Support the Openai Responses API multi-function interface, the following functions have been launched:
  • Text input: Text input
  • Image input: Image input
  • Streaming: Streaming
  • Web search: Web search
  • Deep research: For complex analysis and research tasks
  • Reasoning: Reasoning depth control, supports 4 levels (minimal / low / medium /high). Only the gpt-5 series supports minimal.
  • Verbosity: Output length, gpt-5 series supports 3 levels (low / medium /high)
  • Functions: Functions
  • image_generation tool usage: Image drawing and generation are billed under gpt-image-1.
  • Code Interpreter: Allow models to write and run Python to solve problems
  • Remote MCP: Calling a remote MCP server
  • Computer Use: Computer Use

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Usage (Python call)：

Same as the official OpenAI call method, just replace api_key and base_url for forwarding. Mainland China can access directly. 
client = OpenAI(
    api_key="AIHUBMIX_API_KEY", # Replace with the key you generated in AiHubMix
    base_url="https://aihubmix.com/v1"
)
  1. For inference models, the output inference summary can be controlled using the following parameter, with the detail richness of the summary ranked as detailed > auto > None, where auto provides the best balance.
"summary": "auto"
  1. Optional deep reasoning models: ‎⁠o3-deep-research⁠ and ‎⁠o4-mini-deep-research⁠, only supported on the ‎⁠responses⁠ endpoint.
  2. The gpt-5 series focuses on stable reasoning and consistent outputs, and no longer supports the temperature and top_p parameters for controlling randomness. If you need more freedom, you can try gpt-5-chat-latest, which supports temperature.
  3. Reasoning models (o series / gpt-5 series) have deprecated max_tokens. Please use max_completion_tokens for completions or max_output_tokens for responses to explicitly set the output token limit.
from openai import OpenAI

client = OpenAI(
    api_key="sk-***", # Replace with the key generated in your AIHubMix dashboard
    base_url="https://aihubmix.com/v1"
)

response = client.responses.create(
    model="gpt-5", # gpt-5, gpt-5-chat-latest, gpt-5-mini, gpt-5-nano
    input="Why is tarot divination effective? What are the underlying principles and transferable methods? Output format: Markdown", # GPT-5 does not output in Markdown format by default, so you need to explicitly specify it.
    reasoning={
        "effort": "minimal" # Reasoning depth – Controls how many reasoning tokens the model generates before producing a response. Value can be "minimal", "low", "medium" or "high". Default is "medium".
    },
    text={
        "verbosity": "low" # Output length – Verbosity determines how many output tokens are generated. Value can be "low", "medium", or "high". Models before GPT-5 defaulted to "medium" verbosity.
    },
    stream=True
)

for event in response:
  print(event)
Note:
  1. The latest codex-mini-latest does not support search.
  2. The Computer use feature requires integration with Playwright. It’s recommended to refer to the official repository.
Known issues:
  • Use cases are complex to invoke
  • Takes many screenshots, which is time-consuming and often unreliable
  • May trigger CAPTCHA or Cloudflare human verification, potentially leading to infinite loops