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
LiteLLM python sdk usage - Non-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?",
}
],
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
LiteLLM python sdk 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
LiteLLM python sdk 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
- 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
- Start the proxy
$ litellm --config /path/to/config.yaml
- Send Request to LiteLLM Proxy Server
- OpenAI Python v1.0.0+
- curl
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.
- LiteLLM Python SDK
- LiteLLM Proxy - OpenAI SDK Usage
reasoning with xai/grok-3-mini-beta
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)
reasoning with xai/grok-3-mini-beta
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