diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 098a074892f..d890b78624f 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -1133,9 +1133,28 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                     if AnthropicConfig._is_claude_4_6_model(
                         model
                     ) or AnthropicConfig._is_claude_4_7_model(model):
+                        # ``_map_reasoning_effort`` returns ``type=adaptive``
+                        # for any string on adaptive models without checking
+                        # the value, so reject unmapped efforts here (matching
+                        # the /v1/messages path) instead of relying on the
+                        # downstream ``_apply_output_config`` check. Co-locating
+                        # validation with the mapping prevents garbage from
+                        # leaking into ``optional_params`` if ``map_openai_params``
+                        # is ever called without a subsequent ``transform_request``.
                         mapped_effort = AnthropicConfig.REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(
-                            value, value
+                            value
                         )
+                        if mapped_effort is None:
+                            raise litellm.exceptions.BadRequestError(
+                                message=(
+                                    f"Invalid reasoning_effort: {value!r}. "
+                                    f"Must be one of: 'minimal', 'low', "
+                                    f"'medium', 'high', 'xhigh', 'max', 'none'"
+                                ),
+                                model=model,
+                                llm_provider=self.custom_llm_provider
+                                or "anthropic",
+                            )
                         optional_params["output_config"] = {"effort": mapped_effort}
             elif param == "web_search_options" and isinstance(value, dict):
                 hosted_web_search_tool = self.map_web_search_tool(
diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py
index 7f42743cfc4..6f9e8de62e5 100644
--- a/litellm/llms/bedrock/chat/converse_transformation.py
+++ b/litellm/llms/bedrock/chat/converse_transformation.py
@@ -485,11 +485,28 @@ class AmazonConverseConfig(BaseConfig):
                 if AnthropicConfig._is_claude_4_6_model(
                     model
                 ) or AnthropicConfig._is_claude_4_7_model(model):
+                    # Use ``.get()`` without a fallback so unmapped efforts
+                    # (e.g. ``"disabled"``) surface as a clean 400 here
+                    # rather than leaking the raw garbage string through to
+                    # ``_validate_anthropic_adaptive_effort`` (which does
+                    # catch it, but only because validation happens to run).
+                    # Matches the /v1/messages pattern where validation is
+                    # co-located with the mapping.
                     mapped_effort = (
                         AnthropicConfig.REASONING_EFFORT_TO_OUTPUT_CONFIG_EFFORT.get(
-                            reasoning_effort, reasoning_effort
+                            reasoning_effort
                         )
                     )
+                    if mapped_effort is None:
+                        raise litellm.exceptions.BadRequestError(
+                            message=(
+                                f"Invalid reasoning_effort: {reasoning_effort!r}. "
+                                f"Must be one of: 'minimal', 'low', 'medium', "
+                                f"'high', 'xhigh', 'max', 'none'"
+                            ),
+                            model=model,
+                            llm_provider="bedrock_converse",
+                        )
                     self._validate_anthropic_adaptive_effort(
                         model=model, effort=mapped_effort
                     )