diff --git a/llama_stack/providers/remote/inference/gemini/gemini.py b/llama_stack/providers/remote/inference/gemini/gemini.py
index 11f6f05ad..baf0ec316 100644
--- a/llama_stack/providers/remote/inference/gemini/gemini.py
+++ b/llama_stack/providers/remote/inference/gemini/gemini.py
@@ -17,6 +17,7 @@ class GeminiInferenceAdapter(LiteLLMOpenAIMixin):
             MODEL_ENTRIES,
             api_key_from_config=config.api_key,
             provider_data_api_key_field="gemini_api_key",
+            litellm_provider_name="gemini",
         )
         self.config = config
 
diff --git a/llama_stack/providers/remote/inference/groq/groq.py b/llama_stack/providers/remote/inference/groq/groq.py
index 91c6b6c17..5ad4bf49c 100644
--- a/llama_stack/providers/remote/inference/groq/groq.py
+++ b/llama_stack/providers/remote/inference/groq/groq.py
@@ -36,6 +36,7 @@ class GroqInferenceAdapter(LiteLLMOpenAIMixin):
             model_entries=MODEL_ENTRIES,
             api_key_from_config=config.api_key,
             provider_data_api_key_field="groq_api_key",
+            litellm_provider_name="groq",
         )
         self.config = config
 
diff --git a/llama_stack/providers/remote/inference/sambanova/sambanova.py b/llama_stack/providers/remote/inference/sambanova/sambanova.py
index 9c2dda889..bdffb04bf 100644
--- a/llama_stack/providers/remote/inference/sambanova/sambanova.py
+++ b/llama_stack/providers/remote/inference/sambanova/sambanova.py
@@ -184,6 +184,7 @@ class SambaNovaInferenceAdapter(LiteLLMOpenAIMixin):
             model_entries=MODEL_ENTRIES,
             api_key_from_config=self.config.api_key.get_secret_value() if self.config.api_key else None,
             provider_data_api_key_field="sambanova_api_key",
+            litellm_provider_name="sambanova",
         )
 
     def _get_api_key(self) -> str:
diff --git a/llama_stack/providers/utils/inference/litellm_openai_mixin.py b/llama_stack/providers/utils/inference/litellm_openai_mixin.py
index 0de267f6c..af12fc631 100644
--- a/llama_stack/providers/utils/inference/litellm_openai_mixin.py
+++ b/llama_stack/providers/utils/inference/litellm_openai_mixin.py
@@ -71,7 +71,17 @@ class LiteLLMOpenAIMixin(
         api_key_from_config: str | None,
         provider_data_api_key_field: str,
         openai_compat_api_base: str | None = None,
+        litellm_provider_name: str | None = None,
     ):
+        """
+        Initialize the LiteLLMOpenAIMixin.
+
+        :param model_entries: The model entries to register.
+        :param api_key_from_config: The API key to use from the config.
+        :param provider_data_api_key_field: The field in the provider data that contains the API key.
+        :param openai_compat_api_base: The base URL for OpenAI compatibility, or None if not using OpenAI compatibility.
+        :param litellm_provider_name: The name of the provider, used for model lookups.
+        """
         ModelRegistryHelper.__init__(self, model_entries)
         self.api_key_from_config = api_key_from_config
         self.provider_data_api_key_field = provider_data_api_key_field
@@ -82,6 +92,8 @@ class LiteLLMOpenAIMixin(
         else:
             self.is_openai_compat = False
 
+        self.litellm_provider_name = litellm_provider_name
+
     async def initialize(self):
         pass
 
@@ -421,3 +433,21 @@ class LiteLLMOpenAIMixin(
         logprobs: LogProbConfig | None = None,
     ):
         raise NotImplementedError("Batch chat completion is not supported for OpenAI Compat")
+
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available via LiteLLM for the current
+        provider (self.litellm_provider_name).
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        if not self.litellm_provider_name:
+            logger.warning("Provider name is not set, cannot check model availability.")
+            return False
+
+        if self.litellm_provider_name not in litellm.models_by_provider:
+            logger.warning(f"Provider {self.litellm_provider_name} is not registered in litellm.")
+            return False
+
+        return model in litellm.models_by_provider[self.litellm_provider_name]