---
title: "Vertex AI - Anthropic、DeepSeek、Model Garden、xAI"
url: "/docs/providers/vertex_partner"
canonical_url: "https://docs.litellm.ai/docs/providers/vertex_partner"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/providers/vertex_ai/videos"
  - "/docs/providers/vertex_self_deployed"
---
# Vertex AI - Anthropic、DeepSeek、Model Garden、xAI

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


## 支援的合作夥伴提供者 {#supported-partner-providers}

| 提供者 | LiteLLM 路由 | Vertex 文件 |
|----------|---------------|---------------|
| Anthropic (Claude) | `vertex_ai/claude-*` | [Vertex AI - Anthropic 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/use-claude) |
| DeepSeek | `vertex_ai/deepseek-ai/{MODEL}` | [Vertex AI - DeepSeek 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/deepseek) |
| ZAI (GLM) | `vertex_ai/zai-org/{MODEL}` | [Vertex AI - GLM 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/zaiorg/glm-47) |
| Meta/Llama | `vertex_ai/meta/{MODEL}` | [Vertex AI - Meta 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/llama) |
| Mistral | `vertex_ai/mistral-*` | [Vertex AI - Mistral 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) |
| AI21 (Jamba) | `vertex_ai/jamba-*` | [Vertex AI - AI21 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/ai21) |
| Qwen | `vertex_ai/qwen/*` | [Vertex AI - Qwen 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) |
| OpenAI (GPT-OSS) | `vertex_ai/openai/gpt-oss-*` | [Vertex AI - GPT-OSS 模型](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) |
| xAI (Grok) | `vertex_ai/xai/{MODEL}` | [xAI 模型（含 Vertex）](https://docs.x.ai/docs/models), [Vertex AI Model Garden](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models) |

## Vertex AI - Anthropic (Claude) {#vertex-ai---anthropic-claude}

| 模型名稱       | 函式呼叫                        |
|------------------|--------------------------------------|
| claude-sonnet-4-5@20250929   | `completion('vertex_ai/claude-sonnet-4-5@20250929', messages)` |
| claude-3-opus@20240229   | `completion('vertex_ai/claude-3-opus@20240229', messages)` |
| claude-3-5-sonnet@20240620  | `completion('vertex_ai/claude-3-5-sonnet@20240620', messages)` |
| claude-3-sonnet@20240229   | `completion('vertex_ai/claude-3-sonnet@20240229', messages)` |
| claude-3-haiku@20240307   | `completion('vertex_ai/claude-3-haiku@20240307', messages)` |
| claude-3-7-sonnet@20250219   | `completion('vertex_ai/claude-3-7-sonnet@20250219', messages)` |

#### 使用方式 {#usage}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "claude-sonnet-5"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    temperature=0.7,
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: anthropic-vertex
      litellm_params:
        model: vertex_ai/claude-sonnet-5
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: anthropic-vertex
      litellm_params:
        model: vertex_ai/claude-sonnet-5
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "anthropic-vertex", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

#### 使用方式 - `thinking` / `reasoning_content` {#usage---thinking--reasoning_content}

**SDK**

```python
from litellm import completion

resp = completion(
    model="vertex_ai/claude-sonnet-5",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    thinking={"type": "enabled", "budget_tokens": 1024},
)

```

**PROXY**

1. 設定 config.yaml

```yaml
- model_name: claude-sonnet-5
  litellm_params:
    model: vertex_ai/claude-sonnet-5
    vertex_ai_project: "my-test-project"
    vertex_ai_location: "us-west-1"
```

2. 啟動 proxy

```bash
litellm --config /path/to/config.yaml
```

3. 測試！ 

```bash
curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -d '{
    "model": "claude-sonnet-5",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "thinking": {"type": "enabled", "budget_tokens": 1024}
  }'
```

**預期回應**

```python
ModelResponse(
    id='chatcmpl-c542d76d-f675-4e87-8e5f-05855f5d0f5e',
    created=1740470510,
    model='claude-sonnet-5',
    object='chat.completion',
    system_fingerprint=None,
    choices=[
        Choices(
            finish_reason='stop',
            index=0,
            message=Message(
                content="The capital of France is Paris.",
                role='assistant',
                tool_calls=None,
                function_call=None,
                provider_specific_fields={
                    'citations': None,
                    'thinking_blocks': [
                        {
                            'type': 'thinking',
                            'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                            'signature': 'EuYBCkQYAiJAy6...'
                        }
                    ]
                }
            ),
            thinking_blocks=[
                {
                    'type': 'thinking',
                    'thinking': 'The capital of France is Paris. This is a very straightforward factual question.',
                    'signature': 'EuYBCkQYAiJAy6AGB...'
                }
            ],
            reasoning_content='The capital of France is Paris. This is a very straightforward factual question.'
        )
    ],
    usage=Usage(
        completion_tokens=68,
        prompt_tokens=42,
        total_tokens=110,
        completion_tokens_details=None,
        prompt_tokens_details=PromptTokensDetailsWrapper(
            audio_tokens=None,
            cached_tokens=0,
            text_tokens=None,
            image_tokens=None
        ),
        cache_creation_input_tokens=0,
        cache_read_input_tokens=0
    )
)
```

## VertexAI DeepSeek {#vertexai-deepseek}

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/deepseek-ai/{MODEL}` |
| Vertex 文件 | [Vertex AI - DeepSeek 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/deepseek) |

#### 使用方式 {#usage-1}

**LiteLLM 支援所有 Vertex AI DeepSeek 模型。** 請確保您對所有 Vertex AI DeepSeek 模型使用 `vertex_ai/deepseek-ai/` 前綴。

| 模型名稱 | 使用方式 |
|------------------|------------------------------|
| vertex_ai/deepseek-ai/deepseek-r1-0528-maas | `completion('vertex_ai/deepseek-ai/deepseek-r1-0528-maas', messages)` |

## VertexAI ZAI (GLM) {#vertexai-zai-glm}

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/zai-org/{MODEL}` |
| Vertex 文件 | [Vertex AI - GLM 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/zaiorg/glm-47) |

**LiteLLM 支援所有 Vertex AI GLM 模型。** 請確保您對所有 Vertex AI GLM 模型使用 `vertex_ai/zai-org/` 前綴。

| 模型名稱 | 使用方式 |
|------------|-------|
| vertex_ai/zai-org/glm-4.7-maas | `completion('vertex_ai/zai-org/glm-4.7-maas', messages)` |

#### 使用方式 {#usage-2}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

response = completion(
    model="vertex_ai/zai-org/glm-4.7-maas",
    messages=[{"role": "user", "content": "hi"}],
    vertex_project="your-vertex-project",
    # vertex_location routes to "global"
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
  - model_name: glm-4.7
    litellm_params:
      model: vertex_ai/zai-org/glm-4.7-maas
      vertex_project: "my-project"
      # vertex_location routes to "global"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "glm-4.7",
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

## VertexAI Meta/Llama API {#vertexai-metallama-api}
 
| 模型名稱 | 函式呼叫 |
|------------------|--------------------------------------|
| meta/llama-3.2-90b-vision-instruct-maas | `completion('vertex_ai/meta/llama-3.2-90b-vision-instruct-maas', messages)` |
| meta/llama3-8b-instruct-maas | `completion('vertex_ai/meta/llama3-8b-instruct-maas', messages)` |
| meta/llama3-70b-instruct-maas | `completion('vertex_ai/meta/llama3-70b-instruct-maas', messages)` |
| meta/llama3-405b-instruct-maas   | `completion('vertex_ai/meta/llama3-405b-instruct-maas', messages)` |
| meta/llama-4-scout-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-17b-16e-instruct-maas', messages)` |
| meta/llama-4-scout-17-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-scout-128b-16e-instruct-maas', messages)` |
| meta/llama-4-maverick-17b-128e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-128e-instruct-maas',messages)` |
| meta/llama-4-maverick-17b-16e-instruct-maas | `completion('vertex_ai/meta/llama-4-maverick-17b-16e-instruct-maas',messages)` |

#### 使用方式 {#usage-3}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "meta/llama3-405b-instruct-maas"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: anthropic-llama
      litellm_params:
        model: vertex_ai/meta/llama3-405b-instruct-maas
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: anthropic-llama
      litellm_params:
        model: vertex_ai/meta/llama3-405b-instruct-maas
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "anthropic-llama", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

## VertexAI Mistral API {#vertexai-mistral-api}

[**支援的 OpenAI 參數**](https://github.com/BerriAI/litellm/blob/e0f3cd580cb85066f7d36241a03c30aa50a8a31d/litellm/llms/openai.py#L137)

**LiteLLM 支援所有 Vertex AI Mistral 模型。** 請確保您對所有 Vertex AI Mistral 模型使用 `vertex_ai/mistral-` 前綴。

概觀

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/mistral-{MODEL}` |
| Vertex 文件 | [Vertex AI - Mistral 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral) |
 
| 模型名稱 | 函式呼叫 |
|------------------|--------------------------------------|
| mistral-large@latest   | `completion('vertex_ai/mistral-large@latest', messages)` |
| mistral-large@2407   | `completion('vertex_ai/mistral-large@2407', messages)` |
| mistral-small-2503   | `completion('vertex_ai/mistral-small-2503', messages)` |
| mistral-large-2411   | `completion('vertex_ai/mistral-large-2411', messages)` |
| mistral-nemo@latest   | `completion('vertex_ai/mistral-nemo@latest', messages)` |
| codestral@latest   | `completion('vertex_ai/codestral@latest', messages)` |
| codestral@@2405   | `completion('vertex_ai/codestral@2405', messages)` |

#### 使用方式 {#usage-4}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "mistral-large@2407"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: vertex-mistral
      litellm_params:
        model: vertex_ai/mistral-large@2407
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: vertex-mistral
      litellm_params:
        model: vertex_ai/mistral-large@2407
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "vertex-mistral", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

#### 使用方式 - Codestral FIM {#usage---codestral-fim}

透過 OpenAI [`/v1/completion`](https://platform.openai.com/docs/api-reference/completions/create) 端點在 VertexAI 上呼叫 Codestral 以執行 FIM 任務。 

注意：您也可以透過 `/chat/completion` 呼叫 Codestral。

**SDK**

```python
from litellm import text_completion
import os

# os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""
# OR run `!gcloud auth print-access-token` in your terminal

model = "codestral@2405"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = text_completion(
    model="vertex_ai/" + model,
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
    prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():", 
    suffix="return True",                                              # optional
    temperature=0,                                                     # optional
    top_p=1,                                                           # optional
    max_tokens=10,                                                     # optional
    min_tokens=10,                                                     # optional
    seed=10,                                                           # optional
    stop=["return"],                                                   # optional
)

print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: vertex-codestral
      litellm_params:
        model: vertex_ai/codestral@2405
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: vertex-codestral
      litellm_params:
        model: vertex_ai/codestral@2405
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl -X POST 'http://0.0.0.0:4000/completions' \
      -H "Authorization: Bearer $LITELLM_API_KEY" \
      -H 'Content-Type: application/json' \
      -d '{
            "model": "vertex-codestral", # 👈 the 'model_name' in config
            "prompt": "def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():", 
            "suffix":"return True",                                              # optional
            "temperature":0,                                                     # optional
            "top_p":1,                                                           # optional
            "max_tokens":10,                                                     # optional
            "min_tokens":10,                                                     # optional
            "seed":10,                                                           # optional
            "stop":["return"],                                                   # optional
        }'
```

## VertexAI AI21 Models {#vertexai-ai21-models}
 
| 模型名稱 | 函式呼叫 |
|------------------|--------------------------------------|
| jamba-1.5-mini@001   | `completion(model='vertex_ai/jamba-1.5-mini@001', messages)` |
| jamba-1.5-large@001   | `completion(model='vertex_ai/jamba-1.5-large@001', messages)` |

#### 使用方式 {#usage-5}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "jamba-1.5-mini@001"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: jamba-1.5-mini
      litellm_params:
        model: vertex_ai/jamba-1.5-mini@001
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: jamba-1.5-large
      litellm_params:
        model: vertex_ai/jamba-1.5-large@001
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "jamba-1.5-large",
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

## VertexAI Qwen API {#vertexai-qwen-api}

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/qwen/{MODEL}` |
| Vertex 文件 | [Vertex AI - Qwen 模型](https://cloud.google.com/vertex-ai/generative-ai/docs/maas/qwen) |

**LiteLLM 支援所有 Vertex AI Qwen 模型。** 請確保您對所有 Vertex AI Qwen 模型使用 `vertex_ai/qwen/` 前綴。

| 模型名稱 | 使用方式 |
|------------------|------------------------------|
| vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas | `completion('vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas', messages)` |
| vertex_ai/qwen/qwen3-235b-a22b-instruct-2507-maas | `completion('vertex_ai/qwen/qwen3-235b-a22b-instruct-2507-maas', messages)` |

#### 使用方式 {#usage-6}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "qwen/qwen3-coder-480b-a35b-instruct-maas"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 加入設定**

```yaml
model_list:
    - model_name: vertex-qwen
      litellm_params:
        model: vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-east-1"
    - model_name: vertex-qwen
      litellm_params:
        model: vertex_ai/qwen/qwen3-coder-480b-a35b-instruct-maas
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-west-1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "vertex-qwen", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

## VertexAI GPT-OSS Models {#vertexai-gpt-oss-models}

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/openai/{MODEL}` |
| Vertex 文件 | [Vertex AI - GPT-OSS 模型](https://console.cloud.google.com/vertex-ai/publishers/openai/model-garden/) |

**LiteLLM 支援所有 Vertex AI GPT-OSS 模型。** 請確保您對所有 Vertex AI GPT-OSS 模型使用 `vertex_ai/openai/` 前綴。

| 模型名稱       | 用量                        |
|------------------|------------------------------|
| vertex_ai/openai/gpt-oss-20b-maas | `completion('vertex_ai/openai/gpt-oss-20b-maas', messages)` |

#### 用量 {#usage-7}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

model = "openai/gpt-oss-20b-maas"

vertex_ai_project = "your-vertex-project" # can also set this as os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location" # can also set this as os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/" + model,
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```
**Proxy**

**1. 新增至設定**

```yaml
model_list:
    - model_name: gpt-oss
      litellm_params:
        model: vertex_ai/openai/gpt-oss-20b-maas
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-central1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "gpt-oss", # 👈 the 'model_name' in config
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

#### 用量 - `reasoning_effort` {#usage---reasoning_effort}

GPT-OSS 模型支援 `reasoning_effort` 參數，以提供增強的推理能力。

**SDK**

```python
from litellm import completion

response = completion(
    model="vertex_ai/openai/gpt-oss-20b-maas",
    messages=[{"role": "user", "content": "Solve this complex problem step by step"}],
    reasoning_effort="low",  # Options: "minimal", "low", "medium", "high"
    vertex_ai_project="your-vertex-project",
    vertex_ai_location="us-central1",
)
```

**PROXY**

1. 設定 config.yaml

```yaml
model_list:
- model_name: gpt-oss
  litellm_params:
    model: vertex_ai/openai/gpt-oss-20b-maas
    vertex_ai_project: "my-test-project"
    vertex_ai_location: "us-central1"
```

2. 啟動 proxy

```bash
litellm --config /path/to/config.yaml
```

3. 測試！ 

```bash
curl http://0.0.0.0:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-LITELLM-KEY>" \
  -d '{
    "model": "gpt-oss",
    "messages": [{"role": "user", "content": "Solve this complex problem step by step"}],
    "reasoning_effort": "low"
  }'
```

## VertexAI xAI (Grok) {#vertexai-xai-grok}

Vertex AI Model Garden 中可用的 xAI Grok 模型，使用與其他 Model Garden 發佈者模型相同的 OpenAI 相容 chat-completions 路徑。請使用 `vertex_ai/xai/` 前綴（不是 `xai/`，後者是帶有 `XAI_API_KEY` 的直接 xAI API）。另請參閱 [xAI provider](./xai.md)。

| 屬性 | 詳細資料 |
|----------|---------|
| 提供者路由 | `vertex_ai/xai/{MODEL}` |
| Vertex / xAI 文件 | [xAI models](https://docs.x.ai/docs/models), [Model Garden](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models) |

| 模型名稱 | 用量 |
|------------|--------|
| `vertex_ai/xai/grok-4.1-fast-non-reasoning` | `completion('vertex_ai/xai/grok-4.1-fast-non-reasoning', messages)` |
| `vertex_ai/xai/grok-4.1-fast-reasoning` | `completion('vertex_ai/xai/grok-4.1-fast-reasoning', messages)` |
| `vertex_ai/xai/grok-4.20-non-reasoning` | `completion('vertex_ai/xai/grok-4.20-non-reasoning', messages)` |
| `vertex_ai/xai/grok-4.20-reasoning` | `completion('vertex_ai/xai/grok-4.20-reasoning', messages)` |

#### 用量 {#usage-8}

**SDK**

```python
from litellm import completion
import os

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ""

vertex_ai_project = "your-vertex-project"  # or os.environ["VERTEXAI_PROJECT"]
vertex_ai_location = "your-vertex-location"  # or os.environ["VERTEXAI_LOCATION"]

response = completion(
    model="vertex_ai/xai/grok-4.1-fast-non-reasoning",
    messages=[{"role": "user", "content": "hi"}],
    vertex_ai_project=vertex_ai_project,
    vertex_ai_location=vertex_ai_location,
)
print("\nModel Response", response)
```

**Proxy**

**1. 新增至設定**

```yaml
model_list:
    - model_name: grok-vertex
      litellm_params:
        model: vertex_ai/xai/grok-4.1-fast-non-reasoning
        vertex_ai_project: "my-test-project"
        vertex_ai_location: "us-central1"
```

**2. 啟動 proxy**

```bash
litellm --config /path/to/config.yaml

# RUNNING at http://0.0.0.0:4000
```

**3. 測試！**

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
      --header "Authorization: Bearer $LITELLM_API_KEY" \
      --header 'Content-Type: application/json' \
      --data '{
            "model": "grok-vertex",
            "messages": [
                {
                "role": "user",
                "content": "what llm are you"
                }
            ],
        }'
```

## Related pages

- [Vertex AI 影片生成（Veo）](https://docs.litellm.ai/docs/providers/vertex_ai/videos.md)
- [Vertex AI - 自行部署模型](https://docs.litellm.ai/docs/providers/vertex_self_deployed.md)