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GPT 5.2 High
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GPT 5.2 High

gpt-5.2-highllms.txt
OpenAI
GPT-5.2 supports configurable reasoning effort only through the /responses endpoint. To make higher-intensity reasoning available directly via the /chat interface, GPT-5.2-High is provided as a reasoning-enhanced variant of GPT-5.2 with reasoning_effort preset to high. It is designed for tasks that require deeper analysis, stronger result consistency, and greater controllability. By applying more aggressive reasoning strategies and more effective use of extended context, the model delivers clearer and more reliable responses, making it well suited for complex agent workflows, long-chain decision-making, and reliability-critical advanced applications.

Pricing

PricingCache ReadImage GenerationWeb Search
$1.75$14
$0.175/M tokens-$0.01/request

Input Modalities

  • Text
  • Vision
  • PDF

Output Modalities

  • Text

Context length

  • 400K tokens

Max output

  • 128K tokens

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="gpt-5.2-high",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is GPT 5.2 High?

GPT-5.2 supports configurable reasoning effort only through the /responses endpoint. To make higher-intensity reasoning available directly via the /chat interface, GPT-5.2-High is provided as a reasoning-enhanced variant of GPT-5.2 with reasoning_effort preset to high. It is designed for tasks that require deeper analysis, stronger result consistency, and greater controllability. By applying more aggressive reasoning strategies and more effective use of extended context, the model delivers clearer and more reliable responses, making it well suited for complex agent workflows, long-chain decision-making, and reliability-critical advanced applications.

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