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Reasoning effort

Reasoning models can spend extra tokens thinking through a problem before they answer. The OpenAI-compatible reasoning_effort parameter controls how much of that thinking the model does. Higher effort generally improves quality on hard tasks at the cost of more latency and output tokens. Pass reasoning_effort as a string on the chat completions request. Use a reasoning-capable model and a value it supports. 
from tinfoil import TinfoilAI

client = TinfoilAI(api_key="<YOUR_API_KEY>")

response = client.chat.completions.create(
    model="<MODEL_NAME>",
    reasoning_effort="medium",
    messages=[
        {"role": "user", "content": "What is 17 * 23? Think step by step."}
    ],
)

print(response.choices[0].message.content)
Swift’s ReasoningEffort enum covers none, minimal, low, medium, and high; pass other values with .customValue("xhigh"). Rust’s ReasoningEffort enum covers none, minimal, low, medium, high, and xhigh.

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Supported values per model

The accepted values differ by model. Sending an unsupported value returns a 400 error.
ModelTypeSupported reasoning_effort values
deepseek-v4-proChatnone, minimal, low, medium, high, xhigh, max
glm-5-2Chatnone, minimal, low, medium, high, xhigh, max
kimi-k2-6Chat / Visionnone, minimal, low, medium, high, xhigh, max
gemma4-31bChat / Visionnone, minimal, low, medium, high, xhigh, max
qwen3-vl-30bVisionnone, minimal, low, medium, high, xhigh, max
gpt-oss-120bChatlow, medium, high
gpt-oss-safeguard-120bSafetylow, medium, high
On the standard scale, none disables reasoning and effort increases up to max. The gpt-oss models use OpenAI’s Harmony response format, which defines only low, medium, and high; sending none, minimal, xhigh, or max to these models returns a 400 error. llama3-3-70b is not a reasoning model. It accepts the parameter without error but does not produce a reasoning trace.

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Reading the reasoning trace

The model’s thinking is returned in the reasoning field of the response message, separate from the final answer in content. Higher effort produces a longer trace. 
response = client.chat.completions.create(
    model="<MODEL_NAME>",
    reasoning_effort="high",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)

message = response.choices[0].message
print("Reasoning:", message.reasoning)
print("Answer:", message.content)
Available models and their supported values can change. Query the models endpoint to see which models report "reasoning": true.