GPT-4o is OpenAI's general-purpose multimodal model, suitable for understanding text questions together with image content, then generating explanations, summaries, code, or business copy. While maintaining GPT-4 Turbo-level text and programming capabilities, it enhances visual and non-English language processing. On this platform, gpt-4o is primarily used for conversational workflows with image and text input and text output.
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-4o",
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 calling methods before selecting a model.
GPT-4o is natively designed to cover multiple modalities. The calling specifications on this page focus on image-text conversations and text delivery, and do not include real-time voice or image generation capabilities.
Learn what gpt-4o can bring to your work.
GPT-4o can answer questions by combining images and text requirements, rather than providing only a single description of the scene. You can continue asking about content in screenshots, objects in photos, or diagrammatic representations, and ask it to organize the results into explanations and checklists. Clearly stating the areas of focus and evaluation goals makes it easier to get answers that fit the task.
Its non-English language processing is a key area of improvement over GPT-4 Turbo, making it suitable for Chinese explanations, cross-language rewriting, and multilingual communication. After providing the original text, target audience, and terminology requirements, you can have it produce versions in different tones and then refine the wording through multiple rounds of feedback, rather than merely translating word for word.
In addition to standard responses, GPT-4o can use JSON Mode to produce content that programs can easily read and participate in function tool calls. Applications can provide tool definitions, receive the call parameters proposed by the model, execute them, and then feed back the results; streaming output is suitable for displaying explanations, copy, and code suggestions as they are generated.
Start with specific tasks to find where the model can be effective.
Provide the user's question, an interface screenshot, and product documentation, and have GPT-4o combine the visual information with the operational goal to create an issue explanation, troubleshooting steps, or a draft customer support reply. This is suitable for handling interface issues that are difficult to describe with text alone; when button names and error messages are involved, clear original images should be retained for verification.
Submit the text in the materials together with relevant images, and ask the model to organize a summary by background, key points, and items requiring confirmation, or rewrite diagrams into explanations for readers. Deliverables can be paragraphs, lists, or JSON content. Tasks should clearly specify which conclusions must come from the materials to avoid having it fill in facts that were not provided.
Submit code snippets, error messages, and necessary interface screenshots, and have GPT-4o explain the logic, propose modifications, and then generate Chinese or English explanations for team communication. It is suitable for discussing code issues together with visible symptoms; generated modification suggestions should still be tested in the development environment rather than treated as fixed directly.
Choose based on task complexity, input materials, and expected results.
If a task involves language expression, image understanding, and code explanation at the same time, GPT-4o is a general-purpose choice. Compared with GPT-4 Turbo, its text and programming capabilities retain the same positioning at launch, while visual and multilingual processing have been enhanced. If the task is mainly repetitive short Q&A, classification, or simple rewriting, evaluate gpt-4o-mini first and compare cost and quality using actual samples.
When you need to explain screenshots, charts, or photos, GPT-4o's text-and-image understanding is better suited to the task; when you need to generate new images, choose a dedicated image creation entry point. Existing message-based applications can continue using Chat Completions, while applications that organize tasks with response objects can use Responses, handling input and result formats respectively.
Based on the characteristics of gpt-4o, first validate a small task whose results can be checked.
You can ask directly: Based on this interface screenshot and the user's description, identify the visible error messages and provide a three-step troubleshooting suggestion. Separate what is seen from what is inferred, and clearly state any small text that cannot be read.
Use clear screenshots and mark the areas of concern; verify button names, error codes, and the sequence of actions against the original image.
Use the full model ID gpt-4o, first confirm the public request format and available parameters on the API page, then connect the 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 scope.
Answers to common questions about using gpt-4o.
When using Chat Completions, include a text question and an image_url image block in the content array of the user message, and place the image address in image_url.url. detail can be auto, low, or high. It is recommended to also specify whether you want recognition, comparison, or explanation, rather than submitting only an image without a task objective.
The gpt-4o on this page is used to understand text and images and generate text responses; it is not an image-generation entry point. If you need text-to-image generation, style changes based on reference images, or combined assets, choose gpt-4o-image or a dedicated image model. Understanding images and creating images are different delivery workflows, so first clarify whether the final output should be text or an image.
For combined image-and-text understanding, content explanation, and multilingual expression, prioritize evaluating GPT-4o; for simple and repetitive text tasks, try gpt-4o-mini first. It is recommended to compare key information retention, formatting stability, and number of revisions using the same set of real samples, then decide whether GPT-4o is needed rather than judging performance based only on the model name.
When using Chat Completions, place 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 each round; for longer tasks, retain periodic summaries and a final version that can be checked independently.
Tool workflows should be organized according to the tool definitions and result formats of the selected public interface. The model is responsible for planning, explaining results, and generating call suggestions; querying, running code, and writing are completed by the execution environment provided by the application. Actual completion status should come from tool returns and verification records, and cannot be determined solely from the model's description.
Choose a development approach or application suited to this model and follow its setup guide.
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SDK installation, authentication and examplesOpenAI Responses API
SDK installation, authentication and examples
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