GPT-4o is OpenAI's general-purpose multimodal model, suited 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.
Clarify capacity, input and output, and invocation methods before selecting a model.
GPT-4o's native design covers multiple modalities. The API specifications on this page focus on image-and-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 only providing a single description of the scene. You can continue asking about content in screenshots, objects in photos, or diagrams, and ask it to organize the information into explanations and checklists. Clearly specifying the area of focus and evaluation goal makes it easier to get responses tailored to 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 by word.
In addition to conventional 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 return 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 let GPT-4o combine the visual information with the operational goal to create a problem explanation, troubleshooting steps, or a draft customer support reply. It 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 to be confirmed, or rewrite diagrams into reader-facing explanations. Deliverables can be paragraphs, checklists, or JSON content. The task 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 let GPT-4o explain the logic, propose modification plans, and 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 being treated as already fixed.
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 remained at the same level at launch, while vision and multilingual processing were 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.
Use gpt-4o when you need to explain image content; when you need to generate images from text or reference images, choose image creation entry points such as gpt-4o-image. Existing messages workflows can use Chat Completions; input workflows can use Responses; if you want the service to retain multi-turn conversations, choose AI Chat v2. A standard gpt-4o ID should not be treated as a date-pinned version.
From a small-scale task to production 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 entry point, 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.
Understand output quality and capability boundaries before production use.
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 put the image address in image_url.url. detail can be auto, low, or high. It is recommended to also specify whether you want to identify, compare, or explain something, 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 your final output should be text or an image.
For combined text-and-image 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, format stability, and number of revisions using the same set of real samples, then decide whether GPT-4o is needed rather than judging performance solely by the model name.
When using Chat Completions, include the necessary user and assistant history in messages; when using Responses, provide the relevant conversation in input. If you want to simplify history management, use AI Chat v2's stateful conversations and include the returned id in subsequent requests to keep the discussion focused on the same task.
GPT-4o's language and programming capabilities do not mean that the model itself has web access or a code execution environment. When using custom function tools with Chat Completions, the application needs to validate parameters, perform operations, and return the results. AI Chat v2 provides built-in tools such as web search, web page fetching, and file reading, and can also use authorized connections; these are tool workflows provided by the entry point, not capabilities of the model itself. Running code still requires configuring the appropriate execution tools and implementing permission and security checks.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with a clear goal, and use real results to determine whether it is suitable for your work.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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