xAI
提示
我們支援所有 xAI 模型，只要在送出 litellm 請求時將 model=xai/<any-model-on-xai> 設為前綴即可
支援的模型
最新版本 - Grok 4.1 Fast：針對高效能 agentic 工具呼叫最佳化，具備 2M context 與 prompt caching。
| 模型 | 上下文 | 功能 |
|---|---|---|
xai/grok-4-1-fast-reasoning | 2M tokens | 推理, Function calling, Vision, Audio, Web search, Caching |
xai/grok-4-1-fast-non-reasoning | 2M tokens | Function calling, Vision, Audio, Web search, Caching |
何時使用：
- ✅ 推理模型：複雜分析、規劃、多步驟推理問題
- ✅ 非推理模型：簡單查詢、更快的回應、更低的 token 用量
範例：
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?"}]
)
所有可用模型
| 模型系列 | 模型 | 上下文 | 功能 |
|---|---|---|---|
| Grok 4.1 | xai/grok-4-1-fast-reasoning | 2M | 推理, 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 | 工具、網路搜尋 |
xai/grok-4-0709 | 256K | 工具、網路搜尋 | |
xai/grok-4-fast-reasoning | 2M | 推理, Tools, Web search | |
xai/grok-4-fast-non-reasoning | 2M | 工具、網路搜尋 | |
| Grok 3 | xai/grok-3 | 131K | 工具、網路搜尋 |
xai/grok-3-mini | 131K | 工具、網路搜尋 | |
xai/grok-3-fast-beta | 131K | 工具、網路搜尋 | |
| Grok Code | xai/grok-code-fast | 256K | 推理, Tools, Code generation, Caching |
| Grok 2 | xai/grok-2 | 131K | Tools, Vision |
xai/grok-2-vision-latest | 32K | Tools, Vision |
功能：
- 推理 = 具備 reasoning tokens 的 chain-of-thought 推理
- Tools = Function calling / Tool use
- Web search = 即時網路搜尋
- Vision = 圖像理解
- Audio = 支援音訊輸入
- Caching = 用於節省成本的 prompt caching
- Code generation = 針對程式碼任務最佳化
價格： 請參閱 xAI 的價格頁面 以取得目前費率。
API 金鑰
# env variable
os.environ['XAI_API_KEY']
範例用法
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)
範例用法 - 串流
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)
範例用法 - 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?",
},
],
},
],
)
與 LiteLLM Proxy Server 搭配使用
以下說明如何使用 LiteLLM Proxy Server 呼叫 XAI 模型
- 修改 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
- 啟動 proxy
$ litellm --config /path/to/config.yaml
- 將請求送至 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"
}
],
}'
推理用法
LiteLLM 支援 xAI 模型的推理用法。
- LiteLLM Python SDK
- LiteLLM Proxy - OpenAI SDK 用法
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)
回應範例：
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