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 question answering. Applications can be integrated using the public request format in this page's API section.
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 choosing a model.
Capacity figures are public 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 extraction. When providing input materials, clearly specify the summary scope, names that must be retained, and the delivery format to make results easier to verify. Long context leaves room to provide 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 the relationships between information in an image. The main deliverable is a text explanation, rather than treating image understanding as image generation or automatic editing.
Existing message-based applications can use Chat Completions, passing the current question and necessary history in messages; when using Responses, organize tasks through input and process its return structure. GPT-5 nano is suitable for summarization and classification with clearly defined responsibilities, without needing to adopt another conversation format for simple question answering.
Start with specific tasks to find where the model can be effective.
Input customer messages, existing handling records, and tag definitions, and request a problem summary, missing information, and tag suggestions. Provide examples for easily confused categories, and allow it to return unable to determine. The result can serve as a ticket organization draft, with business programs validating tags and linking records before subsequent processing.
Input interface screenshots and specific questions, such as locating visible error messages, explaining page information, or organizing text in product images. Use image_url together with text instructions, and require it to distinguish between what is seen and what is inferred. Deliverables can be used for customer service explanations or content entry; unclear details should be supplemented with clear close-up images.
GPT-5 nano is suitable for first compressing clear materials into short summaries or tags, then providing additional explanations for a specific field. Clearly specifying key information, allowed categories, and output length helps reduce excessive elaboration; when multiple conflicts or complex reasoning are involved, evaluate a more powerful model.
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, full programming, or long-chain tool tasks, compare it with mini and GPT-5 using the same set of samples before deciding.
Use Chat Completions or Responses and provide the complete 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 according to the selected interface, and do not mix the two formats.
Based on the characteristics of gpt-5-nano, first validate small tasks whose results can be checked.
You can ask directly: Generate a one-line summary and a fixed category for each notice, retaining amounts, dates, and entities; when content is incomplete, mark it as pending confirmation and do not fill in background information.
The clearer the summarization and classification criteria, the more suitable they are; check retention of key information, output length, and handling of missing information.
Use the full model ID gpt-5-nano, 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 evaluate with the same set of real samples whether it is suitable for continued use.
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 a model, compare accuracy on actual tasks, formatting consistency, and rework required, rather than relying only on 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 included in the same message. This visual capability should be understood as image analysis, not as an image generation or image editing model.
When using Chat Completions, include relevant history in messages; when using Responses, organize input and related conversation content according to the documentation. For each turn, provide the latest material, revision goals, and key constraints; for longer tasks, retain interim summaries and a final version that can be checked independently.
Responses uses reasoning configuration, while Chat Completions provides the reasoning_effort field. It is recommended to first test the task using the default configuration, then adjust compatible levels and compare quality and usage; do not directly apply all reasoning enum values from other GPT versions.
Standard endpoints provide 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 already completed the business operation.
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
$0.01per 1M tokens
Official $0.05
Output
$0.08per 1M tokens
Official $0.40
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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