GPT-5 nano is the nano variant of the OpenAI GPT-5 series, supporting text and image understanding and well suited for starting with tasks that have clear rules and verifiable outputs. It has a long native context specification and can be used for material summarization, information organization, and text-and-image Q&A. On this platform, you can choose Responses, Chat Completions, or a simplified conversation method based on application needs.
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-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, input and output, and invocation methods before selecting a model.
Capacity figures are publicly available native specifications; platform calls use the message format, parameter compatibility, and actual request limits of the selected endpoint.
Learn what gpt-5-nano can bring to your work.
Text processing tasks can be organized around summarization, categorization, and field organization. When providing source materials, clearly specify the summary scope, names that must be retained, and delivery format to make results easier to verify. Long context leaves room for providing background in one place, but key rules should still be stated separately rather than buried in a large volume of material.
GPT-5 nano's visual capabilities are suitable for analyzing screenshots, product photos, or charts together with text questions. You can ask it to describe visible content, organize text in images, or explain information relationships in a scene. The focus of delivery is textual explanation, rather than treating image understanding as image generation or automatic editing.
Applications that already manage message history can use Chat Completions; Responses can be selected when interactions are organized with input and response events. Simple Q&A can use the question-and-answer format. Multi-turn conversations continue through stateful and conversation id, reducing the work of repeatedly assembling complete history in the application.
Start with specific tasks to find where the model can be effective.
Provide customer messages, existing handling records, and tag definitions, and request a problem summary, missing information, and tag suggestions. Provide examples for categories that are easy to confuse, and allow an indeterminate result. The output can serve as a ticket organization draft, with tags and related records validated by business logic before subsequent processing.
Provide interface screenshots and specific questions, such as locating visible error messages, explaining page information, or extracting text from product images. Use image_url together with text instructions, and require the model to distinguish between what is seen and what is inferred. The output can be used for customer service explanations or content entry; unclear details should be supplemented with clear close-up images.
Convert meeting notes, business descriptions, or knowledge materials into text, and request topic summaries, action items, and questions requiring confirmation, then continue asking about a particular section. The standard endpoint can carry relevant history, while simplified conversations can reuse an id. Key conclusions should ideally include the corresponding original text for convenient human review.
Choose based on task complexity, input materials, and expected results.
GPT-5 nano, GPT-5 mini, and GPT-5 have the same published context and maximum output figures, so task performance cannot be judged by capacity alone. For routine processing with clear rules and easily verifiable results, evaluate nano first; for complex reasoning, complete programming, or long-chain tool tasks, compare mini and GPT-5 using the same set of samples before deciding.
If your application already uses messages to manage roles and history, choose Chat Completions to preserve the existing structure more easily; if you need Responses event-based interaction, use the corresponding endpoint. If you only want to submit a question and continue the conversation, choose simplified conversations. For workflows requiring format or tool control, choose the standard endpoint rather than putting every requirement into ordinary Q&A.
From a small-scale task to formal integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this endpoint, then submit a small-scale task to review the results.
Keep the full model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using gpt-5-nano.
The official release page lists the same capacity for both: 400K context and 128K maximum output. The same capacity does not mean the same reasoning performance. When choosing, compare accuracy on actual tasks, formatting consistency, and rework requirements rather than looking only at the nano or mini name.
It supports image understanding and can generate text responses about image content. In Chat Completions, text and image_url can be placed in the same message. This visual capability should be understood as image analysis, not as an image generation or image editing model.
The standard endpoint can include relevant conversation history with each request. When using simplified sessions, set stateful=true initially, save the returned id, and continue passing that id along with stateful=true in subsequent requests. Important context should still be provided explicitly; conversation continuation does not mean all details are saved permanently.
Responses uses reasoning configuration, while Chat Completions provides the reasoning_effort field. It is recommended to first test task completion with the default configuration, then adjust compatible levels and compare quality and usage; do not directly apply all reasoning enum values from other GPT versions.
The standard endpoint provides JSON formatting and tool-related configuration, which can be used to design structured processing and function collaboration workflows. First verify compatibility for the required configuration, and validate fields and parameters in the application. Function call results still need to be executed and returned by the program; they do not mean the model has completed the business operation.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
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