Replace hardcoded Bedrock native structured output model set with cost JSON lookup

Move the source of truth for which Bedrock models support native structured outputs (outputConfig.textFormat) from a hardcoded substring set (BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS) to the cost JSON via a new "supports_native_structured_output" flag. This makes it possible to add support for new models (including Claude Sonnet 4.6, which was missing) by updating the JSON alone, with no code changes needed.
2026-07-13 21:06:58 +00:00 · 2026-03-16 18:21:16 -04:00
parent 8f425ec3ff
commit cb66672017
4 changed files with 655 additions and 1080 deletions
@@ -91,34 +91,6 @@ UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [
    "compact-2026-01-12",  # The compact beta feature is not currently supported on the Converse and ConverseStream APIs
 ]

-# Models that support Bedrock's native structured outputs API (outputConfig.textFormat)
-# Uses substring matching against the Bedrock model ID
-# Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html
-BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS = {
-    # Anthropic Claude 4.5+
-    "claude-haiku-4-5",
-    "claude-sonnet-4-5",
-    "claude-opus-4-5",
-    "claude-opus-4-6",
-    # Qwen3
-    "qwen3",
-    # DeepSeek
-    "deepseek-v3.1",
-    # Gemma 3
-    "gemma-3",
-    # MiniMax
-    "minimax-m2",
-    # Mistral (magistral-small excluded: broken constrained decoding on Bedrock)
-    "ministral",
-    "mistral-large-3",
-    "voxtral",
-    # Moonshot
-    "kimi-k2",
-    # NVIDIA
-    "nemotron-nano",
-    # OpenAI (gpt-oss excluded: broken constrained decoding, works via tool-call fallback)
-}
-

 class AmazonConverseConfig(BaseConfig):
    """
@@ -188,8 +160,7 @@ class AmazonConverseConfig(BaseConfig):

            if isinstance(content, list):
                has_guarded_text = any(
-                    isinstance(item, dict) and item.get("type") == "guarded_text"
-                    for item in content
+                    isinstance(item, dict) and item.get("type") == "guarded_text" for item in content
                )
                if has_guarded_text:
                    continue  # Skip this message if it already has guarded_text
@@ -350,13 +321,9 @@ class AmazonConverseConfig(BaseConfig):

        # Check if the model is a Nova 2 model (matches nova-2-lite, nova-2-pro, etc.)
        # Also check for nova-2/ spec prefix for imported models
-        return model_without_region.startswith(
-            "amazon.nova-2-"
-        ) or model_without_region.startswith("nova-2/")
+        return model_without_region.startswith("amazon.nova-2-") or model_without_region.startswith("nova-2/")

-    def _map_web_search_options(
-        self, web_search_options: dict, model: str
-    ) -> Optional[BedrockToolBlock]:
+    def _map_web_search_options(self, web_search_options: dict, model: str) -> Optional[BedrockToolBlock]:
        """
        Map web_search_options to Nova grounding systemTool.

@@ -385,9 +352,7 @@ class AmazonConverseConfig(BaseConfig):
        # (unlike Anthropic), so we just enable grounding with no options
        return BedrockToolBlock(systemTool={"name": "nova_grounding"})

-    def _transform_reasoning_effort_to_reasoning_config(
-        self, reasoning_effort: str
-    ) -> dict:
+    def _transform_reasoning_effort_to_reasoning_config(self, reasoning_effort: str) -> dict:
        """
        Transform reasoning_effort parameter to Nova 2 reasoningConfig structure.

@@ -432,9 +397,7 @@ class AmazonConverseConfig(BaseConfig):
            }
        }

-    def _handle_reasoning_effort_parameter(
-        self, model: str, reasoning_effort: str, optional_params: dict
-    ) -> None:
+    def _handle_reasoning_effort_parameter(self, model: str, reasoning_effort: str, optional_params: dict) -> None:
        """
        Handle the reasoning_effort parameter based on the model type.

@@ -471,9 +434,7 @@ class AmazonConverseConfig(BaseConfig):
            optional_params["reasoning_effort"] = reasoning_effort
        elif self._is_nova_2_model(model):
            # Nova 2 models: transform to reasoningConfig
-            reasoning_config = self._transform_reasoning_effort_to_reasoning_config(
-                reasoning_effort
-            )
+            reasoning_config = self._transform_reasoning_effort_to_reasoning_config(reasoning_effort)
            optional_params.update(reasoning_config)
        else:
            # Anthropic and other models: convert to thinking parameter
@@ -493,8 +454,7 @@ class AmazonConverseConfig(BaseConfig):
            budget = thinking.get("budget_tokens")
            if isinstance(budget, int) and budget < BEDROCK_MIN_THINKING_BUDGET_TOKENS:
                verbose_logger.debug(
-                    "Bedrock requires thinking.budget_tokens >= %d, got %d. "
-                    "Clamping to minimum.",
+                    "Bedrock requires thinking.budget_tokens >= %d, got %d. Clamping to minimum.",
                    BEDROCK_MIN_THINKING_BUDGET_TOKENS,
                    budget,
                )
@@ -518,9 +478,7 @@ class AmazonConverseConfig(BaseConfig):
            "parallel_tool_calls",
        ]

-        if (
-            "arn" in model
-        ):  # we can't infer the model from the arn, so just add all params
+        if "arn" in model:  # we can't infer the model from the arn, so just add all params
            supported_params.append("tools")
            supported_params.append("tool_choice")
            supported_params.append("thinking")
@@ -542,9 +500,7 @@ class AmazonConverseConfig(BaseConfig):
            or base_model.startswith("meta.llama3-3")
            or base_model.startswith("meta.llama4")
            or base_model.startswith("amazon.nova")
-            or supports_function_calling(
-                model=model, custom_llm_provider=self.custom_llm_provider
-            )
+            or supports_function_calling(model=model, custom_llm_provider=self.custom_llm_provider)
        ):
            supported_params.append("tools")

@@ -554,9 +510,7 @@ class AmazonConverseConfig(BaseConfig):

        if litellm.utils.supports_tool_choice(
            model=model, custom_llm_provider=self.custom_llm_provider
-        ) or litellm.utils.supports_tool_choice(
-            model=base_model, custom_llm_provider=self.custom_llm_provider
-        ):
+        ) or litellm.utils.supports_tool_choice(model=base_model, custom_llm_provider=self.custom_llm_provider):
            # only anthropic and mistral support tool choice config. otherwise (E.g. cohere) will fail the call - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
            supported_params.append("tool_choice")

@@ -575,9 +529,7 @@ class AmazonConverseConfig(BaseConfig):
                model=model,
                custom_llm_provider=self.custom_llm_provider,
            )
-            or supports_reasoning(
-                model=base_model, custom_llm_provider=self.custom_llm_provider
-            )
+            or supports_reasoning(model=base_model, custom_llm_provider=self.custom_llm_provider)
        ):
            supported_params.append("thinking")
            supported_params.append("reasoning_effort")
@@ -602,9 +554,7 @@ class AmazonConverseConfig(BaseConfig):
            return ToolChoiceValuesBlock(auto={})
        elif isinstance(tool_choice, dict):
            # only supported for anthropic + mistral models - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ToolChoice.html
-            specific_tool = SpecificToolChoiceBlock(
-                name=tool_choice.get("function", {}).get("name", "")
-            )
+            specific_tool = SpecificToolChoiceBlock(name=tool_choice.get("function", {}).get("name", ""))
            return ToolChoiceValuesBlock(tool=specific_tool)
        else:
            raise litellm.utils.UnsupportedParamsError(
@@ -624,15 +574,9 @@ class AmazonConverseConfig(BaseConfig):
        return ["mp4", "mov", "mkv", "webm", "flv", "mpeg", "mpg", "wmv", "3gp"]

    def get_all_supported_content_types(self) -> List[str]:
-        return (
-            self.get_supported_image_types()
-            + self.get_supported_document_types()
-            + self.get_supported_video_types()
-        )
+        return self.get_supported_image_types() + self.get_supported_document_types() + self.get_supported_video_types()

-    def is_computer_use_tool_used(
-        self, tools: Optional[List[OpenAIChatCompletionToolParam]], model: str
-    ) -> bool:
+    def is_computer_use_tool_used(self, tools: Optional[List[OpenAIChatCompletionToolParam]], model: str) -> bool:
        """Check if computer use tools are being used in the request."""
        if tools is None:
            return False
@@ -645,9 +589,7 @@ class AmazonConverseConfig(BaseConfig):
                        return True
        return False

-    def _transform_computer_use_tools(
-        self, computer_use_tools: List[OpenAIChatCompletionToolParam]
-    ) -> List[dict]:
+    def _transform_computer_use_tools(self, computer_use_tools: List[OpenAIChatCompletionToolParam]) -> List[dict]:
        """Transform computer use tools to Bedrock format."""
        transformed_tools: List[dict] = []

@@ -689,9 +631,7 @@ class AmazonConverseConfig(BaseConfig):

    def _separate_computer_use_tools(
        self, tools: List[OpenAIChatCompletionToolParam], model: str
-    ) -> Tuple[
-        List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]
-    ]:
+    ) -> Tuple[List[OpenAIChatCompletionToolParam], List[OpenAIChatCompletionToolParam]]:
        """
        Separate computer use tools from regular function tools.

@@ -764,10 +704,27 @@ class AmazonConverseConfig(BaseConfig):

    @staticmethod
    def _supports_native_structured_outputs(model: str) -> bool:
-        """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat)."""
-        return any(
-            substring in model for substring in BEDROCK_NATIVE_STRUCTURED_OUTPUT_MODELS
-        )
+        """Check if the Bedrock model supports native structured outputs (outputConfig.textFormat).
+
+        Looks up the ``supports_native_structured_output`` flag in
+        ``litellm.model_cost`` (set in the cost JSON).
+        Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/structured-output.html
+        """
+        from litellm.llms.bedrock.common_utils import get_bedrock_base_model
+
+        base_model = get_bedrock_base_model(model)
+
+        # Try direct lookup
+        info = litellm.model_cost.get(base_model)
+
+        # Try without version suffix (e.g. "model-v1:0" -> "model-v1")
+        if info is None and ":" in base_model:
+            info = litellm.model_cost.get(base_model.rsplit(":", 1)[0])
+
+        if info is not None:
+            return info.get("supports_native_structured_output", False) is True
+
+        return False

    @staticmethod
    def _add_additional_properties_to_schema(schema: dict) -> dict:
@@ -790,25 +747,18 @@ class AmazonConverseConfig(BaseConfig):
        # Recurse into nested schemas
        if "properties" in result and isinstance(result["properties"], dict):
            result["properties"] = {
-                k: AmazonConverseConfig._add_additional_properties_to_schema(v)
-                for k, v in result["properties"].items()
+                k: AmazonConverseConfig._add_additional_properties_to_schema(v) for k, v in result["properties"].items()
            }
        if "items" in result and isinstance(result["items"], dict):
-            result["items"] = AmazonConverseConfig._add_additional_properties_to_schema(
-                result["items"]
-            )
+            result["items"] = AmazonConverseConfig._add_additional_properties_to_schema(result["items"])
        for defs_key in ("$defs", "definitions"):
            if defs_key in result and isinstance(result[defs_key], dict):
                result[defs_key] = {
-                    k: AmazonConverseConfig._add_additional_properties_to_schema(v)
-                    for k, v in result[defs_key].items()
+                    k: AmazonConverseConfig._add_additional_properties_to_schema(v) for k, v in result[defs_key].items()
                }
        for key in ("anyOf", "allOf", "oneOf"):
            if key in result and isinstance(result[key], list):
-                result[key] = [
-                    AmazonConverseConfig._add_additional_properties_to_schema(item)
-                    for item in result[key]
-                ]
+                result[key] = [AmazonConverseConfig._add_additional_properties_to_schema(item) for item in result[key]]

        return result

@@ -838,9 +788,7 @@ class AmazonConverseConfig(BaseConfig):
        }
        """
        if json_schema is not None:
-            json_schema = AmazonConverseConfig._add_additional_properties_to_schema(
-                json_schema
-            )
+            json_schema = AmazonConverseConfig._add_additional_properties_to_schema(json_schema)
        schema_str = json.dumps(json_schema) if json_schema is not None else "{}"
        json_schema_def: JsonSchemaDefinition = {"schema": schema_str}
        if name is not None:
@@ -862,14 +810,9 @@ class AmazonConverseConfig(BaseConfig):
        non_default_params: dict,
        optional_params: dict,
    ):
-        optional_params = self._add_tools_to_optional_params(
-            optional_params=optional_params, tools=tools
-        )
+        optional_params = self._add_tools_to_optional_params(optional_params=optional_params, tools=tools)

-        if (
-            "meta.llama3-3-70b-instruct-v1:0" in model
-            and non_default_params.get("stream", False) is True
-        ):
+        if "meta.llama3-3-70b-instruct-v1:0" in model and non_default_params.get("stream", False) is True:
            optional_params["fake_stream"] = True

    def map_openai_params(
@@ -913,7 +856,9 @@ class AmazonConverseConfig(BaseConfig):
                )
            if param == "tool_choice":
                _tool_choice_value = self.map_tool_choice_values(
-                    model=model, tool_choice=value, drop_params=drop_params  # type: ignore
+                    model=model,
+                    tool_choice=value,
+                    drop_params=drop_params,  # type: ignore
                )
                if _tool_choice_value is not None:
                    optional_params["tool_choice"] = _tool_choice_value
@@ -1024,14 +969,10 @@ class AmazonConverseConfig(BaseConfig):
                json_schema=json_schema,
                description=description,
            )
-            optional_params = self._add_tools_to_optional_params(
-                optional_params=optional_params, tools=[_tool]
-            )
+            optional_params = self._add_tools_to_optional_params(optional_params=optional_params, tools=[_tool])

            if (
-                litellm.utils.supports_tool_choice(
-                    model=model, custom_llm_provider=self.custom_llm_provider
-                )
+                litellm.utils.supports_tool_choice(model=model, custom_llm_provider=self.custom_llm_provider)
                and not is_thinking_enabled
            ):
                optional_params["tool_choice"] = ToolChoiceValuesBlock(
@@ -1043,9 +984,7 @@ class AmazonConverseConfig(BaseConfig):
        optional_params["json_mode"] = True
        return optional_params

-    def update_optional_params_with_thinking_tokens(
-        self, non_default_params: dict, optional_params: dict
-    ):
+    def update_optional_params_with_thinking_tokens(self, non_default_params: dict, optional_params: dict):
        """
        Handles scenario where max tokens is not specified. For anthropic models (anthropic api/bedrock/vertex ai), this requires having the max tokens being set and being greater than the thinking token budget.

@@ -1063,13 +1002,9 @@ class AmazonConverseConfig(BaseConfig):
        is_thinking_enabled = self.is_thinking_enabled(optional_params)
        is_max_tokens_in_request = self.is_max_tokens_in_request(non_default_params)
        if is_thinking_enabled and not is_max_tokens_in_request:
-            thinking_token_budget = cast(dict, optional_params["thinking"]).get(
-                "budget_tokens", None
-            )
+            thinking_token_budget = cast(dict, optional_params["thinking"]).get("budget_tokens", None)
            if thinking_token_budget is not None:
-                optional_params["maxTokens"] = (
-                    thinking_token_budget + DEFAULT_MAX_TOKENS
-                )
+                optional_params["maxTokens"] = thinking_token_budget + DEFAULT_MAX_TOKENS

    @overload
    def _get_cache_point_block(
@@ -1135,23 +1070,15 @@ class AmazonConverseConfig(BaseConfig):
            if message["role"] == "system":
                system_prompt_indices.append(idx)
                if isinstance(message["content"], str) and message["content"]:
-                    system_content_blocks.append(
-                        SystemContentBlock(text=message["content"])
-                    )
-                    cache_block = self._get_cache_point_block(
-                        message, block_type="system", model=model
-                    )
+                    system_content_blocks.append(SystemContentBlock(text=message["content"]))
+                    cache_block = self._get_cache_point_block(message, block_type="system", model=model)
                    if cache_block:
                        system_content_blocks.append(cache_block)
                elif isinstance(message["content"], list):
                    for m in message["content"]:
                        if m.get("type") == "text" and m.get("text"):
-                            system_content_blocks.append(
-                                SystemContentBlock(text=m["text"])
-                            )
-                            cache_block = self._get_cache_point_block(
-                                m, block_type="system", model=model
-                            )
+                            system_content_blocks.append(SystemContentBlock(text=m["text"]))
+                            cache_block = self._get_cache_point_block(m, block_type="system", model=model)
                            if cache_block:
                                system_content_blocks.append(cache_block)
        if len(system_prompt_indices) > 0:
@@ -1189,16 +1116,10 @@ class AmazonConverseConfig(BaseConfig):
        # Exceptions should not be stored in optional_params (this is a defensive fix)
        cleaned_params = filter_exceptions_from_params(optional_params)
        inference_params = safe_deep_copy(cleaned_params)
-        supported_converse_params = list(
-            AmazonConverseConfig.__annotations__.keys()
-        ) + ["top_k"]
+        supported_converse_params = list(AmazonConverseConfig.__annotations__.keys()) + ["top_k"]
        supported_tool_call_params = ["tools", "tool_choice"]
        supported_config_params = list(self.get_config_blocks().keys())
-        total_supported_params = (
-            supported_converse_params
-            + supported_tool_call_params
-            + supported_config_params
-        )
+        total_supported_params = supported_converse_params + supported_tool_call_params + supported_config_params
        inference_params.pop("json_mode", None)  # used for handling json_schema
        # Anthropic-only key. Bedrock expects `outputConfig` (camelCase) and
        # will reject `output_config` if it leaks through pass-through routes.
@@ -1209,25 +1130,15 @@ class AmazonConverseConfig(BaseConfig):
        if request_metadata is not None:
            self._validate_request_metadata(request_metadata)

-        output_config: Optional[OutputConfigBlock] = inference_params.pop(
-            "outputConfig", None
-        )
-        inference_params.pop(
-            "output_config", None
-        )  # Bedrock Converse doesn't support it
+        output_config: Optional[OutputConfigBlock] = inference_params.pop("outputConfig", None)
+        inference_params.pop("output_config", None)  # Bedrock Converse doesn't support it

        # keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params'
-        additional_request_params = {
-            k: v for k, v in inference_params.items() if k not in total_supported_params
-        }
-        inference_params = {
-            k: v for k, v in inference_params.items() if k in total_supported_params
-        }
+        additional_request_params = {k: v for k, v in inference_params.items() if k not in total_supported_params}
+        inference_params = {k: v for k, v in inference_params.items() if k in total_supported_params}

        # Handle parallel_tool_calls configuration
-        parallel_tool_use_config = additional_request_params.pop(
-            "_parallel_tool_use_config", None
-        )
+        parallel_tool_use_config = additional_request_params.pop("_parallel_tool_use_config", None)
        if parallel_tool_use_config is not None and is_claude_4_5_on_bedrock(model):
            for key, value in parallel_tool_use_config.items():
                if (
@@ -1242,9 +1153,7 @@ class AmazonConverseConfig(BaseConfig):
        additional_request_params.pop("parallel_tool_calls", None)

        # Only set the topK value in for models that support it
-        additional_request_params.update(
-            self._handle_top_k_value(model, inference_params)
-        )
+        additional_request_params.update(self._handle_top_k_value(model, inference_params))

        # Filter out internal/MCP-related parameters that shouldn't be sent to the API
        # These are LiteLLM internal parameters, not API parameters
@@ -1253,9 +1162,7 @@ class AmazonConverseConfig(BaseConfig):
        # Filter out non-serializable objects (exceptions, callables, logging objects, etc.)
        # from additional_request_params to prevent JSON serialization errors
        # This filters: Exception objects, callable objects (functions), Logging objects, etc.
-        additional_request_params = filter_exceptions_from_params(
-            additional_request_params
-        )
+        additional_request_params = filter_exceptions_from_params(additional_request_params)

        return (
            inference_params,
@@ -1302,9 +1209,7 @@ class AmazonConverseConfig(BaseConfig):
        # Only separate tools if computer use tools are actually present
        if filtered_tools and self.is_computer_use_tool_used(filtered_tools, model):
            # Separate computer use tools from regular function tools
-            computer_use_tools, regular_tools = self._separate_computer_use_tools(
-                filtered_tools, model
-            )
+            computer_use_tools, regular_tools = self._separate_computer_use_tools(filtered_tools, model)

            # Process regular function tools using existing logic
            bedrock_tools = _bedrock_tools_pt(regular_tools)
@@ -1365,9 +1270,7 @@ class AmazonConverseConfig(BaseConfig):

                anthropic_beta_list.append(computer_use_header)
                # Transform computer use tools to proper Bedrock format
-                transformed_computer_tools = self._transform_computer_use_tools(
-                    computer_use_tools
-                )
+                transformed_computer_tools = self._transform_computer_use_tools(computer_use_tools)
                additional_request_params["tools"] = transformed_computer_tools
        else:
            # No computer use tools, process all tools as regular tools
@@ -1396,15 +1299,9 @@ class AmazonConverseConfig(BaseConfig):
        """
        Bedrock doesn't support tool calling without `tools=` param specified.
        """
-        if (
-            "tools" not in optional_params
-            and messages is not None
-            and has_tool_call_blocks(messages)
-        ):
+        if "tools" not in optional_params and messages is not None and has_tool_call_blocks(messages):
            if litellm.modify_params:
-                optional_params["tools"] = add_dummy_tool(
-                    custom_llm_provider="bedrock_converse"
-                )
+                optional_params["tools"] = add_dummy_tool(custom_llm_provider="bedrock_converse")
            else:
                raise litellm.UnsupportedParamsError(
                    message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
@@ -1458,9 +1355,7 @@ class AmazonConverseConfig(BaseConfig):

        bedrock_tool_config: Optional[ToolConfigBlock] = None
        if len(bedrock_tools) > 0:
-            tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
-                "tool_choice", None
-            )
+            tool_choice_values: ToolChoiceValuesBlock = inference_params.pop("tool_choice", None)
            bedrock_tool_config = ToolConfigBlock(
                tools=bedrock_tools,
            )
@@ -1470,9 +1365,7 @@ class AmazonConverseConfig(BaseConfig):
        data: CommonRequestObject = {
            "additionalModelRequestFields": additional_request_params,
            "system": system_content_blocks,
-            "inferenceConfig": self._transform_inference_params(
-                inference_params=inference_params
-            ),
+            "inferenceConfig": self._transform_inference_params(inference_params=inference_params),
        }

        # Handle all config blocks
@@ -1502,14 +1395,10 @@ class AmazonConverseConfig(BaseConfig):
        litellm_params: dict,
        headers: Optional[dict] = None,
    ) -> RequestObject:
-        messages, system_content_blocks = self._transform_system_message(
-            messages, model=model
-        )
+        messages, system_content_blocks = self._transform_system_message(messages, model=model)

        # Convert last user message to guarded_text if guardrailConfig is present
-        messages = self._convert_consecutive_user_messages_to_guarded_text(
-            messages, optional_params
-        )
+        messages = self._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)
        ## TRANSFORMATION ##

        _data: CommonRequestObject = self._transform_request_helper(
@@ -1520,13 +1409,11 @@ class AmazonConverseConfig(BaseConfig):
            headers=headers,
        )

-        bedrock_messages = (
-            await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
-                messages=messages,
-                model=model,
-                llm_provider="bedrock_converse",
-                user_continue_message=litellm_params.pop("user_continue_message", None),
-            )
+        bedrock_messages = await BedrockConverseMessagesProcessor._bedrock_converse_messages_pt_async(
+            messages=messages,
+            model=model,
+            llm_provider="bedrock_converse",
+            user_continue_message=litellm_params.pop("user_continue_message", None),
        )

        data: RequestObject = {"messages": bedrock_messages, **_data}
@@ -1560,14 +1447,10 @@ class AmazonConverseConfig(BaseConfig):
        litellm_params: dict,
        headers: Optional[dict] = None,
    ) -> RequestObject:
-        messages, system_content_blocks = self._transform_system_message(
-            messages, model=model
-        )
+        messages, system_content_blocks = self._transform_system_message(messages, model=model)

        # Convert last user message to guarded_text if guardrailConfig is present
-        messages = self._convert_consecutive_user_messages_to_guarded_text(
-            messages, optional_params
-        )
+        messages = self._convert_consecutive_user_messages_to_guarded_text(messages, optional_params)

        _data: CommonRequestObject = self._transform_request_helper(
            model=model,
@@ -1616,9 +1499,7 @@ class AmazonConverseConfig(BaseConfig):
            encoding=encoding,
        )

-    def _transform_reasoning_content(
-        self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]
-    ) -> str:
+    def _transform_reasoning_content(self, reasoning_content_blocks: List[BedrockConverseReasoningContentBlock]) -> str:
        """
        Extract the reasoning text from the reasoning content blocks

@@ -1634,9 +1515,7 @@ class AmazonConverseConfig(BaseConfig):
        self, thinking_blocks: List[BedrockConverseReasoningContentBlock]
    ) -> List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]]:
        """Return a consistent format for thinking blocks between Anthropic and Bedrock."""
-        thinking_blocks_list: List[
-            Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]
-        ] = []
+        thinking_blocks_list: List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]] = []
        for block in thinking_blocks:
            if "reasoningText" in block:
                _thinking_block = ChatCompletionThinkingBlock(type="thinking")
@@ -1672,21 +1551,11 @@ class AmazonConverseConfig(BaseConfig):
            cache_creation_input_tokens = usage["cacheWriteInputTokens"]
            input_tokens += cache_creation_input_tokens

-        prompt_tokens_details = PromptTokensDetailsWrapper(
-            cached_tokens=cache_read_input_tokens
-        )
-        reasoning_tokens = (
-            token_counter(text=reasoning_content, count_response_tokens=True)
-            if reasoning_content
-            else 0
-        )
+        prompt_tokens_details = PromptTokensDetailsWrapper(cached_tokens=cache_read_input_tokens)
+        reasoning_tokens = token_counter(text=reasoning_content, count_response_tokens=True) if reasoning_content else 0
        completion_tokens_details = CompletionTokensDetailsWrapper(
            reasoning_tokens=reasoning_tokens,
-            text_tokens=(
-                output_tokens - reasoning_tokens
-                if reasoning_tokens > 0
-                else output_tokens
-            ),
+            text_tokens=(output_tokens - reasoning_tokens if reasoning_tokens > 0 else output_tokens),
        )
        openai_usage = Usage(
            prompt_tokens=input_tokens,
@@ -1701,9 +1570,7 @@ class AmazonConverseConfig(BaseConfig):

    def get_tool_call_names(
        self,
-        tools: Optional[
-            Union[List[ToolBlock], List[OpenAIChatCompletionToolParam]]
-        ] = None,
+        tools: Optional[Union[List[ToolBlock], List[OpenAIChatCompletionToolParam]]] = None,
    ) -> List[str]:
        if tools is None:
            return []
@@ -1742,13 +1609,8 @@ class AmazonConverseConfig(BaseConfig):
            try:
                tool_call_names = self.get_tool_call_names(tools)
                json_content = json.loads(message.content)
-                if (
-                    json_content.get("type") == "function"
-                    and json_content.get("name") in tool_call_names
-                ):
-                    tool_calls = [
-                        ChatCompletionMessageToolCall(function=Function(**json_content))
-                    ]
+                if json_content.get("type") == "function" and json_content.get("name") in tool_call_names:
+                    tool_calls = [ChatCompletionMessageToolCall(function=Function(**json_content))]

                    message.tool_calls = tool_calls
                    message.content = None
@@ -1777,9 +1639,7 @@ class AmazonConverseConfig(BaseConfig):
        """
        content_str = ""
        tools: List[ChatCompletionToolCallChunk] = []
-        reasoningContentBlocks: Optional[
-            List[BedrockConverseReasoningContentBlock]
-        ] = None
+        reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = None
        citationsContentBlocks: Optional[List[CitationsContentBlock]] = None
        for idx, content in enumerate(content_blocks):
            """
@@ -1796,9 +1656,7 @@ class AmazonConverseConfig(BaseConfig):
            if "toolUse" in content:
                ## check tool name was formatted by litellm
                _response_tool_name = content["toolUse"]["name"]
-                response_tool_name = get_bedrock_tool_name(
-                    response_tool_name=_response_tool_name
-                )
+                response_tool_name = get_bedrock_tool_name(response_tool_name=_response_tool_name)
                _function_chunk = ChatCompletionToolCallFunctionChunk(
                    name=response_tool_name,
                    arguments=json.dumps(content["toolUse"]["input"]),
@@ -1849,11 +1707,7 @@ class AmazonConverseConfig(BaseConfig):
        """
        try:
            response_data = json.loads(json_str)
-            if (
-                isinstance(response_data, dict)
-                and "properties" in response_data
-                and len(response_data) == 1
-            ):
+            if isinstance(response_data, dict) and "properties" in response_data and len(response_data) == 1:
                response_data = response_data["properties"]
                return json.dumps(response_data)
        except json.JSONDecodeError:
@@ -1877,11 +1731,7 @@ class AmazonConverseConfig(BaseConfig):
        if not json_mode or not tools:
            return tools if tools else None

-        json_tool_indices = [
-            i
-            for i, t in enumerate(tools)
-            if t["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME
-        ]
+        json_tool_indices = [i for i, t in enumerate(tools) if t["function"].get("name") == RESPONSE_FORMAT_TOOL_NAME]

        if not json_tool_indices:
            # No json_tool_call found, return tools unchanged
@@ -1889,14 +1739,10 @@ class AmazonConverseConfig(BaseConfig):

        if len(json_tool_indices) == len(tools):
            # All tools are json_tool_call — convert first one to content
-            verbose_logger.debug(
-                "Processing JSON tool call response for response_format"
-            )
+            verbose_logger.debug("Processing JSON tool call response for response_format")
            json_mode_content_str: Optional[str] = tools[0]["function"].get("arguments")
            if json_mode_content_str is not None:
-                json_mode_content_str = AmazonConverseConfig._unwrap_bedrock_properties(
-                    json_mode_content_str
-                )
+                json_mode_content_str = AmazonConverseConfig._unwrap_bedrock_properties(json_mode_content_str)
                chat_completion_message["content"] = json_mode_content_str
            return None

@@ -1906,13 +1752,9 @@ class AmazonConverseConfig(BaseConfig):
        first_idx = json_tool_indices[0]
        json_mode_args = tools[first_idx]["function"].get("arguments")
        if json_mode_args is not None:
-            json_mode_args = AmazonConverseConfig._unwrap_bedrock_properties(
-                json_mode_args
-            )
+            json_mode_args = AmazonConverseConfig._unwrap_bedrock_properties(json_mode_args)
            existing = chat_completion_message.get("content") or ""
-            chat_completion_message["content"] = (
-                existing + json_mode_args if existing else json_mode_args
-            )
+            chat_completion_message["content"] = existing + json_mode_args if existing else json_mode_args

        real_tools = [t for i, t in enumerate(tools) if i not in json_tool_indices]
        return real_tools if real_tools else None
@@ -1990,9 +1832,7 @@ class AmazonConverseConfig(BaseConfig):
        chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"}
        content_str = ""
        tools: List[ChatCompletionToolCallChunk] = []
-        reasoningContentBlocks: Optional[
-            List[BedrockConverseReasoningContentBlock]
-        ] = None
+        reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = None
        citationsContentBlocks: Optional[List[CitationsContentBlock]] = None

        if message is not None:
@@ -2011,17 +1851,11 @@ class AmazonConverseConfig(BaseConfig):
            provider_specific_fields["citationsContent"] = citationsContentBlocks

        if provider_specific_fields:
-            chat_completion_message[
-                "provider_specific_fields"
-            ] = provider_specific_fields
+            chat_completion_message["provider_specific_fields"] = provider_specific_fields

        if reasoningContentBlocks is not None:
-            chat_completion_message[
-                "reasoning_content"
-            ] = self._transform_reasoning_content(reasoningContentBlocks)
-            chat_completion_message[
-                "thinking_blocks"
-            ] = self._transform_thinking_blocks(reasoningContentBlocks)
+            chat_completion_message["reasoning_content"] = self._transform_reasoning_content(reasoningContentBlocks)
+            chat_completion_message["thinking_blocks"] = self._transform_thinking_blocks(reasoningContentBlocks)
        chat_completion_message["content"] = content_str
        filtered_tools = self._filter_json_mode_tools(
            json_mode=json_mode,
@@ -722,7 +722,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-haiku-4-5@20251001": {
        "cache_creation_input_token_cost": 1.25e-06,
@@ -745,7 +746,8 @@
        "supports_tool_choice": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 346,
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-3-5-sonnet-20240620-v1:0": {
        "input_cost_per_token": 3e-06,
@@ -967,7 +969,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-opus-4-6-v1": {
        "cache_creation_input_token_cost": 6.25e-06,
@@ -997,7 +1000,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "global.anthropic.claude-opus-4-6-v1": {
        "cache_creation_input_token_cost": 6.25e-06,
@@ -1027,7 +1031,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "us.anthropic.claude-opus-4-6-v1": {
        "cache_creation_input_token_cost": 6.875e-06,
@@ -1057,7 +1062,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "eu.anthropic.claude-opus-4-6-v1": {
        "cache_creation_input_token_cost": 6.875e-06,
@@ -1087,7 +1093,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "au.anthropic.claude-opus-4-6-v1": {
        "cache_creation_input_token_cost": 6.875e-06,
@@ -1117,7 +1124,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-sonnet-4-6": {
        "cache_creation_input_token_cost": 3.75e-06,
@@ -1147,7 +1155,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "global.anthropic.claude-sonnet-4-6": {
        "cache_creation_input_token_cost": 3.75e-06,
@@ -1177,7 +1186,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "us.anthropic.claude-sonnet-4-6": {
        "cache_creation_input_token_cost": 4.125e-06,
@@ -1207,7 +1217,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "eu.anthropic.claude-sonnet-4-6": {
        "cache_creation_input_token_cost": 4.125e-06,
@@ -1237,7 +1248,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "au.anthropic.claude-sonnet-4-6": {
        "cache_creation_input_token_cost": 4.125e-06,
@@ -1267,7 +1279,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-sonnet-4-20250514-v1:0": {
        "cache_creation_input_token_cost": 3.75e-06,
@@ -1327,7 +1340,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
    },
    "anthropic.claude-v1": {
        "input_cost_per_token": 8e-06,
@@ -1577,7 +1591,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "apac.anthropic.claude-3-sonnet-20240229-v1:0": {
        "input_cost_per_token": 3e-06,
@@ -1665,7 +1680,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "azure/ada": {
        "input_cost_per_token": 1e-07,
@@ -12187,7 +12203,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": {
        "input_cost_per_token": 3e-06,
@@ -12401,7 +12418,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "eu.meta.llama3-2-1b-instruct-v1:0": {
        "input_cost_per_token": 1.3e-07,
@@ -14604,17 +14622,14 @@
        "uses_embed_content": true
    },
    "vertex_ai/gemini-embedding-2-preview": {
-        "input_cost_per_audio_per_second": 0.00016,
-        "input_cost_per_image": 0.00012,
-        "input_cost_per_token": 2e-07,
-        "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_token": 1.5e-07,
        "litellm_provider": "vertex_ai",
        "max_input_tokens": 8192,
        "max_tokens": 8192,
        "mode": "embedding",
        "output_cost_per_token": 0,
        "output_vector_size": 3072,
-        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
+        "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal",
        "supports_multimodal": true,
        "uses_embed_content": true
    },
@@ -14631,18 +14646,6 @@
        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing",
        "uses_embed_content": true
    },
-    "vertex_ai/gemini-embedding-2-preview": {
-        "input_cost_per_token": 1.5e-07,
-        "litellm_provider": "vertex_ai",
-        "max_input_tokens": 8192,
-        "max_tokens": 8192,
-        "mode": "embedding",
-        "output_cost_per_token": 0,
-        "output_vector_size": 3072,
-        "source": "https://ai.google.dev/gemini-api/docs/embeddings#multimodal",
-        "supports_multimodal": true,
-        "uses_embed_content": true
-    },
    "gemini/gemini-embedding-001": {
        "input_cost_per_token": 1.5e-07,
        "litellm_provider": "gemini",
@@ -16713,7 +16716,8 @@
        "mode": "chat",
        "output_cost_per_token": 2.9e-07,
        "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
    },
    "google.gemma-3-27b-it": {
        "input_cost_per_token": 2.3e-07,
@@ -16724,7 +16728,8 @@
        "mode": "chat",
        "output_cost_per_token": 3.8e-07,
        "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
    },
    "google.gemma-3-4b-it": {
        "input_cost_per_token": 4e-08,
@@ -16735,7 +16740,8 @@
        "mode": "chat",
        "output_cost_per_token": 8e-08,
        "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
    },
    "google_pse/search": {
        "input_cost_per_query": 0.005,
@@ -16770,7 +16776,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "global.anthropic.claude-sonnet-4-20250514-v1:0": {
        "cache_creation_input_token_cost": 3.75e-06,
@@ -16822,7 +16829,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "global.amazon.nova-2-lite-v1:0": {
        "cache_read_input_token_cost": 7.5e-08,
@@ -20551,7 +20559,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "jp.anthropic.claude-haiku-4-5-20251001-v1:0": {
        "cache_creation_input_token_cost": 1.375e-06,
@@ -20573,7 +20582,8 @@
        "supports_response_schema": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
    },
    "lambda_ai/deepseek-llama3.3-70b": {
        "input_cost_per_token": 2e-07,
@@ -21268,7 +21278,8 @@
        "max_tokens": 8192,
        "mode": "chat",
        "output_cost_per_token": 1.2e-06,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "minimax.minimax-m2.1": {
        "input_cost_per_token": 3e-07,
@@ -21281,7 +21292,8 @@
        "supports_function_calling": true,
        "supports_system_messages": true,
        "supports_tool_choice": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
    },
    "minimax/speech-02-hd": {
        "input_cost_per_character": 0.0001,
@@ -21424,7 +21436,8 @@
        "mode": "chat",
        "output_cost_per_token": 2e-07,
        "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral.ministral-3-3b-instruct": {
        "input_cost_per_token": 1e-07,
@@ -21435,7 +21448,8 @@
        "mode": "chat",
        "output_cost_per_token": 1e-07,
        "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral.ministral-3-8b-instruct": {
        "input_cost_per_token": 1.5e-07,
@@ -21446,7 +21460,8 @@
        "mode": "chat",
        "output_cost_per_token": 1.5e-07,
        "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral.mistral-7b-instruct-v0:2": {
        "input_cost_per_token": 1.5e-07,
@@ -21488,7 +21503,8 @@
        "mode": "chat",
        "output_cost_per_token": 1.5e-06,
        "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral.mistral-small-2402-v1:0": {
        "input_cost_per_token": 1e-06,
@@ -21519,7 +21535,8 @@
        "mode": "chat",
        "output_cost_per_token": 4e-08,
        "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral.voxtral-small-24b-2507": {
        "input_cost_per_token": 1e-07,
@@ -21530,7 +21547,8 @@
        "mode": "chat",
        "output_cost_per_token": 3e-07,
        "supports_audio_input": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
    },
    "mistral/codestral-2405": {
        "input_cost_per_token": 1e-06,
@@ -22217,7 +22235,8 @@
        "mode": "chat",
        "output_cost_per_token": 2.5e-06,
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+        "source": "https://aws.amazon.com/bedrock/pricing/",
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@@ -22231,7 +22250,8 @@
        "supports_system_messages": true,
        "supports_tool_choice": true,
        "supports_vision": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
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@@ -23069,7 +23089,8 @@
        "mode": "chat",
        "output_cost_per_token": 6e-07,
        "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
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@@ -23079,7 +23100,8 @@
        "max_tokens": 8192,
        "mode": "chat",
        "output_cost_per_token": 2.3e-07,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
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@@ -23092,7 +23114,8 @@
        "supports_function_calling": true,
        "supports_system_messages": true,
        "supports_tool_choice": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
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    "o1": {
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@@ -26176,7 +26199,8 @@
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        "supports_function_calling": true,
        "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
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@@ -26188,7 +26212,8 @@
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        "supports_function_calling": true,
        "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
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@@ -26200,7 +26225,8 @@
        "output_cost_per_token": 6e-07,
        "supports_function_calling": true,
        "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
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@@ -26212,7 +26238,8 @@
        "output_cost_per_token": 6e-07,
        "supports_function_calling": true,
        "supports_reasoning": true,
-        "supports_tool_choice": true
+        "supports_tool_choice": true,
+        "supports_native_structured_output": true
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@@ -26223,7 +26250,8 @@
        "mode": "chat",
        "output_cost_per_token": 1.2e-06,
        "supports_function_calling": true,
-        "supports_system_messages": true
+        "supports_system_messages": true,
+        "supports_native_structured_output": true
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@@ -26235,7 +26263,8 @@
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        "supports_function_calling": true,
        "supports_system_messages": true,
-        "supports_vision": true
+        "supports_vision": true,
+        "supports_native_structured_output": true
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@@ -26248,7 +26277,8 @@
        "supports_function_calling": true,
        "supports_system_messages": true,
        "supports_tool_choice": true,
-        "source": "https://aws.amazon.com/bedrock/pricing/"
+        "source": "https://aws.amazon.com/bedrock/pricing/",
+        "supports_native_structured_output": true
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    "recraft/recraftv2": {
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@@ -28260,7 +28290,8 @@
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        "supports_tool_choice": true,
        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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@@ -28418,7 +28449,8 @@
        "supports_response_schema": true,
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-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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@@ -28439,7 +28471,8 @@
        "supports_response_schema": true,
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-        "tool_use_system_prompt_tokens": 346
+        "tool_use_system_prompt_tokens": 346,
+        "supports_native_structured_output": true
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@@ -28491,7 +28524,8 @@
        "supports_response_schema": true,
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-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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@@ -28517,7 +28551,8 @@
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        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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@@ -28543,7 +28578,8 @@
        "supports_response_schema": true,
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        "supports_vision": true,
-        "tool_use_system_prompt_tokens": 159
+        "tool_use_system_prompt_tokens": 159,
+        "supports_native_structured_output": true
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@@ -31147,7 +31183,9 @@
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        "output_cost_per_token": 3.2e-06,
        "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#glm-models",
-        "supported_regions": ["global"],
+        "supported_regions": [
+            "global"
+        ],
        "supports_function_calling": true,
        "supports_prompt_caching": true,
        "supports_reasoning": true,