completions, include relevant history in messages; when using Responses, organize input and related conversation content by document. Provide the latest materials, modification goals, and key constraints each round; for longer tasks, retain interim summaries and a final version that can be checked independently.
It provides context space for more material, but does not guarantee finding all cross-file relationships in one pass. A more reliable approach is to first identify the relevant modules, then submit dependency code, the problem description, and acceptance criteria. Large architectural decisions can be handled by GPT-5.4, while mini handles local analysis and review.
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
$0.15per 1M tokens
Official $0.75
Output
$0.90per 1M tokens
Official $4.50
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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