fix(vertex_ai): merge cache pricing coverage fixes

litellm/llms/vertex_ai/cost_calculator.py

@@ -197,7 +197,7 @@ def _handle_128k_pricing(
     input_rate: Final = (input_cost_per_token_above_128k_tokens or base_input_rate) if above_tier else base_input_rate
 
     cache_read_rate: Final = model_info.get("cache_read_input_token_cost") or input_rate
-    cache_creation_rate: Final = model_info.get("cache_creation_input_token_cost") or base_input_rate
+    cache_creation_rate: Final = model_info.get("cache_creation_input_token_cost") or input_rate
 
     prompt_cost = (
         text_tokens * input_rate + cache_read_tokens * cache_read_rate + cache_creation_tokens * cache_creation_rate

tests/test_litellm/llms/vertex_ai/context_caching/__init__.py

@@ -0,0 +1 @@
+"""Vertex AI context caching tests package."""

tests/test_litellm/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py

@@ -0,0 +1,119 @@
+from typing import Final
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching import (
+    ContextCachingEndpoints,
+)
+
+_CACHE_RESPONSE: Final = {
+    "name": "cachedContents/new-cache-name",
+    "model": "gemini-2.5-flash",
+    "usageMetadata": {"totalTokenCount": 2048},
+    "createTime": "2026-09-01T00:00:00Z",
+    "expireTime": "2026-09-01T01:00:00Z",
+}
+
+_EXPECTED_CREATION: Final = {
+    "name": "cachedContents/new-cache-name",
+    "model": "gemini-2.5-flash",
+    "total_token_count": 2048,
+    "create_time": "2026-09-01T00:00:00Z",
+    "expire_time": "2026-09-01T01:00:00Z",
+}
+
+
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages"
+)
+@patch("litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.local_cache_obj")
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.transform_openai_messages_to_gemini_context_caching"
+)
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.is_prompt_caching_valid_prompt"
+)
+@patch.object(ContextCachingEndpoints, "check_cache")
+@patch.object(ContextCachingEndpoints, "_get_token_and_url_context_caching")
+def test_check_and_create_cache_stores_cached_content_creation_on_logging(
+    mock_get_token_url, mock_check_cache, mock_valid_prompt, mock_transform, mock_cache_obj, mock_separate
+) -> None:
+    mock_separate.return_value = ([{"role": "user", "content": "cached"}], [{"role": "user", "content": "fresh"}])
+    mock_cache_obj.get_cache_key.return_value = "test_cache_key"
+    mock_check_cache.return_value = None
+    mock_valid_prompt.return_value = True
+    mock_get_token_url.return_value = ("token", "https://test-url.com")
+    mock_transform.return_value = {"model": "gemini-2.5-flash", "contents": []}
+    response: Final = MagicMock()
+    response.json.return_value = _CACHE_RESPONSE
+    client: Final = MagicMock(spec=HTTPHandler)
+    client.post.return_value = response
+    logging_obj: Final = MagicMock(spec=Logging)
+    logging_obj.model_call_details = {}
+
+    ContextCachingEndpoints().check_and_create_cache(
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={},
+        api_key="test_key",
+        api_base=None,
+        model="gemini-2.5-flash",
+        client=client,
+        timeout=30.0,
+        logging_obj=logging_obj,
+        custom_llm_provider="vertex_ai",
+        vertex_project="test_project",
+        vertex_location="us-central1",
+        vertex_auth_header="vertex_test_token",
+    )
+
+    assert logging_obj.model_call_details["vertex_ai_cached_content"] == _EXPECTED_CREATION
+
+
+@pytest.mark.asyncio
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.separate_cached_messages"
+)
+@patch("litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.local_cache_obj")
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.transform_openai_messages_to_gemini_context_caching"
+)
+@patch(
+    "litellm.llms.vertex_ai.context_caching.vertex_ai_context_caching.is_prompt_caching_valid_prompt"
+)
+@patch.object(ContextCachingEndpoints, "async_check_cache")
+@patch.object(ContextCachingEndpoints, "_get_token_and_url_context_caching")
+async def test_async_check_and_create_cache_stores_cached_content_creation_on_logging(
+    mock_get_token_url, mock_async_check_cache, mock_valid_prompt, mock_transform, mock_cache_obj, mock_separate
+) -> None:
+    mock_separate.return_value = ([{"role": "user", "content": "cached"}], [{"role": "user", "content": "fresh"}])
+    mock_cache_obj.get_cache_key.return_value = "test_cache_key"
+    mock_async_check_cache.return_value = None
+    mock_valid_prompt.return_value = True
+    mock_get_token_url.return_value = ("token", "https://test-url.com")
+    mock_transform.return_value = {"model": "gemini-2.5-flash", "contents": []}
+    response: Final = MagicMock()
+    response.json.return_value = _CACHE_RESPONSE
+    client: Final = MagicMock(spec=AsyncHTTPHandler)
+    client.post = AsyncMock(return_value=response)
+    logging_obj: Final = MagicMock(spec=Logging)
+    logging_obj.model_call_details = {}
+
+    await ContextCachingEndpoints().async_check_and_create_cache(
+        messages=[{"role": "user", "content": "hi"}],
+        optional_params={},
+        api_key="test_key",
+        api_base=None,
+        model="gemini-2.5-flash",
+        client=client,
+        timeout=30.0,
+        logging_obj=logging_obj,
+        custom_llm_provider="vertex_ai",
+        vertex_project="test_project",
+        vertex_location="us-central1",
+        vertex_auth_header="vertex_test_token",
+    )
+
+    assert logging_obj.model_call_details["vertex_ai_cached_content"] == _EXPECTED_CREATION

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

@@ -6440,3 +6440,33 @@ def test_transform_response_applies_cache_creation_usage(include_creation):
         assert result.usage.prompt_tokens == 10010
         assert not hasattr(result.usage, "cache_creation_input_tokens")
         assert "vertex_ai_cached_content" not in result._hidden_params
+
+
+def test_streaming_chunk_parser_surfaces_cache_creation_usage_and_metadata():
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ModelResponseIterator
+
+    model = "gemini-3.8-flash"
+    cached_content = {
+        "name": "cached-content",
+        "model": model,
+        "total_token_count": 10000,
+        "create_time": None,
+        "expire_time": None,
+    }
+    litellm_logging = MagicMock()
+    litellm_logging.model_call_details = {"vertex_ai_cached_content": cached_content}
+    chunk = {
+        "candidates": [{"content": {"parts": [{"text": "Hello"}]}}],
+        "usageMetadata": {
+            "promptTokenCount": 10,
+            "candidatesTokenCount": 1,
+            "totalTokenCount": 11,
+        },
+    }
+
+    iterator = ModelResponseIterator(streaming_response=[], sync_stream=True, logging_obj=litellm_logging)
+    streaming_chunk = iterator.chunk_parser(chunk)
+
+    assert streaming_chunk.usage.prompt_tokens == 10010
+    assert streaming_chunk.usage.cache_creation_input_tokens == 10000
+    assert streaming_chunk._hidden_params["vertex_ai_cached_content"] == cached_content

tests/test_litellm/llms/vertex_ai/test_cost_calculator.py

@@ -61,3 +61,34 @@ def test_above_128k_pricing_splits_cache_tokens_out_of_the_prompt(
 
     assert prompt_cost == pytest.approx(expected_prompt_cost)
     assert completion_cost == pytest.approx(10 * 0.003)
+
+
+def test_above_128k_pricing_falls_back_to_the_resolved_tier_rate_for_creation_tokens(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """A missing cache-creation rate resolves to the tiered input rate, like generic_cost_per_token."""
+    model: Final = "vertex_ai/fake-above-128k-model-no-creation-rate"
+    monkeypatch.setitem(
+        litellm.model_cost,
+        model,
+        {
+            "litellm_provider": "vertex_ai",
+            "input_cost_per_token": 0.001,
+            "input_cost_per_token_above_128k_tokens": 0.002,
+            "output_cost_per_token": 0.003,
+        },
+    )
+
+    usage: Final = Usage(
+        prompt_tokens=260_000,
+        completion_tokens=10,
+        total_tokens=260_010,
+        prompt_tokens_details=PromptTokensDetailsWrapper(cache_creation_tokens=120_000),
+    )
+    prompt_cost, _ = cost_per_token(
+        model=model,
+        custom_llm_provider="vertex_ai",
+        usage=usage,
+    )
+
+    assert prompt_cost == pytest.approx(140_000 * 0.002 + 120_000 * 0.002)