--- title: "xAI" url: "/docs/providers/xai" canonical_url: "https://docs.litellm.ai/docs/providers/xai" type: "docs" last_updated: "2026-10-08" related: - "/docs/providers/watsonx/audio_transcription" - "/docs/providers/xai_realtime" --- # xAI > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt https://docs.x.ai/docs :::tip **我們支援所有 xAI 模型，只要在送出 litellm 請求時將 `model=xai/` 設為前綴即可** ::: ## 支援的模型 {#supported-models} **Grok 4.5** - 具有 500K context、reasoning（low/medium/high）、vision、tools、web search 與 prompt caching 的前沿模型，適用於 coding、agentic tasks 和 knowledge work。 | Model | Context | Features | |-------|---------|----------| | `xai/grok-4.5` | 500K tokens | **Reasoning**, Function calling, Vision, Web search, Caching | **範例：** ```python from litellm import completion response = completion( model="xai/grok-4.5", messages=[{"role": "user", "content": "Find and fix the bug, then explain it."}], reasoning_effort="high", # low | medium | high (default high) ) ``` **功能：** - **Reasoning** = 使用 reasoning tokens 的 chain-of-thought reasoning - **Tools** = Function calling / 工具使用 - **Web search** = 即時網際網路搜尋 - **Vision** = 圖像理解 - **Caching** = 供降低成本的 prompt caching - **Structured outputs** = JSON / schema 限制的回應 **價格：** 請參閱 [xAI 的價格頁面](https://docs.x.ai/docs/models) 以取得目前費率。 ## API 金鑰 {#api-key} ```python # env variable os.environ['XAI_API_KEY'] ``` ## 範例用法 {#sample-usage} ```python showLineNumbers title="LiteLLM python sdk usage - Non-streaming" from litellm import completion import os os.environ['XAI_API_KEY'] = "" response = completion( model="xai/grok-4.5", messages=[ { "role": "user", "content": "What's the weather like in Boston today in Fahrenheit?", } ], max_tokens=10, response_format={ "type": "json_object" }, seed=123, temperature=0.2, top_p=0.9, tool_choice="auto", tools=[], user="user", ) print(response) ``` ## 範例用法 - 串流 {#sample-usage---streaming} ```python showLineNumbers title="LiteLLM python sdk usage - Streaming" from litellm import completion import os os.environ['XAI_API_KEY'] = "" response = completion( model="xai/grok-4.5", 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, temperature=0.2, top_p=0.9, tool_choice="auto", tools=[], user="user", ) for chunk in response: print(chunk) ``` ## 範例用法 - Vision {#sample-usage---vision} ```python showLineNumbers title="LiteLLM python sdk usage - Vision" import os from litellm import completion os.environ["XAI_API_KEY"] = "your-api-key" response = completion( model="xai/grok-4.5", 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 {#usage-with-litellm-proxy-server} 以下說明如何使用 LiteLLM Proxy Server 呼叫 XAI 模型 1. 修改 config.yaml ```yaml showLineNumbers model_list: - model_name: my-model litellm_params: model: xai/ # add xai/ prefix to route as XAI provider api_key: api-key # api key to send your model ``` 2. 啟動 proxy ```bash $ litellm --config /path/to/config.yaml ``` 3. 將請求送至 LiteLLM Proxy Server **OpenAI Python v1.0.0+** ```python showLineNumbers import openai client = openai.OpenAI( api_key="sk-", # 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** ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \ --header "Authorization: Bearer $LITELLM_API_KEY" \ --header 'Content-Type: application/json' \ --data '{ "model": "my-model", "messages": [ { "role": "user", "content": "what llm are you" } ], }' ``` ## 推理用法 {#reasoning-usage} LiteLLM 支援 xAI 模型的推理用法。 **LiteLLM Python SDK** ```python showLineNumbers title="reasoning with xai/grok-4.5" import litellm response = litellm.completion( model="xai/grok-4.5", messages=[{"role": "user", "content": "What is 101*3?"}], reasoning_effort="low", # low | medium | high ) print("Reasoning Content:") print(response.choices[0].message.reasoning_content) print("\nFinal Response:") print(response.choices[0].message.content) print("\nNumber of completion tokens:") print(response.usage.completion_tokens) print("\nNumber of reasoning tokens:") print(response.usage.completion_tokens_details.reasoning_tokens) ``` **LiteLLM Proxy - OpenAI SDK 用法** ```python showLineNumbers title="reasoning with xai/grok-4.5" import openai client = openai.OpenAI( api_key="sk-", # 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-4.5", messages=[{"role": "user", "content": "What is 101*3?"}], reasoning_effort="low", # low | medium | high ) print("Reasoning Content:") print(response.choices[0].message.reasoning_content) print("\nFinal Response:") print(response.choices[0].message.content) print("\nNumber of completion tokens:") print(response.usage.completion_tokens) print("\nNumber of reasoning tokens:") print(response.usage.completion_tokens_details.reasoning_tokens) ``` **回應範例：** ```shell 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: 14 Number of reasoning tokens: 310 ``` ## Related pages - [WatsonX 音訊轉錄](https://docs.litellm.ai/docs/providers/watsonx/audio_transcription.md) - [xAI 語音代理程式（Realtime API）](https://docs.litellm.ai/docs/providers/xai_realtime.md)