diff --git a/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md b/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md
new file mode 100644
index 000000000000..bbb294898564
--- /dev/null
+++ b/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md
@@ -0,0 +1,43 @@
+# Claude Code - Prompt Cache Routing
+
+Claude's [Prompt Caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) feature helps to optimize API usage through attempting to cache prompts and re-use cached prompts during subsequent API calls. This feature is used by Claude Code. 
+
+When LiteLLM [load balancing](../proxy/load_balancing.md) is enabled, to ensure this prompt caching feature still works with Claude Code, LiteLLM needs to be configured to use the `PromptCachingDeploymentCheck` pre-call check. This pre-call check will ensure that API calls that used prompt caching are remembered and that subsequent API calls that try to use that prompt caching are routed to the same model deployment where a cache write occurred. 
+
+## Set Up
+
+1. Configure the router so that it uses the `PromptCachingDeploymentCheck` (via setting the `optional_pre_call_checks` property), and configure the models so that they can access multiple deployments of Claude; below, we show an example for multiple AWS accounts (referred to as `account-1` and `account-2`, using the `aws_profile_name` property):
+```yaml
+router_settings:
+  optional_pre_call_checks: ["prompt_caching"]
+
+model_list:
+- litellm_params:
+    model: us.anthropic.claude-sonnet-4-5-20250929-v1:0
+    aws_profile_name: account-1
+    aws_region_name: us-west-2
+  model_info:
+    litellm_provider: bedrock
+  model_name: us.anthropic.claude-sonnet-4-5-20250929-v1:0
+- litellm_params:
+    model: us.anthropic.claude-sonnet-4-5-20250929-v1:0
+    aws_profile_name: account-2
+    aws_region_name: us-west-2
+  model_info:
+    litellm_provider: bedrock
+  model_name: us.anthropic.claude-sonnet-4-5-20250929-v1:0
+```
+2. Utilize Claude Code:
+   1. Launch Claude Code, which will do a warm-up API call that tries to cache its warm-up prompt and its system prompt.
+   2. Wait a few seconds, then quit Claude Code and re-open it.
+   3. You'll notice that the warm-up API call successfully gets a cache hit (if using Claude Code in an IDE like VS Code, ensure that you don't do anything between step 2.1 and 2.2 here, otherwise there may not be a cache hit): 
+      1. Go to the [LiteLLM Request Logs page](../proxy/ui_logs.md) in the Admin UI
+      2. Click on the individual requests to see (a) the cache creation and cache read tokens; and (b) the Model ID. In particular, the API call from step 2.1 should show a cache write, and the API call from step 2.2 should show a cache read; in addition, the Model ID should be equal (meaning the API call is getting forwarded to the same AWS account).
+
+## Related
+
+- [Claude Code - Quickstart](./claude_responses_api.md)
+- [Claude Code - Customer Tracking](./claude_code_customer_tracking.md)
+- [Claude Code - Plugin Marketplace](./claude_code_plugin_marketplace.md)
+- [Claude Code - WebSearch](./claude_code_websearch.md)
+- [Proxy - Load Balancing](../proxy/load_balancing.md)
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index dc4d7ce61958..13e5f3d26a9d 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -122,6 +122,7 @@ const sidebars = {
           items: [
             "tutorials/claude_responses_api",
             "tutorials/claude_code_customer_tracking",
+            "tutorials/claude_code_prompt_cache_routing",
             "tutorials/claude_code_websearch",
             "tutorials/claude_mcp",
             "tutorials/claude_non_anthropic_models",
diff --git a/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py b/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py
index d3d237d9f288..e9c4b69d8ef9 100644
--- a/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py
+++ b/litellm/router_utils/pre_call_checks/prompt_caching_deployment_check.py
@@ -61,9 +61,10 @@ async def async_log_success_event(self, kwargs, response_obj, start_time, end_ti
         if (
             call_type != CallTypes.completion.value
             and call_type != CallTypes.acompletion.value
+            and call_type != CallTypes.anthropic_messages.value
         ):  # only use prompt caching for completion calls
             verbose_logger.debug(
-                "litellm.router_utils.pre_call_checks.prompt_caching_deployment_check: skipping adding model id to prompt caching cache, CALL TYPE IS NOT COMPLETION"
+                "litellm.router_utils.pre_call_checks.prompt_caching_deployment_check: skipping adding model id to prompt caching cache, CALL TYPE IS NOT COMPLETION or ANTHROPIC MESSAGE"
             )
             return
 
diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py
index 08ae804ea80e..75ec806ee176 100644
--- a/tests/test_litellm/test_router.py
+++ b/tests/test_litellm/test_router.py
@@ -1869,3 +1869,124 @@ async def mock_original_function(**kwargs):
 
     assert result["result"] == "success"
     assert result["selected_guardrail"]["id"] == "guardrail-1"
+
+@pytest.mark.asyncio
+async def test_anthropic_messages_call_type_is_cached():
+    """
+    Regression test: Verify that anthropic_messages call type is allowed
+    in PromptCachingDeploymentCheck.async_log_success_event.
+    """
+    import asyncio
+    from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import (
+        PromptCachingDeploymentCheck,
+    )
+    from litellm.router_utils.prompt_caching_cache import PromptCachingCache
+    from litellm.caching.dual_cache import DualCache
+    from litellm.types.utils import CallTypes
+    from litellm.types.utils import (
+        StandardLoggingPayload,
+        StandardLoggingModelInformation,
+        StandardLoggingMetadata,
+        StandardLoggingHiddenParams,
+    )
+    
+    # Create mock standard logging payload inline
+    def create_standard_logging_payload() -> StandardLoggingPayload:
+        return StandardLoggingPayload(
+            id="test_id",
+            call_type="completion",
+            response_cost=0.1,
+            response_cost_failure_debug_info=None,
+            status="success",
+            total_tokens=30,
+            prompt_tokens=20,
+            completion_tokens=10,
+            startTime=1234567890.0,
+            endTime=1234567891.0,
+            completionStartTime=1234567890.5,
+            model_map_information=StandardLoggingModelInformation(
+                model_map_key="gpt-3.5-turbo", model_map_value=None
+            ),
+            model="gpt-3.5-turbo",
+            model_id="model-123",
+            model_group="openai-gpt",
+            api_base="https://api.openai.com",
+            metadata=StandardLoggingMetadata(
+                user_api_key_hash="test_hash",
+                user_api_key_org_id=None,
+                user_api_key_alias="test_alias",
+                user_api_key_team_id="test_team",
+                user_api_key_user_id="test_user",
+                user_api_key_team_alias="test_team_alias",
+                spend_logs_metadata=None,
+                requester_ip_address="127.0.0.1",
+                requester_metadata=None,
+            ),
+            cache_hit=False,
+            cache_key=None,
+            saved_cache_cost=0.0,
+            request_tags=[],
+            end_user=None,
+            requester_ip_address="127.0.0.1",
+            messages=[{"role": "user", "content": "Hello, world!"}],
+            response={"choices": [{"message": {"content": "Hi there!"}}]},
+            error_str=None,
+            model_parameters={"stream": True},
+            hidden_params=StandardLoggingHiddenParams(
+                model_id="model-123",
+                cache_key=None,
+                api_base="https://api.openai.com",
+                response_cost="0.1",
+                additional_headers=None,
+            ),
+        )
+    
+    cache = DualCache()
+    deployment_check = PromptCachingDeploymentCheck(cache=cache)
+    prompt_cache = PromptCachingCache(cache=cache)
+    
+    # Create messages with enough tokens to pass the caching threshold
+    test_messages = [
+        {
+            "role": "user", 
+            "content": [
+                {
+                    "type": "text", 
+                    "text": "test long message here" * 1024,
+                    "cache_control": {
+                        "type": "ephemeral",
+                        "ttl": "5m"
+                    }
+                }
+            ]
+        }
+    ]
+    test_model_id = "test-model-id-123"
+    
+    # Create a payload with anthropic_messages call type
+    payload = create_standard_logging_payload()
+    payload["call_type"] = CallTypes.anthropic_messages.value
+    payload["messages"] = test_messages
+    payload["model"] = "anthropic/claude-3-5-sonnet-20240620"
+    payload["model_id"] = test_model_id
+    
+    # Log the success event (should cache the model_id)
+    await deployment_check.async_log_success_event(
+        kwargs={"standard_logging_object": payload},
+        response_obj={},
+        start_time=1234567890.0,
+        end_time=1234567891.0,
+    )
+    
+    # Small delay to ensure cache write completes
+    await asyncio.sleep(0.1)
+    
+    # Verify that the model_id was actually cached
+    cached_result = await prompt_cache.async_get_model_id(
+        messages=test_messages,
+        tools=None,
+    )
+    
+    # This assertion will FAIL if anthropic_messages is filtered out
+    assert cached_result is not None, "Model ID should be cached for anthropic_messages call type"
+    assert cached_result["model_id"] == test_model_id, f"Expected {test_model_id}, got {cached_result['model_id']}"