test(utils.py): handle scenario where text tokens + reasoning tokens … (#10165)

* test(utils.py): handle scenario where text tokens + reasoning tokens set, but reasoning tokens not charged separately

Addresses https://github.com/BerriAI/litellm/pull/10141#discussion_r2051555332

* fix(vertex_and_google_ai_studio.py): only set content if non-empty str

tests/litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -26,6 +26,47 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_toke
 from litellm.types.utils import Usage
 
 
+def test_reasoning_tokens_no_price_set():
+    model = "o1-mini"
+    custom_llm_provider = "openai"
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+    model_cost_map = litellm.model_cost[model]
+    usage = Usage(
+        completion_tokens=1578,
+        prompt_tokens=17,
+        total_tokens=1595,
+        completion_tokens_details=CompletionTokensDetailsWrapper(
+            accepted_prediction_tokens=None,
+            audio_tokens=None,
+            reasoning_tokens=952,
+            rejected_prediction_tokens=None,
+            text_tokens=626,
+        ),
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            audio_tokens=None, cached_tokens=None, text_tokens=17, image_tokens=None
+        ),
+    )
+    prompt_cost, completion_cost = generic_cost_per_token(
+        model=model,
+        usage=usage,
+        custom_llm_provider="openai",
+    )
+    assert round(prompt_cost, 10) == round(
+        model_cost_map["input_cost_per_token"] * usage.prompt_tokens,
+        10,
+    )
+    print(f"completion_cost: {completion_cost}")
+    expected_completion_cost = (
+        model_cost_map["output_cost_per_token"] * usage.completion_tokens
+    )
+    print(f"expected_completion_cost: {expected_completion_cost}")
+    assert round(completion_cost, 10) == round(
+        expected_completion_cost,
+        10,
+    )
+
+
 def test_reasoning_tokens_gemini():
     model = "gemini-2.5-flash-preview-04-17"
     custom_llm_provider = "gemini"

tests/litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py

@@ -239,3 +239,23 @@ def test_vertex_ai_thinking_output_part():
     content, reasoning_content = v.get_assistant_content_message(parts=parts)
     assert content == "Hello world"
     assert reasoning_content == "I'm thinking..."
+
+
+def test_vertex_ai_empty_content():
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+    from litellm.types.llms.vertex_ai import HttpxPartType
+
+    v = VertexGeminiConfig()
+    parts = [
+        HttpxPartType(
+            functionCall={
+                "name": "get_current_weather",
+                "arguments": "{}",
+            },
+        ),
+    ]
+    content, reasoning_content = v.get_assistant_content_message(parts=parts)
+    assert content is None
+    assert reasoning_content is None