> ## Documentation Index
> Fetch the complete documentation index at: https://doc.tokendog.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Reasoning

> Use reasoning models with reasoning_effort

Reasoning models think more deeply before answering — good for math, code, and multi-step tasks. Use `reasoning_effort` to control depth (e.g. `low` / `medium` / `high`).

<CodeGroup>
  ```bash curl theme={null}
  curl https://tokendog.io/v1/chat/completions \
    -H "Authorization: Bearer $TOKENDOG_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model":"gpt-5.1",
      "reasoning_effort":"high",
      "messages":[{"role":"user","content":"A number plus half of itself equals 36. Find the number."}]
    }'
  ```

  ```python Python theme={null}
  from openai import OpenAI
  client = OpenAI(base_url="https://tokendog.io/v1", api_key="YOUR_TOKENDOG_API_KEY")
  resp = client.chat.completions.create(
      model="gpt-5.1",
      reasoning_effort="high",
      messages=[{"role":"user","content":"A number plus half of itself equals 36. Find the number."}],
  )
  print(resp.choices[0].message.content)
  ```

  ```javascript Node theme={null}
  import OpenAI from "openai";
  const client = new OpenAI({ baseURL:"https://tokendog.io/v1", apiKey:"YOUR_TOKENDOG_API_KEY" });
  const resp = await client.chat.completions.create({
    model:"gpt-5.1",
    reasoning_effort:"high",
    messages:[{role:"user",content:"A number plus half of itself equals 36. Find the number."}],
  });
  console.log(resp.choices[0].message.content);
  ```
</CodeGroup>

<Tip>Higher effort means deeper thinking but more latency and cost; use `low` for simple tasks.</Tip>