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sgp
API Reference
Chat
Completions

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.

ParametersExpand Collapse
ending_before: Optional[str]
limit: Optional[int]
maximum10000
minimum1
model_vendor: Optional[InferenceModelVendor]
One of the following:
"openai"
"cohere"
"vertex_ai"
"anthropic"
"azure"
"gemini"
"launch"
"llmengine"
"model_zoo"
"bedrock"
"xai"
"fireworks_ai"
sort_by: Optional[str]
sort_order: Optional[SortOrder]
One of the following:
"asc"
"desc"
starting_after: Optional[str]
ReturnsExpand Collapse
class CompletionModelsResponse: …
items: List[ModelDefinition]
model_name: str

model name, for example gpt-4o

model_type: InferenceModelType

model type, for example chat_completion

One of the following:
"generic"
"completion"
"chat_completion"
model_vendor: InferenceModelVendor

model vendor, for example openai

One of the following:
"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

One of the following:
"unknown"
"available"
"unavailable"
object: Optional[Literal["list"]]

List available chat completion models

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)
{
  "items": [
    {
      "model_name": "model_name",
      "model_type": "generic",
      "model_vendor": "openai",
      "model_availability": "unknown"
    }
  ],
  "object": "list"
}
Returns Examples
{
  "items": [
    {
      "model_name": "model_name",
      "model_type": "generic",
      "model_vendor": "openai",
      "model_availability": "unknown"
    }
  ],
  "object": "list"
}