Reasoning

presencePenalty, frequencyPenalty and stop parameters are not supported by reasoning models. Adding them in the request would result in an error.

Key Features

Think Before Responding: Thinks through problems step-by-step before delivering an answer.
Math & Quantitative Strength: Excels at numerical challenges and logic puzzles.
Reasoning Trace: Usage metrics expose reasoning_tokens. Some models can also return encrypted reasoning via include: ["reasoning.encrypted_content"] (see below).

In Chat Completions, only grok-3-mini returns message.reasoning_content.

grok-3, grok-4 and grok-4-fast-reasoning do not return reasoning_content. If supported, you can request encrypted reasoning content via include: ["reasoning.encrypted_content"] in the Responses API instead.

Encrypted Reasoning Content

For grok-4, the reasoning content is encrypted by us and can be returned if you pass include: ["reasoning.encrypted_content"] to the Responses API. You can send the encrypted content back to provide more context to a previous conversation. See Adding encrypted thinking content for more details on how to use the content.

Control how hard the model thinks

reasoning_effort is not supported by grok-3, grok-4 and grok-4-fast-reasoning. Specifying reasoning_effort parameter will get an error response. Only grok-3-mini supports reasoning_effort.

The reasoning_effort parameter controls how much time the model spends thinking before responding. It must be set to one of these values:

low: Minimal thinking time, using fewer tokens for quick responses.
high: Maximum thinking time, leveraging more tokens for complex problems.

Choosing the right level depends on your task: use low for simple queries that should complete quickly, and high for harder problems where response latency is less important.

Usage Example

Here’s a simple example using grok-3-mini to multiply 101 by 3.

import os

from xai_sdk import Client
from xai_sdk.chat import system, user

client = Client(
    api_key=os.getenv("XAI_API_KEY"),
    timeout=3600, # Override default timeout with longer timeout for reasoning models
)

chat = client.chat.create(
    model="grok-3-mini",
    reasoning_effort="high",
    messages=[system("You are a highly intelligent AI assistant.")],
)
chat.append(user("What is 101*3?"))

response = chat.sample()

print("Final Response:")
print(response.content)

print("Number of completion tokens:")
print(response.usage.completion_tokens)

print("Number of reasoning tokens:")
print(response.usage.reasoning_tokens)

Sample Output

Output


Final Response:
The result of 101 multiplied by 3 is 303.

Number of completion tokens:
14

Number of reasoning tokens:
310

Notes on Consumption

When you use a reasoning model, the reasoning tokens are also added to your final consumption amount. The reasoning token consumption will likely increase when you use a higher reasoning_effort setting.