---
title: "indigo-owl-4"
description: "Reasoning model for complex multi-step problem solving"
---

`indigo-owl-4` is Liutong's reasoning model. It is optimized for tasks that require careful, multi-step thinking — math, logic, code analysis, and structured problem solving.

## Capabilities

- Complex mathematical reasoning and proofs
- Multi-step logical deduction
- Code review and architectural analysis
- Scientific reasoning and hypothesis testing
- Strategic planning and decision analysis

## When to use indigo-owl-4

Use this model when accuracy on hard problems matters more than speed. `indigo-owl-4` takes additional time to "think" through problems before responding, which produces significantly better results on challenging tasks.

For simple Q&A or conversation, [`crimson-falcon-4`](/docs/models/crimson-falcon-4) is faster and more cost-effective.

## API usage

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.liutong.llby.org/v1",
    api_key="lt_your_api_key",
)

response = client.chat.completions.create(
    model="indigo-owl-4",
    messages=[
        {"role": "user", "content": "Prove that the square root of 2 is irrational."},
    ],
)

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

## Parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `max_tokens` | int | — | Maximum tokens to generate. |
| `stream` | bool | false | Enable streaming responses. |

> **Note:** Reasoning models may not support all parameters that chat models do (e.g., `temperature` may be fixed). The model manages its own reasoning strategy internally.

## Endpoint

`POST /v1/chat/completions`

See the full [Chat Completions API reference](/docs/api-reference/chat-completions).