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gpt-4

OpenAIChat
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gpt-4

A classic model for complex text and maintaining existing applications

GPT-4 is OpenAI's classic text model, suited for detailed instructions, multiple constraints, code explanations, and document revisions. It can serve as a regression baseline for existing GPT-4 applications, allowing developers to compare constraint adherence, information retention, and revision costs using the same set of materials. This page describes text input and text output for the exact gpt-4 invocation ID, and does not transfer the visual capabilities from GPT-4 series research demonstrations to this model.

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OpenAIModel brand
ChatModel type
Text understandingTask capability
STANDARD APIs · QUICK SETUP

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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 host
api.acedata.cloud
model
gpt-4
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OpenAI Python SDK
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-4",
    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.

Specifications and interface features

Clarify capacity, inputs and outputs, and invocation methods before selecting a model.

Native context
Official native specification: 8,192 tokens
Input and output
Text input; text output including natural language and code
Behavior control
Specify the task, tone, style, and response requirements through system messages
Standard chat endpoint
Chat Completions uses model and messages; supports streamed text
Responses endpoint
Responses uses model and input
Native maximum output
8,192 tokens

The native context figure corresponds to the version at release; platform calls use the message and conversation formats of their respective endpoints, and shared fields cannot all be treated as GPT-4 features.

Core capabilities

Learn what gpt-4 can bring to your work.

Organize complex requirements into clear responses

GPT-4's advantages become more apparent as task complexity increases: it can handle more detailed instructions than simple question answering, such as simultaneously constraining audience, tone, structure, and content boundaries. Clearly state the background materials and acceptance criteria to use it for generating analyses, explaining solutions, or revising text; the more specific the task requirements, the easier it is to determine whether a response is acceptable.

Explain text materials under multiple constraints

GPT-4 can organize key points around given text, explain terminology, and compare different statements. It is suitable for inputting product specifications, process descriptions, or code, and requesting that assumptions, conclusions, and exceptions be separated; for chart tasks, first provide the corresponding data and textual descriptions, and do not infer that this model has visual capabilities from image fields in shared messages.

Set expression and discussion styles by task

Through system messages, GPT-4 can work within a fixed role, expression style, and task scope, for example listing assumptions before giving conclusions, or explaining code to non-technical readers.

Applicable Scenarios

Start from specific tasks and find where the model can play a role.

Code Explanation and Modification Drafts

Provide relevant code, error messages, and expected behavior, allowing GPT-4 to explain the logic, identify areas worth checking, and propose modification drafts. Suitable for turning difficult-to-read functions into maintenance documentation, or for adding information iteratively around the same issue. The deliverable is code and explanatory text; actual execution, testing, and security review are still handled by the development process.

Product Copywriting and Resource Localization

Provide product descriptions, Markdown content, or JSON resources that need translation, clarify terminology, placeholders, and target readers, and request rewriting while preserving structure and identifiers. After completion, check formatting, terminology, and context; translation capabilities in ordinary chat do not mean automatic integration with dedicated localization services.

Business Materials and Process Documentation

Provide text from data tables, metric definitions, or process records, and let GPT-4 create easy-to-read explanations and issue lists. Conclusions must be based on the materials, with missing values and unexplained relationships explicitly left blank; this type of text analysis is suitable for maintaining existing applications and does not promise direct reading of screenshots.

How to Choose This Model

Select based on task complexity, input materials, and expected results.

Complex Text Tasks Better Demonstrate the Value of the Choice

If the task only involves brief rewriting or ordinary conversation, the difference between GPT-4 and GPT-3.5 may not be obvious; when requirements involve detailed constraints, creative organization, and more complex understanding, GPT-4 is more worth choosing. Existing GPT-4 applications can also retain it as an evaluation baseline, using real business samples to compare other models rather than judging upgrade benefits solely by name.

Distinguish Standard Names from Specialized Variants

gpt-4-32k and gpt-4-0314 are historical native variants. They do not indicate that these access points are still provided in the current catalog, and they should not be confused with gpt-4. When longer text processing is needed, compare current models such as GPT-4.1 in the catalog and validate interfaces and performance using the same samples. Real-time information tasks should explicitly design retrieval steps; ordinary GPT-4 conversations do not automatically connect to the internet.

Start with a specific task

Based on the characteristics of gpt-4, first validate small tasks whose results can be checked.

01

Maintain a text baseline for legacy applications

You can ask directly: Explain this complex function as maintenance documentation, organized by inputs, processing branches, return values, and risks. Rely only on the provided code; do not speculate about external system behavior.

02

Prepare inputs that support decisions

Use text, code, and logs; for regression testing of existing applications, record the complete prompt and checkable expected results.

03

Then integrate it into your workflow

Use the full model ID gpt-4, 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.

Usage boundaries

Before formal use, understand output quality and the scope of capabilities.

  • GPT-4 can still make factual and reasoning errors, and may even express incorrect conclusions with confidence. When analyzing materials, you can ask it to distinguish known facts, assumptions, and items requiring confirmation; for professional decisions, verify the basis. Generated code may also introduce security issues, so testing and review must not be omitted.
  • Model knowledge is not a real-time information source. When handling the latest policies, product changes, or recent events, provide relevant new materials and citations before requesting analysis; ordinary conversation does not itself imply automatic web searching or continuously updated knowledge.
  • gpt-4 is a classic model for text input and output. Visual, audio, or structured fields in shared interfaces do not mean this model supports every combination; for image tasks, choose a model that the catalog explicitly states supports vision, and follow the documentation for the specific request.

Frequently Asked Questions

Answers to common questions about using gpt-4.

What are the main differences between GPT-4 and GPT-3.5?

The differences are mainly evident in complex tasks. GPT-4 is better suited for detailed instructions, creative organization, and analysis with multiple conditions; the gap may not be obvious in simple casual chats. When choosing, compare content accuracy, constraint adherence, and the number of revisions using the same set of real tasks.

Does gpt-4 support image input or image generation?

The current official gpt-4 model documentation lists text for both input and output. GPT-4's early research release introduced multimodal directions, but that does not mean the exact invocation ID on this page supports vision or drawing; when you need to view images, choose models that explicitly list vision capabilities, such as GPT-4o or GPT-4.1, and choose a dedicated image endpoint to generate images.

How can I have GPT-4 continue a previous discussion?

When using Chat Completions, submit the necessary user and assistant history in messages. This can continue the discussion, but it does not imply permanent memory, nor should irrelevant content accumulate indefinitely.

Is gpt-4 the same as the 32K or a fixed-date version?

No. At launch, the official context length for gpt-4 was 8,192 tokens, while gpt-4-32k was another variant; gpt-4-0314 was used to lock the snapshot at that time. A name without a date cannot be directly treated as that snapshot; when reproducing a version, record the full model name and request settings.

Which endpoint should I choose first when calling GPT-4?

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 according to the selected interface, and do not mix the two formats.

Use it in your favorite tools

Choose a development approach or application suited to this model and follow its setup guide.

Connect gpt-4 to OpenAI SDK

OpenAI Chat Completions API

SDK installation, authentication and examples

Connect gpt-4 to OpenAI SDK · Responses

OpenAI Responses API

SDK installation, authentication and examples

Connect gpt-4 to Cherry Studio

Using Ace Data Cloud with Cherry Studio

Chat client setup · Base URL, API key and model

Connect gpt-4 to ChatBox

Using Ace Data Cloud with ChatBox

Chat client setup · Base URL, API key and model
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