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gpt-5.2

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gpt-5.2

A deep reasoning model for professional long-form analysis and complex programming

GPT‑5.2 is OpenAI's reasoning model for professional knowledge work, corresponding to GPT‑5.2 Thinking. It excels at organizing information from long documents, code, and images into verifiable analytical results, and handling tasks that require multi-step reasoning and tool collaboration. Compared with lightweight everyday conversations, it is better suited for complex code fixes, cross-material synthesis, chart interpretation, and projects with clear delivery requirements.

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

Keep your SDK. Connect in minutes.

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 hostapi.acedata.cloud
modelgpt-5.2
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-5.2",
    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, input and output, and invocation methods before selecting a model.

Model positioning
The API model gpt-5.2 corresponding to GPT‑5.2 Thinking
Input and output
Text and image understanding; text responses and code generation
Native reasoning control
Supports xhigh reasoning effort, suitable for tasks where answer quality is the top priority
Invocation endpoints
/openai/responses、/openai/chat/completions、/aichat/conversations
Structured delivery
Chat Completions provides text, json_object, and json_schema format configuration
Interaction and tools
Streaming responses, function tool definitions, and tool selection control
Continuous conversations
AI Chat can continue conversations through stateful=true and a conversation id

Thinking positioning and xhigh are native capabilities; use input organization, response formats, and session management according to the selected platform endpoint.

Core Capabilities

Learn what gpt-5.2 can bring to your work.

Organize lengthy materials into complete judgments

GPT‑5.2’s long-context strength is not limited to summarization; it also connects conditions, exceptions, and conclusions scattered across different sections. When handling reports, contract texts, or project materials, you can ask it to compare materials around the same question and provide evidence, differences, and items requiring confirmation, reducing the work of reading section by section and manually piecing together conclusions afterward.

Move from code issues to repair solutions

It is well suited to locating issues by combining relevant code, error logs, and requirement descriptions, generating patches, and explaining the reasons for changes. Compared with GPT‑5.1 Thinking, GPT‑5.2 has been enhanced for software engineering and frontend development, making it particularly suitable for tasks involving multiple files, interface constraints, or complex interfaces, rather than merely completing short code snippets.

Combine image understanding with tool collaboration

When working with dashboards, product screenshots, and technical diagrams, GPT‑5.2 can analyze visual content together with textual requirements and explain chart relationships and interface layouts. After connecting business functions, it can also continue reasoning based on tool-returned results and organize subsequent steps, making it suitable for linking information retrieval, analysis, and final answers into a clear workflow.

Use Cases

Start with specific tasks to find where the model can make an impact.

Contract and research material synthesis

Provide contract texts with section labels, research excerpts, and comparison goals, and have the model deliver a table of clause differences, key findings, and citation locations. Define the comparison dimensions first, then ask it to explain the basis for each item. This is suitable for reviewing consistency, omissions, and mutual impacts across multiple materials, rather than receiving only a broad summary.

Complex bug fixes and code review

Submit relevant files, reproduction steps, logs, and expected behavior, and ask it to first explain the failure chain before outputting modified code and validation recommendations. During review, you can specify compatibility requirements, security boundaries, and interfaces that must not be changed. The final deliverables can include a patch draft, risk explanation, and test checklist, with the development environment performing actual execution validation.

Operations charts and product screenshot analysis

Provide clear charts or interface screenshots together with business questions, and ask it to explain trends, identify layout relationships, or propose investigation paths. Deliverables can include analysis notes, anomaly-checking lists, and product improvement recommendations; when precise values are involved, also provide the raw data to avoid treating image-based estimates directly as statistical conclusions.

How to choose this model

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

Better suited for difficult tasks than GPT‑5.1

When a task requires cross-paragraph connections, multi-file fixes, or detailed visual reasoning, prioritize GPT‑5.2; official comparisons show improvements in these areas over GPT‑5.1 Thinking. If you already have simple rewriting or classification workflows with stable results, you can keep the original choice and use GPT‑5.2 for difficult cases, judging its value by accuracy and rework volume on real tasks.

Distinguish Thinking, Instant, and Pro

gpt-5.2 corresponds to Thinking; it is not an alternative spelling of Instant. Instant corresponds to gpt-5.2-chat-latest and is more oriented toward everyday work and learning. Pro is intended for difficult problems where users are willing to wait for higher-quality answers. This model is suitable for applications that need in-depth analysis while retaining reasoning control; the three versions should not be regarded as completely identical.

Get started

From a small-scale task to full integration.

01

Prepare tasks and materials

Define the objective, required inputs, and output requirements, using real business examples as a starting point.

02

Try it in the API playground

Open the playground page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.

03

Integrate according to the API documentation

Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.

Usage boundaries

Before formal use, understand output quality and capability limits.

  • Stronger visual understanding still does not equal precise measurement. Small text, low-resolution screenshots, dense charts, and approximate bounding boxes can all lead to misjudgments; some official visual evaluations also used Python tools. When precise coordinates or values are needed, use the original data and dedicated verification steps.
  • Tool-calling capability does not mean the model will independently run code, click interfaces, or obtain business data. Applications need to provide executable tools and return the results; generating a patch also does not mean it has passed testing. Table and presentation operations in ChatGPT should not be directly interpreted as meaning that a single text request will return a finished file.
  • Long-material analysis can still omit conditions or confuse similar content. It is recommended to retain section identifiers, require answers to include supporting evidence, and review key conclusions. For reading PDF content, first use extracted text or page images; image understanding is also not equivalent to image generation or speech output.

Frequently Asked Questions

Answers to common questions about using gpt-5.2.

Is gpt-5.2 the same as GPT‑5.2 Thinking?

Yes. OpenAI maps GPT‑5.2 Thinking to the API name gpt-5.2. Instant uses gpt-5.2-chat-latest, and Pro uses gpt-5.2-pro. When calling this model, select gpt-5.2; do not interpret it as Instant just because it belongs to the chat category.

How do I use GPT‑5.2 to analyze images?

You can provide both text and image_url in the message content of Chat Completions, and clearly specify the charts, areas, or relationships you want it to read. It is suitable for analyzing screenshots and technical diagrams and returns textual explanations; for small annotations, provide clear cropped images, and for important values, also provide text data.

When is it worth using xhigh?

Complex code diagnosis, comparing solutions with multiple constraints, and difficult reasoning are better suited to trying xhigh. It emphasizes reasoning quality rather than using the highest intensity for every simple question. Responses uses the reasoning configuration, while Chat Completions uses reasoning_effort; you can adjust them according to task difficulty and compare results.

How should I choose among the three options?

For existing conversational programs using messages, choose Chat Completions; if you want to organize interactions around input, reasoning, and tools, choose Responses. For simple Q&A or if you do not want to pass the full conversation history yourself, choose AI Chat, ask using question, and continue the conversation with stateful=true and the returned id.

Can GPT‑5.2 output JSON or directly perform fixes?

Chat Completions can be configured for JSON or JSON Schema output, which is suitable for delivering classification results, issue lists, and analysis fields; your program still needs to validate the content. Performing fixes requires the application to provide tools and permissions; the model generating code or a function call request does not mean the modification is complete or tests have passed.

Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.

Use it in your favorite tools

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

Connect gpt-5.2 to OpenAI SDK

OpenAI Chat Completions API

SDK installation, authentication and examples

Connect gpt-5.2 to OpenAI SDK · Responses

OpenAI Responses API

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