gpt-5-mini is the mini model in the OpenAI GPT-5 series. It can handle tasks involving text and image understanding, and delivers results in text. It is suitable for transforming clear business rules, long-form materials, or interface screenshots into summaries, Q&A, and structured drafts. Applications can integrate it using the public request format in the API section of this page.
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-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-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 selecting a model.
Capacity is based on OpenAI's published native specifications; use format, reasoning, and conversation controls according to the selected entry point, and this does not mean that all general parameters apply to this model.
Learn what gpt-5-mini can bring to your work.
gpt-5-mini can process images together with text questions, making it suitable for explaining interface content, organizing information in images, or asking follow-up questions about screenshots. Its visual value lies in converting visible content into text analysis rather than generating images. When submitting, specify the area of focus, evaluation criteria, and expected answer format to reduce ambiguity caused by open-ended descriptions.
Native long context allows lengthy text and multi-turn history to participate in a task together. When processing manuals, requirements materials, or interview records, first provide section identifiers, then request summaries by topic, paragraph comparisons, or action-item extraction. Long input does not mean every detail will be retained accurately, so key conclusions should ideally include the corresponding location in the original text.
You can define the goal as explicit fields, such as issue category, summary, and recommended next steps, so the model generates drafts that are easy for programs to process, with results then validated by the program. When format constraints or function tool definitions are needed, you can choose the Chat Completions endpoint that provides the relevant fields and use it when the model supports the corresponding configuration. Tool calls express function requests; actual execution, permission checks, and result feedback remain the application's responsibility, and generated parameters must not be treated as proof that an operation has been completed.
Start with specific tasks to find where the model can be effective.
Use extracted product descriptions, operating procedures, or internal policies as text input, along with user questions and answer boundaries, and request conclusions, supporting paragraphs, and information that still needs to be added. This is suitable for drafting knowledge-base answers, training outlines, and process comparison tables; important clauses should retain their original wording so reviewers can return to the source material and verify them item by item.
Provide screenshots of error interfaces, descriptions of user actions, and known troubleshooting steps, and have the model organize visible messages, potentially relevant settings, and questions that require further inquiry. Deliverables can be support ticket summaries or troubleshooting checklists. Logs, account status, and system configuration beyond the screenshot must be provided separately and cannot be inferred from the image as known facts.
Submit customer messages, requirement descriptions, or document paragraphs together with classification rules, and request processing drafts with fixed fields, such as topic, urgency, and missing information. This is suitable for assisted classification and summarization stages in workflows. Before batch use, prepare representative samples to check field completeness, boundary categories, and handling of empty values, then integrate it into subsequent programs.
Choose based on task complexity, input materials, and expected results.
GPT-5, GPT-5 mini, and GPT-5 nano have the same public context and maximum output capacity, so do not choose a model based solely on how much text it can hold. When task rules are clear and delivery formats are stable, you can first use mini to validate samples; when complex judgment is involved, give the same set of difficult problems to GPT-5 for comparison; tasks with simple steps can also be included in nano comparisons, with the decision based on evaluation results.
Use Chat Completions or Responses and provide the full model ID. Chat Completions uses messages and choices, while Responses uses input and the corresponding response structure; handle history management, streaming events, and tool parameters separately for the selected interface, and do not mix the two formats.
Based on the characteristics of gpt-5-mini, first validate small tasks whose results can be checked.
You can ask directly: Organize this specification into user questions and answers, with each answer using only information from the original text; list the limitations, required conditions, and questions not explained in the material.
Provide precise fields and output examples; use representative samples to compare quality differences between lightweight models and the primary model.
Use the full model ID gpt-5-mini, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, error handling, and relevant evidence, and use the same set of real samples to assess whether it is suitable for ongoing use.
Before formal use, understand output quality and capability limits.
Answers to common questions about using gpt-5-mini.
It supports image understanding and can generate text responses about screenshots, photos, or charts, but do not treat it as an image generation model. When using Chat Completions, you can combine text and image_url in the message content, and clearly specify what needs to be identified, explained, or compared.
According to publicly available native specifications, both have a 400K context window and 128K maximum output, and GPT-5 nano is also listed with the same capacity. The same capacity does not mean the same task performance; model selection should compare correctness, completeness, and calling cost on real tasks rather than looking only at the mini name.
When using Chat Completions, include relevant history in 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 phased summaries and final versions that can be checked independently.
When structured results are needed, first clearly define field meanings, allowed values, and how missing information should be handled, then ask the model to generate a JSON draft. The Chat Completions endpoint provides JSON object and JSON Schema format options; when the model accepts the corresponding configuration, these options can be used to constrain output. After receiving the result, parsing and business validation are still required, especially checking required fields and factual content; do not write directly to the database merely because the format is correct.
Simply selecting this model does not automatically provide real-time web information. Ordinary text and image Q&A should be based on the submitted materials; when the latest data is needed, the application can retrieve and provide content, or configure an appropriate tool workflow. A model proposing a tool call also does not mean the external operation has already been executed successfully.
Choose a development approach or application suited to this model and follow its setup guide.
OpenAI Chat Completions API
SDK installation, authentication and examplesOpenAI 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 model