reasoning_effort to control depth (e.g. low / medium / high).
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."}]
}'
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)
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);
Higher effort means deeper thinking but more latency and cost; use
low for simple tasks.