Skip to main content

Overview

Reasoning models are advanced language models optimized for complex problem-solving tasks. By generating detailed reasoning steps (chain-of-thought), they improve the accuracy of answers in analytical scenarios.

Typical Use Cases

  • Complex Problem Solving: Suitable for tasks requiring step-by-step logic, such as math or scientific reasoning.
  • Decision Support Systems: Helps explain the logic behind conclusions by providing detailed reasoning processes.
  • Education and Training: Assists learners in understanding complex concepts by presenting derivation processes clearly.

Installation & Setup

Before using reasoning models, make sure the latest OpenAI SDK is installed:

API Usage

Use the /chat/completions endpoint to invoke reasoning models.

Request Parameters

  • max_tokens: Sets the maximum number of tokens the model can return.
  • temperature: Recommended between 0.5 and 0.7 (suggested: 0.6) to balance creativity and logic.
  • top_p: Recommended value is 0.95.

Example Code

Streaming Response

Non-Streaming Response


Context Management

Reasoning outputs are not automatically carried over to the next round of dialogue. You must manually maintain the message history:

Supported Models

The following reasoning models are currently supported on the Novita platform:

Billing

  • Billing is based on the number of tokens for both input and output.
  • Please refer to each model’s pricing page for specific billing rules and token conversion details.

Notes & Best Practices

  • Avoid placing reasoning instructions in the system message. Instead, make the intent explicit in the user message.
  • For mathematical tasks, clearly instruct the model, e.g., “Please reason step by step and provide a final answer.”
  • To prevent the model from skipping reasoning steps, consider asking for a newline before the final answer.
Last modified on November 7, 2025