fix(groq): register and correctly route compound systems

litellm/llms/groq/chat/transformation.py

@@ -39,6 +39,8 @@ from litellm.types.utils import ModelResponse, ModelResponseStream
 
 from ...openai_like.chat.transformation import OpenAILikeChatConfig
 
+GROQ_COMPOUND_MODELS = frozenset({"compound", "compound-mini"})
+
 
 class GroqChatConfig(OpenAILikeChatConfig):
     frequency_penalty: Optional[int] = None
@@ -84,6 +86,26 @@ class GroqChatConfig(OpenAILikeChatConfig):
     def get_config(cls):
         return super().get_config()
 
+    @staticmethod
+    def _get_groq_model_name(model: str) -> str:
+        return f"groq/{model}" if model in GROQ_COMPOUND_MODELS else model
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        return super().transform_request(
+            model=self._get_groq_model_name(model),
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+
     def get_model_response_iterator(
         self,
         streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],

litellm/model_prices_and_context_window_backup.json

@@ -24104,6 +24104,28 @@
         "supports_response_schema": false,
         "supports_tool_choice": true
     },
+    "groq/compound": {
+        "input_cost_per_token": 0.0,
+        "litellm_provider": "groq",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0.0,
+        "supports_tool_choice": true,
+        "supports_web_search": true
+    },
+    "groq/compound-mini": {
+        "input_cost_per_token": 0.0,
+        "litellm_provider": "groq",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0.0,
+        "supports_tool_choice": true,
+        "supports_web_search": true
+    },
     "groq/whisper-large-v3": {
         "input_cost_per_second": 3.083e-05,
         "litellm_provider": "groq",

model_prices_and_context_window.json

@@ -24262,6 +24262,28 @@
         "supports_response_schema": false,
         "supports_tool_choice": true
     },
+    "groq/compound": {
+        "input_cost_per_token": 0.0,
+        "litellm_provider": "groq",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0.0,
+        "supports_tool_choice": true,
+        "supports_web_search": true
+    },
+    "groq/compound-mini": {
+        "input_cost_per_token": 0.0,
+        "litellm_provider": "groq",
+        "max_input_tokens": 131072,
+        "max_output_tokens": 8192,
+        "max_tokens": 8192,
+        "mode": "chat",
+        "output_cost_per_token": 0.0,
+        "supports_tool_choice": true,
+        "supports_web_search": true
+    },
     "groq/whisper-large-v3": {
         "input_cost_per_second": 3.083e-05,
         "litellm_provider": "groq",

tests/test_litellm/llms/groq/chat/test_transformation.py

@@ -0,0 +1,92 @@
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import httpx
+import pytest
+
+import litellm
+from litellm.llms.custom_httpx.http_handler import HTTPHandler
+from litellm.llms.groq.chat.transformation import GroqChatConfig
+
+
+@pytest.mark.parametrize(
+    "model, expected",
+    [
+        ("compound", "groq/compound"),
+        ("compound-mini", "groq/compound-mini"),
+        ("llama-3.3-70b-versatile", "llama-3.3-70b-versatile"),
+        ("openai/gpt-oss-120b", "openai/gpt-oss-120b"),
+    ],
+)
+def test_get_groq_model_name(model, expected):
+    assert GroqChatConfig._get_groq_model_name(model) == expected
+
+
+@pytest.mark.parametrize("model", ["groq/compound", "groq/compound-mini"])
+def test_compound_models_in_backup_cost_map(model):
+    """
+    Regression test for https://github.com/BerriAI/litellm/issues/32467
+
+    The Groq compound systems must be registered so they route through the groq
+    provider and expose metadata.
+    """
+    json_path = Path(__file__).parents[5] / "litellm" / "model_prices_and_context_window_backup.json"
+    with open(json_path) as f:
+        model_cost = json.load(f)
+
+    info = model_cost.get(model)
+    assert info is not None, f"{model} missing from backup JSON"
+    assert info["litellm_provider"] == "groq"
+    assert info["mode"] == "chat"
+    assert info["max_input_tokens"] == 131072
+    assert info["max_output_tokens"] == 8192
+
+
+@pytest.mark.parametrize(
+    "requested_model, expected_api_model",
+    [
+        ("groq/compound", "groq/compound"),
+        ("groq/compound-mini", "groq/compound-mini"),
+        ("groq/llama-3.3-70b-versatile", "llama-3.3-70b-versatile"),
+    ],
+)
+def test_compound_model_name_sent_to_groq(requested_model, expected_api_model):
+    """
+    Regression test for https://github.com/BerriAI/litellm/issues/32467
+
+    Groq exposes the compound systems with the "groq/" prefix as part of the
+    actual model id, so LiteLLM must send "groq/compound(-mini)" instead of the
+    prefix-stripped "compound(-mini)", which Groq rejects with model_not_found.
+    """
+    client = HTTPHandler()
+    fake_response = httpx.Response(
+        status_code=200,
+        json={
+            "id": "chatcmpl-1",
+            "object": "chat.completion",
+            "created": 1,
+            "model": expected_api_model,
+            "service_tier": "auto",
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {"role": "assistant", "content": "hi"},
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
+        },
+        request=httpx.Request("POST", "https://api.groq.com/openai/v1/chat/completions"),
+    )
+
+    with patch.object(client, "post", return_value=fake_response) as mock_post:
+        litellm.completion(
+            model=requested_model,
+            messages=[{"role": "user", "content": "hi"}],
+            api_key="fake-key",
+            client=client,
+        )
+
+    sent_body = json.loads(mock_post.call_args.kwargs["data"])
+    assert sent_body["model"] == expected_api_model