diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py
index 11a4f68ece0..6079b709bcc 100644
--- a/litellm/llms/anthropic/common_utils.py
+++ b/litellm/llms/anthropic/common_utils.py
@@ -1411,6 +1411,97 @@ def flatten_unencrypted_web_search_results_in_anthropic_messages(  # mutable-ok:
     return [_flatten_web_search_results_in_message(m) for m in messages]  # mutable-ok: JSON wire format
 
 
+def _normalized_cache_control(cache_control: object) -> dict[str, str] | None:  # mutable-ok: JSON wire format
+    if not isinstance(cache_control, Mapping):
+        return None
+    cache_type: Final = cache_control.get("type")
+    return {"type": cache_type if isinstance(cache_type, str) else "ephemeral"}  # mutable-ok: JSON wire format
+
+
+def _with_portable_cache_control(block: Mapping[str, object]) -> dict[str, object]:  # mutable-ok: JSON wire format
+    if "cache_control" not in block:
+        return dict(block)  # mutable-ok: JSON wire format
+    normalized: Final = _normalized_cache_control(block["cache_control"])
+    rest: Final = {key: value for key, value in block.items() if key != "cache_control"}  # mutable-ok: JSON wire format
+    return rest if normalized is None else {**rest, "cache_control": normalized}  # mutable-ok: JSON wire format
+
+
+def _with_portable_cache_control_in_blocks(blocks: object) -> object:
+    if isinstance(blocks, str) or not isinstance(blocks, Sequence):
+        return blocks
+    return [  # mutable-ok: JSON wire format
+        _with_portable_cache_control(block) if isinstance(block, Mapping) else block for block in blocks
+    ]
+
+
+def _with_portable_cache_control_in_content_block(block: object) -> object:
+    if not isinstance(block, Mapping):
+        return block
+    portable: Final = _with_portable_cache_control(block)
+    if portable.get("type") != "tool_result" or "content" not in portable:
+        return portable
+    return {  # mutable-ok: JSON wire format
+        **portable,
+        "content": _with_portable_cache_control_in_blocks(portable["content"]),
+    }
+
+
+def _with_portable_cache_control_in_message(message: object) -> object:
+    if not isinstance(message, Mapping) or "content" not in message:
+        return message
+    content: Final = message["content"]
+    if isinstance(content, str) or not isinstance(content, Sequence):
+        return message
+    return {  # mutable-ok: JSON wire format
+        **message,
+        "content": [  # mutable-ok: JSON wire format
+            _with_portable_cache_control_in_content_block(block) for block in content
+        ],
+    }
+
+
+def _with_portable_cache_control_in_messages(messages: object) -> object:
+    if isinstance(messages, str) or not isinstance(messages, Sequence):
+        return messages
+    return [  # mutable-ok: JSON wire format
+        _with_portable_cache_control_in_message(message) for message in messages
+    ]
+
+
+def _with_portable_cache_control_in_scoped_value(key: str, value: object) -> object:
+    match key:
+        case "system" | "tools":
+            return _with_portable_cache_control_in_blocks(value)
+        case "messages":
+            return _with_portable_cache_control_in_messages(value)
+        case _:
+            return value
+
+
+def normalize_cache_control_in_anthropic_payload(
+    payload: Mapping[str, object],
+) -> dict[str, object]:  # mutable-ok: JSON wire format
+    """
+    Return a copy of an Anthropic /v1/messages payload with every
+    ``cache_control`` entry reduced to ``{"type": <its type, or "ephemeral">}``
+    at the places the Messages API defines it: the request itself, system
+    blocks, tools, message content blocks, and ``tool_result`` content blocks.
+    Application data such as ``tool_use.input`` and tool ``input_schema`` is
+    never touched, even when it happens to contain a ``cache_control`` key.
+
+    Anthropic itself accepts prompt-caching extensions such as ``ttl``, but
+    strict non-Anthropic implementations of the Messages API validate the field
+    literally and reject the whole request (``cache_control.ttl: 1h is not
+    supported``, ``cache_control.type is required``), which 400s clients like
+    Claude Code that send cache hints. Non-dict ``cache_control`` values are
+    dropped entirely. The caller's payload is never mutated.
+    """
+    portable: Final = _with_portable_cache_control(payload)
+    return {  # mutable-ok: JSON wire format
+        key: _with_portable_cache_control_in_scoped_value(key, value) for key, value in portable.items()
+    }
+
+
 def process_anthropic_headers(headers: httpx.Headers | dict) -> dict:
     openai_headers: Final = {}
     if "anthropic-ratelimit-requests-limit" in headers:
diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
index 69985bcdaa3..b82903d6f87 100644
--- a/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/messages/handler.py
@@ -99,6 +99,10 @@ def _deployment_passes_through_anthropic_messages(model_info: object) -> bool:
     return isinstance(supported_endpoints, (list, tuple)) and "/v1/messages" in supported_endpoints
 
 
+def _deployment_supports_cache_control_ttl(model_info: object) -> bool:
+    return isinstance(model_info, dict) and model_info.get("cache_control_ttl") is True
+
+
 ####### ENVIRONMENT VARIABLES ###################
 # Initialize any necessary instances or variables here
 base_llm_http_handler = BaseLLMHTTPHandler()
@@ -568,7 +572,9 @@ def anthropic_messages_handler(
             OpenAILikeAnthropicMessagesConfig,
         )
 
-        anthropic_messages_provider_config = OpenAILikeAnthropicMessagesConfig()
+        anthropic_messages_provider_config = OpenAILikeAnthropicMessagesConfig(
+            cache_control_ttl=_deployment_supports_cache_control_ttl(kwargs.get("model_info")),
+        )
     if anthropic_messages_provider_config is None:
         # Route to Responses API for OpenAI / Azure, chat/completions for everything else.
         if _should_route_to_responses_api(custom_llm_provider, original_model, model):
diff --git a/litellm/llms/openai_like/README.md b/litellm/llms/openai_like/README.md
index e9aaafe48a1..e1409b81c35 100644
--- a/litellm/llms/openai_like/README.md
+++ b/litellm/llms/openai_like/README.md
@@ -54,7 +54,10 @@ That's it! The provider will be automatically loaded and available.
     "constraints": {
       "temperature_max": 1.0,
       "temperature_min": 0.0,
-      "temperature_min_with_n_gt_1": 0.3
+      "temperature_min_with_n_gt_1": 0.3,
+      // /v1/messages providers only: keep Anthropic cache_control extensions
+      // such as ttl instead of stripping them down to {"type": ...}
+      "cache_control_ttl": true
     },
     
     // Optional: Special handling flags
diff --git a/litellm/llms/openai_like/messages/transformation.py b/litellm/llms/openai_like/messages/transformation.py
index 11dc236064d..ac99617521c 100644
--- a/litellm/llms/openai_like/messages/transformation.py
+++ b/litellm/llms/openai_like/messages/transformation.py
@@ -1,11 +1,13 @@
 from typing import Any, Final
 
 import litellm
+from litellm.llms.anthropic.common_utils import normalize_cache_control_in_anthropic_payload
 from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
     AnthropicMessagesConfig,
 )
 from litellm.llms.openai_like.json_loader import SimpleProviderConfig
 from litellm.secret_managers.main import get_secret_str
+from litellm.types.router import GenericLiteLLMParams
 
 DEFAULT_ANTHROPIC_API_VERSION: Final = "2023-06-01"
 
@@ -19,10 +21,17 @@ class OpenAILikeAnthropicMessagesConfig(AnthropicMessagesConfig):
     ``"/v1/messages"``. The inbound Anthropic payload (system, cache_control,
     thinking, tools, ...) is forwarded essentially unchanged to
     ``{api_base}/v1/messages``, so Anthropic-only features that the
-    Anthropic->OpenAI translation would otherwise drop are preserved. Response
-    parsing and streaming are inherited from the native Anthropic config.
+    Anthropic->OpenAI translation would otherwise drop are preserved. The one
+    exception is ``cache_control``, whose Anthropic-only extensions (``ttl``)
+    are stripped unless the deployment opts in with
+    ``model_info.cache_control_ttl: true``. Response parsing and streaming are
+    inherited from the native Anthropic config.
     """
 
+    def __init__(self, cache_control_ttl: bool = False) -> None:
+        super().__init__()
+        self._cache_control_ttl: Final = cache_control_ttl
+
     def validate_anthropic_messages_environment(
         self,
         headers: dict[str, str],
@@ -53,6 +62,35 @@ class OpenAILikeAnthropicMessagesConfig(AnthropicMessagesConfig):
     def should_filter_anthropic_beta_headers(self) -> bool:
         return False
 
+    def supports_cache_control_ttl(self) -> bool:
+        return self._cache_control_ttl
+
+    def transform_anthropic_messages_request(
+        self,
+        model: str,
+        messages: list[dict],  # mutable-ok: matches dict-typed base signature
+        anthropic_messages_optional_request_params: dict,  # mutable-ok: matches dict-typed base signature
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,  # mutable-ok: matches dict-typed base signature
+    ) -> dict:  # mutable-ok: matches dict-typed base signature
+        """
+        Anthropic ignores prompt-caching hints it cannot honor, but strict
+        non-Anthropic implementations of the Messages API 400 the whole request
+        on Anthropic-only ``cache_control`` extensions (``cache_control.ttl: 1h
+        is not supported``), so unless the provider declares ttl support the
+        hints are reduced to their portable ``{"type": ...}`` core.
+        """
+        request: Final = super().transform_anthropic_messages_request(
+            model=model,
+            messages=messages,
+            anthropic_messages_optional_request_params=anthropic_messages_optional_request_params,
+            litellm_params=litellm_params,
+            headers=headers,
+        )
+        if self.supports_cache_control_ttl():
+            return request
+        return normalize_cache_control_in_anthropic_payload(request)
+
     def get_complete_url(
         self,
         api_base: str | None,
@@ -81,7 +119,7 @@ class JSONProviderAnthropicMessagesConfig(OpenAILikeAnthropicMessagesConfig):
     """
 
     def __init__(self, provider: SimpleProviderConfig):
-        super().__init__()
+        super().__init__(cache_control_ttl=bool(provider.constraints.get("cache_control_ttl")))
         self._provider = provider
 
     @property
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
index ad4c3d6bfbb..e819433c269 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
@@ -296,21 +296,15 @@ async def test_bedrock_converse_budget_tokens_preserved():
         mock_acompletion.assert_called_once()
 
         call_kwargs = mock_acompletion.call_args.kwargs
-        print(
-            "acompletion call kwargs: ", json.dumps(call_kwargs, indent=4, default=str)
-        )
+        print("acompletion call kwargs: ", json.dumps(call_kwargs, indent=4, default=str))
 
         # Verify thinking parameter is passed through with budget_tokens preserved
         thinking_param = call_kwargs.get("thinking")
-        assert (
-            thinking_param is not None
-        ), "thinking parameter should be passed to acompletion"
-        assert (
-            thinking_param.get("type") == "enabled"
-        ), "thinking.type should be 'enabled'"
-        assert (
-            thinking_param.get("budget_tokens") == 1024
-        ), f"thinking.budget_tokens should be 1024, but got {thinking_param.get('budget_tokens')}"
+        assert thinking_param is not None, "thinking parameter should be passed to acompletion"
+        assert thinking_param.get("type") == "enabled", "thinking.type should be 'enabled'"
+        assert thinking_param.get("budget_tokens") == 1024, (
+            f"thinking.budget_tokens should be 1024, but got {thinking_param.get('budget_tokens')}"
+        )
 
 
 def test_openai_model_with_thinking_converts_to_reasoning():
@@ -342,23 +336,18 @@ def test_openai_model_with_thinking_converts_to_reasoning():
         call_kwargs = mock_responses.call_args.kwargs
 
         # Verify reasoning is set (converted from thinking)
-        assert (
-            "reasoning" in call_kwargs
-        ), "reasoning should be passed to litellm.responses"
+        assert "reasoning" in call_kwargs, "reasoning should be passed to litellm.responses"
 
         # budget_tokens=1024 -> effort="low" (at the LOW budget threshold)
         # reasoning_auto_summary is False by default, so no summary key
         expected_reasoning = {"effort": "low"}
         assert call_kwargs["reasoning"] == expected_reasoning, (
-            f"reasoning should be {expected_reasoning} for budget_tokens=1024, "
-            f"got {call_kwargs.get('reasoning')}"
+            f"reasoning should be {expected_reasoning} for budget_tokens=1024, got {call_kwargs.get('reasoning')}"
         )
         assert "summary" not in call_kwargs["reasoning"]
 
         # Verify thinking is NOT passed directly to the Responses API
-        assert (
-            "thinking" not in call_kwargs
-        ), "thinking should NOT be passed directly to litellm.responses"
+        assert "thinking" not in call_kwargs, "thinking should NOT be passed directly to litellm.responses"
 
 
 class TestThinkingParameterTransformation:
@@ -411,9 +400,7 @@ class TestThinkingParameterTransformation:
                 thinking=thinking,
                 model="openai/gpt-5.2",
             )
-            assert result == {
-                "reasoning_effort": {"effort": "high", "summary": "detailed"}
-            }
+            assert result == {"reasoning_effort": {"effort": "high", "summary": "detailed"}}
         finally:
             litellm.reasoning_auto_summary = original
 
@@ -611,9 +598,9 @@ class TestThinkingSummaryPreservation:
             mock_responses.assert_called_once()
             call_kwargs = mock_responses.call_args.kwargs
             reasoning = call_kwargs["reasoning"]
-            assert (
-                reasoning["summary"] == "concise"
-            ), f"Expected summary='concise', got summary='{reasoning.get('summary')}'"
+            assert reasoning["summary"] == "concise", (
+                f"Expected summary='concise', got summary='{reasoning.get('summary')}'"
+            )
 
     def test_responses_adapter_preserves_summary(self):
         """translate_thinking_to_reasoning should include summary when user provides it."""
@@ -622,9 +609,7 @@ class TestThinkingSummaryPreservation:
         )
 
         thinking = {"type": "enabled", "budget_tokens": 5000, "summary": "concise"}
-        result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(
-            thinking
-        )
+        result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(thinking)
         assert result == {"effort": "high", "summary": "concise"}
 
     def test_responses_adapter_no_summary_by_default(self):
@@ -638,11 +623,7 @@ class TestThinkingSummaryPreservation:
         try:
             litellm.reasoning_auto_summary = False
             thinking = {"type": "enabled", "budget_tokens": 5000}
-            result = (
-                LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(
-                    thinking
-                )
-            )
+            result = LiteLLMAnthropicToResponsesAPIAdapter.translate_thinking_to_reasoning(thinking)
             assert result == {"effort": "high"}
             assert result is not None and "summary" not in result
         finally:
@@ -659,9 +640,7 @@ class TestThinkingSummaryPreservation:
             thinking=thinking,
             model="openai/gpt-5.2",
         )
-        assert result == {
-            "reasoning_effort": {"effort": "high", "summary": "concise"}
-        }
+        assert result == {"reasoning_effort": {"effort": "high", "summary": "concise"}}
 
     def test_translate_thinking_for_model_disabled_stays_plain_string_when_auto_summary_enabled(self):
         """Disabled thinking must stay a plain string even when reasoning_auto_summary is on."""
@@ -807,9 +786,7 @@ def test_presanitized_flag_not_leaked_to_provider_params():
 
     def fake_base_handler(*args, **kwargs):
         captured.update(kwargs)
-        captured["optional"] = kwargs.get(
-            "anthropic_messages_optional_request_params", {}
-        )
+        captured["optional"] = kwargs.get("anthropic_messages_optional_request_params", {})
         return "stub"
 
     with patch.object(
@@ -974,6 +951,38 @@ def test_gate_passthrough_skipped_when_only_chat_completions_supported(monkeypat
     assert "config" not in captured
 
 
+@pytest.mark.parametrize(
+    "model_info, expected_ttl_support",
+    [
+        ({"supported_endpoints": ["/v1/messages"]}, False),
+        ({"supported_endpoints": ["/v1/messages"], "cache_control_ttl": True}, True),
+        ({"supported_endpoints": ["/v1/messages"], "cache_control_ttl": "yes"}, False),
+    ],
+)
+def test_gate_passthrough_forwards_cache_control_ttl_only_when_deployment_opts_in(
+    monkeypatch, model_info, expected_ttl_support
+):
+    """The passthrough config strips cache_control.ttl unless the deployment sets
+    model_info.cache_control_ttl to exactly true."""
+    from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
+        anthropic_messages_handler,
+    )
+
+    captured, _ = _gate_stubs(monkeypatch)
+
+    result = anthropic_messages_handler(
+        max_tokens=100,
+        messages=[{"role": "user", "content": "Hello"}],
+        model="openai/some-model",
+        api_key="sk-test",
+        api_base="https://host/v1",
+        model_info=model_info,
+    )
+
+    assert result == "native-passthrough"
+    assert captured["config"].supports_cache_control_ttl() is expected_ttl_support
+
+
 def test_first_party_claude_4_8_plus_cost_map_entries_carry_mid_conversation_system_flag():
     """Regional and provider-prefixed Claude 4.8+/5 entries carry
     ``supports_mid_conversation_system``, but the bare first-party keys
@@ -987,9 +996,7 @@ def test_first_party_claude_4_8_plus_cost_map_entries_carry_mid_conversation_sys
 
     import litellm
 
-    cost_map_path = os.path.join(
-        os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json"
-    )
+    cost_map_path = os.path.join(os.path.dirname(litellm.__file__), "model_prices_and_context_window_backup.json")
     with open(cost_map_path) as f:
         cost_map = json.load(f)
     rules = cost_map["fallback_generalizations"]["rules"]
@@ -1028,9 +1035,7 @@ def test_first_party_claude_4_8_plus_cost_map_entries_carry_mid_conversation_sys
         ("perplexity/sonar", "sonar", "https://api.perplexity.ai/chat/completions"),
     ],
 )
-async def test_messages_strips_provider_prefix_exactly_once(
-    requested_model, expected_wire_model, expected_url
-):
+async def test_messages_strips_provider_prefix_exactly_once(requested_model, expected_wire_model, expected_url):
     """
     BerriAI/litellm#37716: only the leading provider segment may be stripped on the way upstream.
 
diff --git a/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py b/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py
index 9a6a039a470..67a56fdcd79 100644
--- a/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py
+++ b/tests/test_litellm/llms/openai_like/messages/test_openai_like_anthropic_messages_transformation.py
@@ -318,3 +318,203 @@ def test_json_provider_messages_config_probes_capabilities_under_provider_slug()
     )
     assert JSONProviderAnthropicMessagesConfig(provider).custom_llm_provider == "exampleprovider"
     assert OpenAILikeAnthropicMessagesConfig().custom_llm_provider == "anthropic"
+
+
+def _cache_control_request_params() -> tuple[list, dict]:
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "write a regex for a US phone number",
+                    "cache_control": {"type": "ephemeral", "ttl": "1h"},
+                }
+            ],
+        }
+    ]
+    optional_params = {
+        "max_tokens": 256,
+        "system": [
+            {
+                "type": "text",
+                "text": "You are Claude Code.",
+                "cache_control": {"type": "ephemeral", "ttl": "5m"},
+            }
+        ],
+        "tools": [
+            {
+                "name": "lookup",
+                "input_schema": {"type": "object"},
+                "cache_control": {"type": "ephemeral", "ttl": "1h"},
+            }
+        ],
+    }
+    return messages, optional_params
+
+
+def test_request_strips_cache_control_ttl_everywhere(config):
+    """Regression: Claude Code always sends ``cache_control: {type: ephemeral,
+    ttl: 1h}``, and strict non-Anthropic /v1/messages validators 400 the whole
+    request on the ttl extension (``cache_control.ttl: 1h is not supported``)."""
+    messages, optional_params = _cache_control_request_params()
+
+    payload = config.transform_anthropic_messages_request(
+        model="some-model",
+        messages=messages,
+        anthropic_messages_optional_request_params=optional_params,
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    assert payload["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral"}
+    assert payload["system"][0]["cache_control"] == {"type": "ephemeral"}
+    assert payload["tools"][0]["cache_control"] == {"type": "ephemeral"}
+    assert messages[0]["content"][0]["cache_control"] == {"type": "ephemeral", "ttl": "1h"}
+
+
+def test_request_defaults_missing_cache_control_type_and_drops_non_dict(config):
+    payload = config.transform_anthropic_messages_request(
+        model="some-model",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "a", "cache_control": {"ttl": "1h"}},
+                    {"type": "text", "text": "b", "cache_control": None},
+                ],
+            }
+        ],
+        anthropic_messages_optional_request_params={"max_tokens": 64},
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    blocks = payload["messages"][0]["content"]
+    assert blocks[0]["cache_control"] == {"type": "ephemeral"}
+    assert "cache_control" not in blocks[1]
+
+
+def test_native_anthropic_config_keeps_cache_control_ttl():
+    """Anthropic itself accepts ttl, so the normalization must stay scoped to
+    the OpenAI-like passthrough and never reach the native Anthropic path."""
+    from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
+        AnthropicMessagesConfig,
+    )
+
+    messages, optional_params = _cache_control_request_params()
+    payload = AnthropicMessagesConfig().transform_anthropic_messages_request(
+        model="claude-sonnet-4-20250514",
+        messages=messages,
+        anthropic_messages_optional_request_params=optional_params,
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    assert payload["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral", "ttl": "1h"}
+    assert payload["system"][0]["cache_control"] == {"type": "ephemeral", "ttl": "5m"}
+
+
+def test_deployment_opt_in_keeps_cache_control_ttl():
+    config = OpenAILikeAnthropicMessagesConfig(cache_control_ttl=True)
+    payload = config.transform_anthropic_messages_request(
+        model="some-model",
+        messages=[
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "hi", "cache_control": {"type": "ephemeral", "ttl": "1h"}}],
+            }
+        ],
+        anthropic_messages_optional_request_params={"max_tokens": 16},
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+    assert payload["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral", "ttl": "1h"}
+
+
+def test_json_provider_constraint_opts_into_cache_control_ttl():
+    from litellm.llms.openai_like.json_loader import SimpleProviderConfig
+    from litellm.llms.openai_like.messages.transformation import (
+        JSONProviderAnthropicMessagesConfig,
+    )
+
+    base_data = {"base_url": "https://api.example.com/v1", "api_key_env": "EXAMPLE_API_KEY"}
+    strict = JSONProviderAnthropicMessagesConfig(SimpleProviderConfig(slug="strictprov", data=base_data))
+    lenient = JSONProviderAnthropicMessagesConfig(
+        SimpleProviderConfig(slug="lenientprov", data={**base_data, "constraints": {"cache_control_ttl": True}})
+    )
+
+    def transform(provider_config):
+        messages, optional_params = _cache_control_request_params()
+        return provider_config.transform_anthropic_messages_request(
+            model="some-model",
+            messages=messages,
+            anthropic_messages_optional_request_params=optional_params,
+            litellm_params=GenericLiteLLMParams(),
+            headers={},
+        )
+
+    assert transform(strict)["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral"}
+    assert transform(lenient)["messages"][0]["content"][0]["cache_control"] == {"type": "ephemeral", "ttl": "1h"}
+
+
+def test_request_strips_ttl_only_where_the_messages_api_defines_cache_control(config):
+    """Regression: the sanitizer must only touch ``cache_control`` where the
+    Messages API defines it (request, system, tools, content blocks, tool_result
+    content), never application data such as ``tool_use.input`` or a tool's
+    ``input_schema`` that happens to contain a ``cache_control`` key."""
+    tool_input = {"cache_control": {"type": "ephemeral", "ttl": "1h"}, "query": "x"}
+    input_schema = {
+        "type": "object",
+        "properties": {"cache_control": {"type": "string", "ttl": "1h"}},
+    }
+    messages = [
+        {
+            "role": "assistant",
+            "content": [{"type": "tool_use", "id": "toolu_1", "name": "lookup", "input": tool_input}],
+        },
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_1",
+                    "cache_control": {"type": "ephemeral", "ttl": "1h"},
+                    "content": [
+                        {"type": "text", "text": "result", "cache_control": {"type": "ephemeral", "ttl": "1h"}}
+                    ],
+                },
+                {"type": "text", "text": "plain string content stays", "cache_control": {"ttl": "1h"}},
+            ],
+        },
+        {"role": "user", "content": "a plain string message"},
+    ]
+    optional_params = {
+        "max_tokens": 64,
+        "cache_control": {"type": "ephemeral", "ttl": "1h"},
+        "tools": [
+            {
+                "name": "lookup",
+                "input_schema": input_schema,
+                "cache_control": {"type": "ephemeral", "ttl": "1h"},
+            }
+        ],
+    }
+
+    payload = config.transform_anthropic_messages_request(
+        model="some-model",
+        messages=messages,
+        anthropic_messages_optional_request_params=optional_params,
+        litellm_params=GenericLiteLLMParams(),
+        headers={},
+    )
+
+    assert payload["cache_control"] == {"type": "ephemeral"}
+    assert payload["tools"][0]["cache_control"] == {"type": "ephemeral"}
+    assert payload["tools"][0]["input_schema"] == input_schema
+    assert payload["messages"][0]["content"][0]["input"] == tool_input
+    tool_result = payload["messages"][1]["content"][0]
+    assert tool_result["cache_control"] == {"type": "ephemeral"}
+    assert tool_result["content"][0]["cache_control"] == {"type": "ephemeral"}
+    assert payload["messages"][1]["content"][1]["cache_control"] == {"type": "ephemeral"}
+    assert payload["messages"][2] == {"role": "user", "content": "a plain string message"}