litellm/docs/my-website/docs/providers/xai.md

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xAI

https://docs.x.ai/docs

:::tip

We support ALL xAI models, just set model=xai/<any-model-on-xai> as a prefix when sending litellm requests

:::

Supported Models

Latest Release - Grok 4.1 Fast: Optimized for high-performance agentic tool calling with 2M context and prompt caching.

Model	Context	Features
xai/grok-4-1-fast-reasoning	2M tokens	Reasoning, Function calling, Vision, Audio, Web search, Caching
xai/grok-4-1-fast-non-reasoning	2M tokens	Function calling, Vision, Audio, Web search, Caching

When to use:

• ✅ Reasoning model: Complex analysis, planning, multi-step reasoning problems
• ✅ Non-reasoning model: Simple queries, faster responses, lower token usage

Example:

from litellm import completion

# With reasoning
response = completion(
    model="xai/grok-4-1-fast-reasoning",
    messages=[{"role": "user", "content": "Analyze this problem step by step..."}]
)

# Without reasoning
response = completion(
    model="xai/grok-4-1-fast-non-reasoning",
    messages=[{"role": "user", "content": "What's 2+2?"}]
)

---

All Available Models

Model Family	Model	Context	Features
Grok 4.1	xai/grok-4-1-fast-reasoning	2M	Reasoning, Tools, Vision, Audio, Web search, Caching
	xai/grok-4-1-fast-non-reasoning	2M	Tools, Vision, Audio, Web search, Caching
Grok 4	xai/grok-4	256K	Tools, Web search
	xai/grok-4-0709	256K	Tools, Web search
	xai/grok-4-fast-reasoning	2M	Reasoning, Tools, Web search
	xai/grok-4-fast-non-reasoning	2M	Tools, Web search
Grok 3	xai/grok-3	131K	Tools, Web search
	xai/grok-3-mini	131K	Tools, Web search
	xai/grok-3-fast-beta	131K	Tools, Web search
Grok Code	xai/grok-code-fast	256K	Reasoning, Tools, Code generation, Caching
Grok 2	xai/grok-2	131K	Tools, Vision
	xai/grok-2-vision-latest	32K	Tools, Vision

Features:

• Reasoning = Chain-of-thought reasoning with reasoning tokens
• Tools = Function calling / Tool use
• Web search = Live internet search
• Vision = Image understanding
• Audio = Audio input support
• Caching = Prompt caching for cost savings
• Code generation = Optimized for code tasks

Pricing: See xAI's pricing page for current rates.

API Key

# env variable
os.environ['XAI_API_KEY']

Sample Usage

from litellm import completion
import os

os.environ['XAI_API_KEY'] = ""
response = completion(
    model="xai/grok-3-mini-beta",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)
print(response)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['XAI_API_KEY'] = ""
response = completion(
    model="xai/grok-3-mini-beta",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    stream=True,
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)

for chunk in response:
    print(chunk)

Sample Usage - Vision

import os 
from litellm import completion

os.environ["XAI_API_KEY"] = "your-api-key"

response = completion(
    model="xai/grok-2-vision-latest",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://science.nasa.gov/wp-content/uploads/2023/09/web-first-images-release.png",
                        "detail": "high",
                    },
                },
                {
                    "type": "text",
                    "text": "What's in this image?",
                },
            ],
        },
    ],
)

Usage with LiteLLM Proxy Server

Here's how to call a XAI model with the LiteLLM Proxy Server

1. Modify the config.yaml

model_list:
  - model_name: my-model
    litellm_params:
      model: xai/<your-model-name>  # add xai/ prefix to route as XAI provider
      api_key: api-key                 # api key to send your model

2. Start the proxy

$ litellm --config /path/to/config.yaml

3. Send Request to LiteLLM Proxy Server

import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="my-model",
    messages = [
        {
            "role": "user",
            "content": "what llm are you"
        }
    ],
)

print(response)

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'

Reasoning Usage

LiteLLM supports reasoning usage for xAI models.

import litellm
response = litellm.completion(
    model="xai/grok-3-mini-beta",
    messages=[{"role": "user", "content": "What is 101*3?"}],
    reasoning_effort="low",
)

print("Reasoning Content:")
print(response.choices[0].message.reasoning_content)

print("\nFinal Response:")
print(completion.choices[0].message.content)

print("\nNumber of completion tokens (input):")
print(completion.usage.completion_tokens)

print("\nNumber of reasoning tokens (input):")
print(completion.usage.completion_tokens_details.reasoning_tokens)

import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="xai/grok-3-mini-beta",
    messages=[{"role": "user", "content": "What is 101*3?"}],
    reasoning_effort="low",
)

print("Reasoning Content:")
print(response.choices[0].message.reasoning_content)

print("\nFinal Response:")
print(completion.choices[0].message.content)

print("\nNumber of completion tokens (input):")
print(completion.usage.completion_tokens)

print("\nNumber of reasoning tokens (input):")
print(completion.usage.completion_tokens_details.reasoning_tokens)

Example Response:

Reasoning Content:
Let me calculate 101 multiplied by 3:
101 * 3 = 303.
I can double-check that: 100 * 3 is 300, and 1 * 3 is 3, so 300 + 3 = 303. Yes, that's correct.

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

Number of completion tokens (input):
14

Number of reasoning tokens (input):
310