## List available chat completion models

`chat.completions.models(CompletionModelsParams**kwargs)  -> CompletionModelsResponse`

**get** `/v5/chat/completions/models`

Lists the models available for use with /v5/chat/completions.

Results are served directly from the stored inference-models catalog filtered to the chat-completion
model type, not from a live provider probe, so availability reflects the catalog's recorded status.
Pass the optional `model_vendor` query parameter to restrict results to a single vendor. Results are
paginated, and each entry reports the model name, vendor, type, and availability. If the underlying
catalog query fails the endpoint returns an empty list rather than raising an error.

### Parameters

- `ending_before: Optional[str]`

- `limit: Optional[int]`

- `model_vendor: Optional[InferenceModelVendor]`

  - `"openai"`

  - `"cohere"`

  - `"vertex_ai"`

  - `"anthropic"`

  - `"azure"`

  - `"gemini"`

  - `"launch"`

  - `"llmengine"`

  - `"model_zoo"`

  - `"bedrock"`

  - `"xai"`

  - `"fireworks_ai"`

- `sort_by: Optional[str]`

- `sort_order: Optional[SortOrder]`

  - `"asc"`

  - `"desc"`

- `starting_after: Optional[str]`

### Returns

- `class CompletionModelsResponse: …`

  - `items: List[ModelDefinition]`

    - `model_name: str`

      model name, for example `gpt-4o`

    - `model_type: InferenceModelType`

      model type, for example `chat_completion`

      - `"generic"`

      - `"completion"`

      - `"chat_completion"`

    - `model_vendor: InferenceModelVendor`

      model vendor, for example `openai`

      - `"openai"`

      - `"cohere"`

      - `"vertex_ai"`

      - `"anthropic"`

      - `"azure"`

      - `"gemini"`

      - `"launch"`

      - `"llmengine"`

      - `"model_zoo"`

      - `"bedrock"`

      - `"xai"`

      - `"fireworks_ai"`

    - `model_availability: Optional[InferenceModelAvailability]`

      model availability indicating availability status, for example `available`

      - `"unknown"`

      - `"available"`

      - `"unavailable"`

  - `object: Optional[Literal["list"]]`

    - `"list"`

### Example

```python
import os
from scale_gp_beta import SGPClient

client = SGPClient(
    api_key=os.environ.get("SGP_API_KEY"),  # This is the default and can be omitted
)
response = client.chat.completions.models()
print(response.items)
```

#### Response

```json
{
  "items": [
    {
      "model_name": "model_name",
      "model_type": "generic",
      "model_vendor": "openai",
      "model_availability": "unknown"
    }
  ],
  "object": "list"
}
```