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Home/Models/OpenAI/GPT-5.2 Chat
O

GPT-5.2 Chat

Input:$1.4/M
Output:$11.2/M
Context:128,000
Max Output:16,384
gpt-5.2-chat-latest is the Chat-optimized snapshot of OpenAI’s GPT-5.2 family (branded in ChatGPT as GPT-5.2 Instant). It is the model for interactive/chat use cases that need a blend of speed, long-context handling, multimodal inputs and reliable conversational behaviour.
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Overview
Features
Pricing
API

What is gpt-5.2-chat-latest

gpt-5.2-chat-latest is the ChatGPT-aligned snapshot of OpenAI’s GPT-5.2 family, offered as the recommended chat model for developers who want the ChatGPT experience in the API. It combines large-context chat behavior, structured outputs, tool/function calling, and multimodal understanding in a package tuned for interactive conversational workflows and applications. It is intended for most chat use cases where a high-quality, low-friction conversational model is required.

Basic information

  • Model name (API): gpt-5.2-chat-latest — described by OpenAI as the chat-oriented snapshot used by ChatGPT; recommended for chat use cases in the API.
  • Family / variants: Part of the GPT-5.2 family (Instant, Thinking, Pro). gpt-5.2-chat-latest is the ChatGPT snapshot optimized for chat-style interactions, while other GPT-5.2 variants (e.g., Thinking, Pro) trade latency for deeper reasoning or higher fidelity.
  • Input: Standard tokenized text for prompts and messages via the Chat/Responses API; supports function/tool calling (custom tools and constrained function-like outputs) and multimodal inputs where enabled by the API. Developers pass chat messages (role + content) or the Responses API inputs; the model accepts arbitrary text prompts and structured tool-call instructions.
  • Output: Tokenized natural language responses, structured JSON/function outputs when function-calling is used, and (where enabled) multimodal replies. The API supports parameters for reasoning effort/verbosity and structured return formats.
  • Knowledge cutoff: August 31, 2025 .

Main features (user-facing capabilities)

  • Chat-optimized dialog — tuned for interactive conversational flows, system messages, tool calls and low-latency responses appropriate to chat UIs.
  • Large long-context support for chat — 128k token context to support long conversations, documents, codebases, or agent memory. Useful for summarization, long-doc Q&A and multi-step agent workflows.
  • Improved tool & agent reliability — support for allowed-tools lists, custom tools, and stronger tool-calling reliability for multi-step tasks.
  • Reasoning controls — support for configurable reasoning effort levels (none, medium, high, xhigh on some GPT-5.2 variants) to trade latency and cost for deeper internal reasoning. Chat snapshot expects lower latency defaults.
  • Context compaction / Compact API — new APIs and compaction utilities to summarize and compress conversation state for long-running agents while preserving important facts. (Helps reduce token costs while keeping context fidelity).
  • Multimodality & vision improvements: enhanced image understanding and chart/screenshot reasoning compared with earlier generations (GPT-5.2 family is promoted for stronger multimodal capability).

Representative production use cases (where chat-latest shines)

  • Interactive assistants for knowledge workers: long conversation continuity (meeting notes, policy drafting, contract Q&A) that need preserved context across many turns (128k tokens).
  • Customer support agents & internal tools: chat-first deployments that require tool calls (search, CRM lookups) with allowed-tools safety controls.
  • Multimodal help desks: image + chat workflows (e.g., screenshot triage, annotated diagrams) using images-as-input capability.
  • Coding helpers embedded in IDEs: fast, chat-oriented code completions and debugging help (use chat snapshot for low-latency interactions, Thinking/Pro for heavyweight verification).
  • Long-document summarization & review: legal or technical documents spanning many pages—compact API and 128k context help keep context fidelity and reduce token costs.

How to access and use GPT-5.2 chat API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to GPT-5.2 chat API

Select the “gpt-5.2-chat-latest” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account.Compatibility with the Chat/Responses-style APIs.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

See also Gemini 3 Pro Preview API

FAQ

What is the difference between GPT-5.2 Chat and standard GPT-5.2?

GPT-5.2 Chat (gpt-5.2-chat-latest) is the same snapshot used in ChatGPT, optimized for interactive conversation with a 128K context window and 16K max output, while GPT-5.2 offers 400K context and 128K output for API-focused workloads.

Is GPT-5.2 Chat Latest suitable for production API use?

OpenAI recommends standard GPT-5.2 for most API usage, but GPT-5.2 Chat Latest is useful for testing ChatGPT-specific improvements and building conversational interfaces that mirror the ChatGPT experience.

Does GPT-5.2 Chat Latest support function calling and structured outputs?

Yes, GPT-5.2 Chat Latest fully supports both function calling and structured outputs, making it suitable for building chat applications with tool integration and predictable response formats.

What is the context window limitation of GPT-5.2 Chat Latest?

GPT-5.2 Chat Latest has a 128K token context window with 16K max output tokens—smaller than GPT-5.2's 400K/128K—reflecting its optimization for real-time conversational use rather than massive document processing.

Does GPT-5.2 Chat Latest support caching for cost optimization?

Yes, GPT-5.2 Chat Latest supports cached input tokens at $0.175 per million (10x cheaper than regular input), making it cost-effective for applications with repeated context like system prompts.

Features for GPT-5.2 Chat

Explore the key features of GPT-5.2 Chat, designed to enhance performance and usability. Discover how these capabilities can benefit your projects and improve user experience.

Pricing for GPT-5.2 Chat

Explore competitive pricing for GPT-5.2 Chat, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how GPT-5.2 Chat can enhance your projects while keeping costs manageable.
Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Input:$1.4/M
Output:$11.2/M
Input:$1.75/M
Output:$14/M
-20%

Sample code and API for GPT-5.2 Chat

gpt-5.2-chat-latest is OpenAI’s Instant/Chat-tuned snapshot of the GPT-5.2 family (the ChatGPT-facing “Instant” variant) optimized for conversational/chat workloads, low-latency developer use, and broad ChatGPT integration.
POST
/v1/chat/completions
POST
/v1/responses
Python
JavaScript
Curl
from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

response = client.responses.create(
    model="gpt-5.2-chat-latest",
    input="How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
)

print(response.output_text)

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

response = client.responses.create(
    model="gpt-5.2-chat-latest",
    input="How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
)

print(response.output_text)

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token, and paste it here
const COMETAPI_KEY = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const BASE_URL = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey: COMETAPI_KEY,
  baseURL: BASE_URL,
});

async function main() {
  const response = await client.responses.create({
    model: "gpt-5.2-chat-latest",
    input: "How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
  });

  console.log(response.output_text);
}

main();

Curl Code Example

curl https://api.cometapi.com/v1/responses \
     --header "Authorization: Bearer $COMETAPI_KEY" \
     --header "content-type: application/json" \
     --data \
'{
    "model": "gpt-5.2-pro",
    "input": "How much gold would it take to coat the Statue of Liberty in a 1mm layer?",
    "reasoning": {
        "effort": "high"
    }
}'

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