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 conversation, it is better suited for complex code fixes, cross-material synthesis, chart interpretation, and projects with clear delivery requirements.
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.acedata.cloudgpt-5.2import 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.
Clarify capacity, inputs and outputs, and invocation methods before choosing a model.
Thinking positioning and xhigh are native capabilities of GPT-5.2; applications provide tasks and relevant history according to the structure of Chat Completions or Responses, without mixing the two sets of parameters and result parsing.
Learn what gpt-5.2 can bring to your work.
GPT‑5.2's strength with long context is not limited to summarization; it also connects conditions, exceptions, and conclusions scattered across different sections. When handling reports, contract text, 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 assembling conclusions afterward.
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 is enhanced in software engineering and frontend development, making it especially suitable for tasks involving multiple files, interface constraints, or complex interfaces, rather than merely completing short code snippets.
When faced with dashboards, product screenshots, and technical diagrams, GPT‑5.2 can analyze visual content together with textual requirements, explaining chart relationships and interface layouts. After connecting business functions, it can also continue reasoning based on tool results and organize subsequent steps, making it suitable for linking information retrieval, analysis, and final answers into a clear workflow.
Start with specific tasks to find where the model can be effective.
Provide contract text with section labels, research excerpts, and comparison objectives, and have the model deliver a table of clause differences, key viewpoints, and citation locations. Define 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.
Submit relevant files, reproduction steps, logs, and expected behavior, and ask it to explain the failure chain first, then output code changes and verification 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 notes, and a test checklist, with the actual execution verification completed in the development environment.
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, provide the raw data as well to avoid treating image-based estimates directly as statistical conclusions.
Choose based on task complexity, input materials, and expected results.
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 your existing choice and use GPT‑5.2 for difficult cases, judging its value by accuracy and rework on real tasks.
gpt-5.2 corresponds to Thinking; it is not another spelling of Instant. Instant corresponds to gpt-5.2-chat-latest and is more oriented toward everyday work and study. Pro is for difficult problems where you 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 treated as completely identical.
Based on the characteristics of gpt-5.2, first validate a small task whose results can be checked.
You can ask directly: synthesize reports, data-table text, and charts into a decision memo. List data definitions, contradictions, and missing information, with each conclusion matched to its supporting evidence.
Specify material versions and metric definitions; verify facts, numbers, and citation locations across materials.
Use the full model ID gpt-5.2, first confirm the public request format and available parameters on the API page, then connect your 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 limits.
Answers to common questions about using gpt-5.2.
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 merely because it belongs to the chat category.
You can provide both text and image_url in the message content of Chat Completions, and clearly specify the charts, regions, or relationships you want it to read. It is suitable for analyzing screenshots and technical diagrams, returning textual explanations; for small annotations, provide clear cropped images, and for important values, also provide text data.
Complex code diagnosis, comparing solutions with multiple constraints, and difficult reasoning are better suited to trying xhigh. It emphasizes reasoning quality rather than applying the highest intensity to every simple question. Responses uses the reasoning configuration, while Chat Completions uses reasoning_effort; adjust them according to task difficulty and compare results.
Use Chat Completions or Responses and provide the complete model ID. Chat Completions uses messages and choices, while Responses uses input and its corresponding response structure; handle history management, streaming events, and tool parameters separately according to the selected interface, and do not mix the two formats.
Chat Completions can be configured for JSON or JSON Schema output, making it 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 that the modification has been completed or that tests have passed.
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
$0.35per 1M tokens
Official $1.75
Output
$2.80per 1M tokens
Official $14.00
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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