diff --git a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
index 8b94c34942c..de911beabdd 100644
--- a/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
+++ b/litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py
@@ -19,6 +19,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
 from litellm.llms.ollama.common_utils import OllamaResponseIdNormalizer
 from litellm.types.llms.databricks import DatabricksTool
 from litellm.types.llms.openai import (
+    ChatCompletionAnnotation,
     ChatCompletionReasoningItem,
     ChatCompletionThinkingBlock,
     ImageURLListItem,
@@ -59,6 +60,31 @@ _CHOICES_FIELDS: Final[frozenset] = frozenset(Choices.model_fields.keys())
 _MODEL_RESPONSE_FIELDS: Final[frozenset] = frozenset(ModelResponse.model_fields.keys()) | {"usage"}
 
 
+_ANNOTATION_ENTRIES: Final = TypeAdapter(tuple[object, ...])
+
+
+def _is_provider_specific_annotation(annotation: object) -> bool:
+    match annotation:
+        case {"type": "url_citation"}:
+            return False
+        case {"type": _}:
+            return True
+        case _:
+            return False
+
+
+def _annotation_entries(annotations: object) -> tuple[object, ...]:
+    return _ANNOTATION_ENTRIES.validate_python(annotations) if isinstance(annotations, list) else ()
+
+
+def partition_annotations(annotations: object) -> tuple[object, tuple[object, ...]]:
+    entries: Final = _annotation_entries(annotations)
+    others: Final = tuple(annotation for annotation in entries if _is_provider_specific_annotation(annotation))
+    if not others:
+        return annotations, ()
+    return [annotation for annotation in entries if not _is_provider_specific_annotation(annotation)] or None, others
+
+
 def _normalize_images_for_message(
     images: list[dict] | None,
 ) -> list[ImageURLListItem] | None:
@@ -787,6 +813,9 @@ def convert_to_model_response_object(
                     provider_specific_fields = dict(choice["message"].get("provider_specific_fields", None) or {})
                     for f in choice["message"].keys() - _MESSAGE_FIELDS:
                         provider_specific_fields[f] = choice["message"][f]
+                    annotations, other_annotations = partition_annotations(choice["message"].get("annotations", None))
+                    if other_annotations:
+                        provider_specific_fields["annotations"] = list(other_annotations)
 
                     # Handle reasoning models that display `reasoning_content` within `content`
                     reasoning_content, content = extract_reasoning_content(choice["message"])
@@ -809,7 +838,9 @@ def convert_to_model_response_object(
                         reasoning_content=reasoning_content,
                         thinking_blocks=thinking_blocks,
                         reasoning_items=choice["message"].get("reasoning_items", None),
-                        annotations=choice["message"].get("annotations", None),
+                        annotations=cast(  # cast-ok: provider annotation dicts pass through whole, keeping keys such as url_citation.content
+                            list[ChatCompletionAnnotation] | None, annotations
+                        ),
                         images=_normalize_images_for_message(choice["message"].get("images", None)),
                     )
                     finish_reason = choice.get("finish_reason", None)
diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index 9bc53791b36..b3f89b33d1d 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -424,6 +424,109 @@ def anthropic_image_source_to_openai_url(image_source: Mapping[str, object]) ->
     return None
 
 
+@dataclass(frozen=True, slots=True)
+class InlineDocument:
+    filename: str
+    file_data: str
+
+
+def _document_media_type(source: object) -> str:
+    match source:
+        case {"media_type": str() as media_type} if media_type:
+            return media_type
+        case _:
+            return "application/pdf"
+
+
+def anthropic_inline_document(document: Mapping[str, object]) -> InlineDocument | None:
+    source: Final = document.get("source")
+    match source:
+        case {"type": "base64", "data": str() as data} if data:
+            title: Final = document.get("title")
+            return InlineDocument(
+                filename=title if isinstance(title, str) and title else "document.pdf",
+                file_data=f"data:{_document_media_type(source)};base64,{data}",
+            )
+        case _:
+            return None
+
+
+def is_pdf_file_part(part: object) -> bool:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data.startswith("data:"):
+            return file_data.startswith("data:application/pdf")
+        case {"type": "file", "file": {"format": str() as file_format}}:
+            return file_format == "application/pdf"
+        case {"type": "file", "file": {"filename": str() as filename}}:
+            return filename.lower().endswith(".pdf")
+        case _:
+            return False
+
+
+_CONTENT_PARTS: Final = TypeAdapter(tuple[object, ...])
+
+
+def content_parts(message: Mapping[str, object]) -> Sequence[object]:
+    content: Final = message.get("content")
+    return _CONTENT_PARTS.validate_python(content) if isinstance(content, list) else ()
+
+
+def _has_pdf_file_part(message: Mapping[str, object]) -> bool:
+    return any(is_pdf_file_part(part) for part in content_parts(message))
+
+
+def messages_have_pdf_file_part(messages: Sequence[Mapping[str, object]]) -> bool:
+    return any(_has_pdf_file_part(message) for message in messages)
+
+
+def _message_without_pdf_file_parts(message: AllMessageValues) -> AllMessageValues:
+    if not _has_pdf_file_part(message):
+        return message
+    kept: Final = [part for part in content_parts(message) if not is_pdf_file_part(part)]
+    return cast(AllMessageValues, {**message, "content": kept})  # cast-ok: same message with fewer content parts
+
+
+def _is_pdf_only_user_turn(message: AllMessageValues) -> bool:
+    parts: Final = content_parts(message)
+    return message["role"] == "user" and bool(parts) and all(is_pdf_file_part(part) for part in parts)
+
+
+def messages_without_pdf_file_parts(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(
+        _message_without_pdf_file_parts(message) for message in messages if not _is_pdf_only_user_turn(message)
+    )
+
+
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_as_image_url(part: object) -> object:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            fields: Final = _CONTENT_PART.validate_python(part)
+            return {key: value for key, value in fields.items() if key != "file"} | {
+                "type": "image_url",
+                "image_url": {"url": file_data},
+            }
+        case _:
+            return part
+
+
+def message_with_file_parts_as_image_urls(message: Mapping[str, object]) -> Mapping[str, object]:
+    parts: Final = content_parts(message)
+    if not parts:
+        return message
+    return {**message, "content": [_file_part_as_image_url(part) for part in parts]}
+
+
+def _typed_message_with_file_parts_as_image_urls(message: AllMessageValues) -> AllMessageValues:
+    return cast(AllMessageValues, message_with_file_parts_as_image_urls(message))  # cast-ok: same message, image parts
+
+
+def messages_with_file_parts_as_image_urls(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(_typed_message_with_file_parts_as_image_urls(message) for message in messages)
+
+
 def _image_part_url(part: Mapping[str, object]) -> str | None:
     """The image URL carried by one content part, whichever of the three dialects wrote it."""
     part_type: Final = part.get("type")
diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index 7c91ffdff76..8e1791c3c92 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -19,6 +19,7 @@ from litellm.litellm_core_utils.core_helpers import (
 )
 from litellm.litellm_core_utils.hidden_params import HIDDEN_PARAMS_ATTR, set_hidden_params
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import BEDROCK_TRACE_FIELDS, object_items, object_mapping
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import partition_annotations
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     get_content_from_model_response,  # pyright: ignore[reportUnknownVariableType]  # response param accepts an untyped dict
 )
@@ -1700,10 +1701,17 @@ def stream_chunk_builder(
             # Merge annotations from ALL chunks — providers may spread
             # them across multiple streaming chunks or send them only in
             # the final chunk.
-            all_annotations: Final[list] = []
+            all_annotations: Final[list[object]] = []
             for ac in annotation_chunks:
                 all_annotations.extend(ac["choices"][0]["delta"]["annotations"])
-            response["choices"][0]["message"]["annotations"] = all_annotations
+            message_annotations, provider_annotations = partition_annotations(all_annotations)
+            if message_annotations is not None:
+                response["choices"][0]["message"]["annotations"] = message_annotations
+            if provider_annotations:
+                response.choices[0].message.provider_specific_fields = {
+                    **(response.choices[0].message.provider_specific_fields or {}),
+                    "annotations": list(provider_annotations),
+                }
 
         audio_chunks: Final = [
             chunk
@@ -1766,7 +1774,10 @@ def stream_chunk_builder(
 
             if combined_provider_fields:
                 _choice = response.choices[0]
-                _choice.message.provider_specific_fields = combined_provider_fields
+                _choice.message.provider_specific_fields = {
+                    **(_choice.message.provider_specific_fields or {}),
+                    **combined_provider_fields,
+                }
 
         completion_output = get_content_from_model_response(response)
 
diff --git a/litellm/llms/anthropic/messages/adapters/transformation.py b/litellm/llms/anthropic/messages/adapters/transformation.py
index 3524c9e73d8..ddda759384f 100644
--- a/litellm/llms/anthropic/messages/adapters/transformation.py
+++ b/litellm/llms/anthropic/messages/adapters/transformation.py
@@ -123,6 +123,7 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingCho
 
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     anthropic_image_source_to_openai_url,
+    anthropic_inline_document,
     parse_tool_call_arguments,
     reasoning_content_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -185,6 +186,8 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionAssistantMessage,
     ChatCompletionAssistantToolCall,
+    ChatCompletionFileObject,
+    ChatCompletionFileObjectFile,
     ChatCompletionImageObject,
     ChatCompletionImageUrlObject,
     ChatCompletionRedactedThinkingBlock,
@@ -498,7 +501,9 @@ class LiteLLMAnthropicMessagesAdapter:
         for m in ordered_messages:
             user_message: ChatCompletionUserMessage | None = None
             tool_message_list: list[ChatCompletionToolMessage] = []
-            new_user_content_list: list[ChatCompletionTextObject | ChatCompletionImageObject] = []
+            new_user_content_list: list[
+                ChatCompletionTextObject | ChatCompletionImageObject | ChatCompletionFileObject
+            ] = []
             if m["role"] == "system":
                 system_message = self._translate_midturn_system_message_to_openai(m, model)
                 if system_message is not None:
@@ -531,13 +536,8 @@ class LiteLLMAnthropicMessagesAdapter:
                                     self._add_prompt_cache_breakpoint_if_present(content, image_obj)
                                 )
                         elif content.get("type") == "document":
-                            # Convert Anthropic document format (PDF, etc.) to OpenAI format
-                            source = content.get("source", {})
-                            openai_image_url = self._translate_anthropic_image_to_openai(cast(dict, source))
-
-                            if openai_image_url:
-                                image_url_obj = ChatCompletionImageUrlObject(url=openai_image_url)
-                                doc_obj = ChatCompletionImageObject(type="image_url", image_url=image_url_obj)
+                            doc_obj = self._translate_anthropic_document_to_openai(content)
+                            if doc_obj is not None:
                                 self._add_cache_control_if_applicable(content, doc_obj, model)
                                 new_user_content_list.append(doc_obj)
                         elif content.get("type") == "tool_result":
@@ -1357,7 +1357,7 @@ class LiteLLMAnthropicMessagesAdapter:
                 text: Final = tool_result_text_part(block)
                 return None if text is None else ChatCompletionTextObject(type="text", text=text)
             case "image" | "document":
-                return self._tool_result_image_part(block.get("source"))
+                return self._source_image_url_part(block.get("source"))
             case "tool_reference":
                 return ChatCompletionToolReferenceObject(
                     type="tool_reference", tool_name=str(block.get("tool_name") or "")
@@ -1365,7 +1365,18 @@ class LiteLLMAnthropicMessagesAdapter:
             case _:
                 return None
 
-    def _tool_result_image_part(self, image_source: object) -> ChatCompletionImageObject | None:
+    def _translate_anthropic_document_to_openai(
+        self, document: Mapping[str, object]
+    ) -> ChatCompletionFileObject | ChatCompletionImageObject | None:
+        inline_document: Final = anthropic_inline_document(document)
+        if inline_document is None:
+            return self._source_image_url_part(document.get("source"))
+        return ChatCompletionFileObject(
+            type="file",
+            file=ChatCompletionFileObjectFile(filename=inline_document.filename, file_data=inline_document.file_data),
+        )
+
+    def _source_image_url_part(self, image_source: object) -> ChatCompletionImageObject | None:
         if not isinstance(image_source, dict):
             return None
         openai_image_url = self._translate_anthropic_image_to_openai(image_source)
diff --git a/litellm/llms/anthropic/messages/responses_adapters/transformation.py b/litellm/llms/anthropic/messages/responses_adapters/transformation.py
index 1dd07c1739b..94c608f90e6 100644
--- a/litellm/llms/anthropic/messages/responses_adapters/transformation.py
+++ b/litellm/llms/anthropic/messages/responses_adapters/transformation.py
@@ -13,6 +13,7 @@ from typing import Any, Final, cast
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     TOOL_RESULT_IMAGE_BOUNDARY,
     TOOL_RESULT_IMAGE_PLACEHOLDER,
+    anthropic_inline_document,
     encrypted_reasoning_signature,
     responses_reasoning_items_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -106,27 +107,18 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
         block: Mapping[str, object],
     ) -> dict[str, str] | None:  # mutable-ok: API message payload
         """Convert an Anthropic document block to a Responses input_file part."""
+        inline_document: Final = anthropic_inline_document(block)
+        if inline_document is not None:
+            return {
+                "type": "input_file",
+                "filename": inline_document.filename,
+                "file_data": inline_document.file_data,
+            }
         raw_source: Final = block.get("source")
         if not isinstance(raw_source, Mapping):
             return None
         source: Final = cast(Mapping[str, object], raw_source)  # cast-ok: untrusted client payload
-        source_type: Final = source.get("type")
-        if source_type == "base64":
-            data: Final = source.get("data")
-            if not isinstance(data, str) or not data:
-                return None
-            raw_media_type: Final = source.get("media_type")
-            media_type: Final = (
-                raw_media_type if isinstance(raw_media_type, str) and raw_media_type else "application/pdf"
-            )
-            raw_title: Final = block.get("title")
-            filename: Final = raw_title if isinstance(raw_title, str) and raw_title else "document.pdf"
-            return {
-                "type": "input_file",
-                "filename": filename,
-                "file_data": f"data:{media_type};base64,{data}",
-            }
-        if source_type == "url":
+        if source.get("type") == "url":
             url: Final = source.get("url")
             if not isinstance(url, str) or not url:
                 return None
diff --git a/litellm/llms/azure_ai/chat/transformation.py b/litellm/llms/azure_ai/chat/transformation.py
index 8bb6072d0a9..d382d653a53 100644
--- a/litellm/llms/azure_ai/chat/transformation.py
+++ b/litellm/llms/azure_ai/chat/transformation.py
@@ -1,7 +1,7 @@
 import copy
 import enum
 import re
-from collections.abc import AsyncIterator, Collection, Iterator
+from collections.abc import AsyncIterator, Collection, Iterator, Mapping
 from typing import TYPE_CHECKING, Final, cast
 
 import httpx
@@ -10,10 +10,11 @@ from httpx import Response
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
-    audio_or_image_in_message_content,
+    content_parts,
     convert_content_list_to_str,
     drop_tool_reference_parts_from_tool_messages,
     filter_value_from_dict,
+    is_pdf_file_part,
 )
 from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.azure_ai.common_utils import (
@@ -46,6 +47,20 @@ NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = (
     "cache_control",
 )
 
+LIST_ONLY_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_audio"})
+
+
+def _is_list_only_content_part(part: object) -> bool:
+    match part:
+        case {"type": str() as part_type} if part_type in LIST_ONLY_CONTENT_PART_TYPES:
+            return True
+        case _:
+            return is_pdf_file_part(part)
+
+
+def _has_list_only_content_part(message: Mapping[str, object]) -> bool:
+    return any(_is_list_only_content_part(part) for part in content_parts(message))
+
 
 class AzureAIChatCompletionResponseIterator(OpenAIChatCompletionResponseIterator):
     def chunk_parser(self, chunk: dict[str, object]) -> ModelResponseStream:  # mutable-ok: mirrors override contract
@@ -246,7 +261,7 @@ class AzureAIStudioConfig(OpenAIConfig):
                these with "Extra inputs are not permitted", which breaks multi-turn
                Anthropic-format clients that echo thinking blocks back as history.
             2. Transforms list content to a string.
-            3. If message contains an image or audio, send as is (user-intended)
+            3. If message contains an image, audio, or PDF file part, send as is (user-intended)
 
         Operates on a deep copy so the caller's messages keep their thinking blocks
         and provider metadata, which a fallback to another provider still needs.
@@ -257,8 +272,7 @@ class AzureAIStudioConfig(OpenAIConfig):
             for field in NON_OPENAI_SPEC_MESSAGE_FIELDS:
                 filter_value_from_dict(message_dict, field)
 
-            # Do nothing if the message contains an image or audio
-            if audio_or_image_in_message_content(message):
+            if _has_list_only_content_part(message):
                 continue
 
             texts = convert_content_list_to_str(message=message)
diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py
index 65921b628fb..81a186eadf4 100644
--- a/litellm/llms/gigachat/chat/transformation.py
+++ b/litellm/llms/gigachat/chat/transformation.py
@@ -38,6 +38,15 @@ else:
 
 _EMPTY_FUNCTION: Final[Mapping[str, object]] = MappingProxyType({})
 _JSON_VALUE: Final = TypeAdapter(object)
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_data(part: Mapping[str, object]) -> str | None:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            return file_data
+        case _:
+            return None
 
 
 def is_valid_json(value: str) -> bool:
@@ -295,7 +304,7 @@ class GigaChatConfig(BaseConfig):
 
     def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[object]]:
         """
-        Extract text and image attachments from a multimodal message content list.
+        Extract text, image and file attachments from a multimodal message content list.
 
         Args:
             content: List of content parts (OpenAI multimodal format)
@@ -320,6 +329,10 @@ class GigaChatConfig(BaseConfig):
                         file_id = self._upload_image(upload_url)
                         if file_id:
                             attachments.append(file_id)
+                elif (file_data := _file_part_data(_CONTENT_PART.validate_python(part))) and (
+                    uploaded_file_id := self._upload_image(file_data)
+                ):
+                    attachments.append(uploaded_file_id)
         text: Final = "\n".join(texts) if texts else ""
         return text, attachments
 
diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py
index e8d37f857e6..8257a75b4a5 100644
--- a/litellm/llms/oci/chat/transformation.py
+++ b/litellm/llms/oci/chat/transformation.py
@@ -20,6 +20,7 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_OCI_CHAT_MAX_TOKENS
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
+from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
@@ -681,7 +682,9 @@ class OCIChatConfig(BaseConfig):
                 servingMode=serving_mode,
                 chatRequest=OCIChatRequestPayload(
                     apiFormat=vendor.value,
-                    messages=adapt_messages_to_generic_oci_standard(messages),
+                    messages=adapt_messages_to_generic_oci_standard(
+                        list(messages_with_file_parts_as_image_urls(messages))
+                    ),
                     **self._get_optional_params(vendor, optional_params, model),
                 ),
             )
diff --git a/litellm/llms/sap/chat/transformation.py b/litellm/llms/sap/chat/transformation.py
index 622c28f3c90..0270f814328 100755
--- a/litellm/llms/sap/chat/transformation.py
+++ b/litellm/llms/sap/chat/transformation.py
@@ -11,6 +11,7 @@ import httpx
 from pydantic import BaseModel
 
 import litellm
+from litellm.litellm_core_utils.prompt_templates.common_utils import message_with_file_parts_as_image_urls
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ModelResponse
@@ -104,7 +105,7 @@ def _message_role_mapping(role: str) -> type[ChatMessage]:
 def _messages_to_sap_template(messages: list[AllMessageValues]) -> list:
     template: Final = []
     for message in messages:
-        folded_message = _fold_message_cache_control(message)
+        folded_message = _fold_message_cache_control(message_with_file_parts_as_image_urls(message))
         message_role_class = _message_role_mapping(str(folded_message["role"]))
         validated_message = validate_dict(folded_message, message_role_class)
         template.append(validated_message)
diff --git a/litellm/llms/snowflake/chat/transformation.py b/litellm/llms/snowflake/chat/transformation.py
index 030413030eb..928dcc3bc52 100644
--- a/litellm/llms/snowflake/chat/transformation.py
+++ b/litellm/llms/snowflake/chat/transformation.py
@@ -112,7 +112,7 @@ def _convert_image_url_to_anthropic(block: Mapping[str, object]) -> object:
     pdf/text data URIs become document blocks rather than malformed image blocks.
     """
     image_url: Final = block.get("image_url")
-    url: Final = image_url if isinstance(image_url, str) else _image_url_field(image_url, "url")
+    url: Final = image_url if isinstance(image_url, str) else _str_field(image_url, "url")
     if not url:
         return block
 
@@ -121,18 +121,31 @@ def _convert_image_url_to_anthropic(block: Mapping[str, object]) -> object:
         if select_anthropic_content_block_type_for_file(_data_uri_media_type(url)) == "document"
         else create_anthropic_image_param(
             image_url if isinstance(image_url, dict) else url,
-            format=_image_url_field(image_url, "format"),
+            format=_str_field(image_url, "format"),
             is_bedrock_invoke=True,
         )
     )
+    return _with_cache_control(converted, block)
+
+
+def _convert_file_to_anthropic(block: Mapping[str, object]) -> object:
+    file_data: Final = _str_field(block.get("file"), "file_data")
+    if not file_data:
+        return block
+    return _with_cache_control(
+        anthropic_process_openai_file_message({"type": "file", "file": {"file_data": file_data}}), block
+    )
+
+
+def _with_cache_control(converted: Mapping[str, object], block: Mapping[str, object]) -> Mapping[str, object]:
     cache_control: Final = block.get("cache_control")
     if cache_control is None:
         return converted
     return {**converted, "cache_control": cache_control}
 
 
-def _image_url_field(image_url: object, key: str) -> str | None:
-    value: Final = image_url.get(key) if isinstance(image_url, dict) else None
+def _str_field(container: object, key: str) -> str | None:
+    value: Final = container.get(key) if isinstance(container, dict) else None
     return value if isinstance(value, str) else None
 
 
@@ -141,15 +154,23 @@ def _data_uri_media_type(url: str) -> str:
     return match.group(1) if match else ""
 
 
-def _convert_image_url_blocks_to_anthropic(content: object) -> object:
+def _convert_media_block_to_anthropic(block: object) -> object:
+    mapping: Final = as_str_mapping(block)
+    if mapping is None:
+        return block
+    match mapping.get("type"):
+        case "image_url":
+            return _convert_image_url_to_anthropic(mapping)
+        case "file":
+            return _convert_file_to_anthropic(mapping)
+        case _:
+            return block
+
+
+def _convert_media_blocks_to_anthropic(content: object) -> object:
     if not isinstance(content, list):
         return content
-    return [
-        _convert_image_url_to_anthropic(block)
-        if isinstance(block, Mapping) and block.get("type") == "image_url"
-        else block
-        for block in content
-    ]
+    return [_convert_media_block_to_anthropic(block) for block in content]
 
 
 def _convert_tool_result_to_anthropic(
@@ -391,7 +412,7 @@ class SnowflakeConfig(SnowflakeBaseConfig, OpenAIGPTConfig):
                 conversation.append(
                     {
                         "role": role,
-                        "content": _convert_image_url_blocks_to_anthropic(content),
+                        "content": _convert_media_blocks_to_anthropic(content),
                     }
                 )
 
diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
index bb200fd542d..a3a3427670d 100644
--- a/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
+++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/main.py
@@ -9,6 +9,7 @@ import httpx
 
 import litellm
 from litellm import LlmProviders
+from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
 from litellm.types.llms.vertex_ai import VertexPartnerProvider
 from litellm.utils import ModelResponse
 
@@ -217,7 +218,7 @@ class VertexAIPartnerModels(VertexBase):
                 return base_llm_http_handler.completion(
                     model=model,
                     stream=stream,
-                    messages=messages,
+                    messages=list(messages_with_file_parts_as_image_urls(messages)),
                     acompletion=acompletion,
                     api_base=api_base,
                     model_response=model_response,
diff --git a/litellm/main.py b/litellm/main.py
index d1af2de3a13..50ab274a060 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -108,6 +108,10 @@ from litellm.litellm_core_utils.mock_functions import (
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     get_content_from_model_response as get_content_from_model_response,  # noqa: PLC0414  # public re-export
 )
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    messages_have_pdf_file_part,
+    messages_without_pdf_file_parts,
+)
 from litellm.litellm_core_utils.provider_affinity import add_provider_affinity_header
 from litellm.litellm_core_utils.request_timeout_resolver import (
     get_configured_request_timeout,
@@ -191,11 +195,13 @@ from litellm.utils import (
     get_requester_metadata,
     get_secret,
     get_standard_openai_params,
+    is_explicitly_disabled_factory,
     mock_completion_streaming_obj,
     pre_process_non_default_params,
     read_config_args,
     should_run_mock_completion,
     supports_httpx_timeout,
+    supports_pdf_input,
     validate_and_fix_openai_messages,
     validate_and_fix_openai_tools,
     validate_and_fix_thinking_param,
@@ -393,6 +399,125 @@ def _resolve_control_options(kwargs: Mapping[str, object], model: str) -> Contro
             return assert_never(control)
 
 
+PROVIDERS_WITHOUT_DOCUMENT_INPUT: Final = frozenset({"ollama", "ollama_chat"})
+PROVIDERS_DROPPING_UNDECLARED_PDF_INPUT: Final = frozenset({"azure_ai"})
+
+
+def _cost_map_pdf_input_support(model: str, custom_llm_provider: str) -> bool | None:
+    if custom_llm_provider in PROVIDERS_WITHOUT_DOCUMENT_INPUT or is_explicitly_disabled_factory(
+        model, custom_llm_provider, "supports_pdf_input"
+    ):
+        return False
+    if supports_pdf_input(model, custom_llm_provider):
+        return True
+    return None
+
+
+def _deployment_pdf_input_support(model: str, base_model: str, custom_llm_provider: str) -> bool | None:
+    supports: Final = (
+        _cost_map_pdf_input_support(model, custom_llm_provider),
+        _cost_map_pdf_input_support(base_model, custom_llm_provider),
+    )
+    if False in supports:
+        return False
+    if True in supports:
+        return True
+    return None
+
+
+def _declared_pdf_input_support(
+    model: str, custom_llm_provider: str, model_info: Mapping[str, object] | None, base_model: str | None
+) -> bool | None:
+    match model_info:
+        case {"supports_pdf_input": bool() as declared}:
+            return declared
+        case _ if base_model:
+            return _deployment_pdf_input_support(model, base_model, custom_llm_provider)
+        case _:
+            return _cost_map_pdf_input_support(model, custom_llm_provider)
+
+
+def _pdf_input_unreadable(
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+) -> bool:
+    match _declared_pdf_input_support(model, custom_llm_provider, model_info, base_model):
+        case False:
+            return True
+        case None:
+            return drop_params and custom_llm_provider in PROVIDERS_DROPPING_UNDECLARED_PDF_INPUT
+        case True:
+            return False
+
+
+def _messages_without_unreadable_pdfs(
+    messages: Sequence[AllMessageValues],
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+) -> tuple[AllMessageValues, ...] | None:
+    if not messages_have_pdf_file_part(messages) or not _pdf_input_unreadable(
+        model, custom_llm_provider, model_info, base_model, drop_params
+    ):
+        return None
+    if not drop_params:
+        raise litellm.BadRequestError(
+            message=(
+                f"{custom_llm_provider}/{model} does not accept PDF input. "
+                "Remove the PDF, or set drop_params=True to send the request without it"
+            ),
+            model=model,
+            llm_provider=custom_llm_provider,
+        )
+    readable_messages: Final = messages_without_pdf_file_parts(messages)
+    if not any(message["role"] == "user" for message in readable_messages):
+        raise litellm.BadRequestError(
+            message=(
+                f"{custom_llm_provider}/{model} does not accept PDF input, "
+                "and no user message is left once the PDF is dropped"
+            ),
+            model=model,
+            llm_provider=custom_llm_provider,
+        )
+    return readable_messages
+
+
+def _messages_readable_by_model(
+    messages: Sequence[AllMessageValues],
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+    ensure_alternating_roles: bool | None,
+    user_continue_message: ChatCompletionUserMessage | None,
+    assistant_continue_message: ChatCompletionAssistantMessage | None,
+) -> tuple[AllMessageValues, ...] | None:
+    pdf_free_messages: Final = _messages_without_unreadable_pdfs(
+        messages=messages,
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        model_info=model_info,
+        base_model=base_model,
+        drop_params=drop_params,
+    )
+    if pdf_free_messages is None:
+        return None
+    return tuple(
+        get_completion_messages(
+            messages=list(pdf_free_messages),
+            ensure_alternating_roles=ensure_alternating_roles or False,
+            user_continue_message=user_continue_message,
+            assistant_continue_message=assistant_continue_message,
+        )
+    )
+
+
 ####### COMPLETION ENDPOINTS ################
 
 
@@ -4234,7 +4359,6 @@ def _complete_sagemaker(ctx: CompletionDispatchContext) -> _CompletionDispatchRe
 _ADDITIONAL_DROP_PARAMS_ADAPTER: Final = TypeAdapter(list[str])
 _OPTIONAL_PARAMS_ADAPTER: Final = TypeAdapter(dict[str, object])
 _OLLAMA_EMBEDDING_HEADERS_ADAPTER: Final = TypeAdapter(dict[str, str])
-_REQUEST_KWARGS_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _MODEL_INFO_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _PROVIDER_ROUTING_HINTS: Final = TypeAdapter(ProviderRoutingHints)
 _BASE_URL_HINT: Final = TypeAdapter(BaseUrlRoutingHint)
@@ -5558,9 +5682,15 @@ def completion(
     )
     headers = kwargs.get("headers", None) or extra_headers
 
-    ensure_alternating_roles: Final[bool | None] = kwargs.get("ensure_alternating_roles", None)
-    user_continue_message: Final[ChatCompletionUserMessage | None] = kwargs.get("user_continue_message", None)
-    assistant_continue_message: ChatCompletionAssistantMessage | None = kwargs.get("assistant_continue_message", None)
+    ensure_alternating_roles: Final = cast(  # cast-ok: read from untyped kwargs
+        bool | None, kwargs.get("ensure_alternating_roles", None)
+    )
+    user_continue_message: Final = cast(  # cast-ok: read from untyped kwargs
+        ChatCompletionUserMessage | None, kwargs.get("user_continue_message", None)
+    )
+    assistant_continue_message: Final = cast(  # cast-ok: read from untyped kwargs
+        ChatCompletionAssistantMessage | None, kwargs.get("assistant_continue_message", None)
+    )
     headers = {} if headers is None else dict(headers)
     if extra_headers is not None:
         headers.update(extra_headers)
@@ -5928,15 +6058,17 @@ def completion(
         def optional_params_for_model_info(deployment_info: Mapping[str, object] | None) -> dict[str, object]:
             return get_optional_params(**mapping_args, model_info=deployment_info, **mapping_special_params)
 
-        optional_params = optional_params_for_model_info(
+        validated_model_info: Final = (
             _MODEL_INFO_ADAPTER.validate_python(model_info) if isinstance(model_info, dict) else None
         )
+        deployment_base_model: Final = base_model if isinstance(base_model, str) else None
+        optional_params = optional_params_for_model_info(validated_model_info)
         fakes_stream: Final = partial(
             _completion_fakes_stream,
             model,
             custom_llm_provider,
             provider_config,
-            base_model=base_model if isinstance(base_model, str) else None,
+            base_model=deployment_base_model,
             bridges_to_responses_api=bridges_to_responses_api is True,
         )
         resolve_timeout: Final = partial(
@@ -5974,16 +6106,29 @@ def completion(
             functions_unsupported_model: Final[list[object]] = TypeAdapter(list[object]).validate_python(
                 optional_params.pop("functions_unsupported_model")
             )
-            request_kwargs: Final = _REQUEST_KWARGS_ADAPTER.validate_python(kwargs)
             messages = function_call_prompt(
                 messages=messages,
                 functions=functions_unsupported_model,
                 model=model,
                 llm_provider=custom_llm_provider,
-                drop_params=litellm.drop_params is True
-                or normalize_drop_params(request_kwargs.get("drop_params")) is True,
+                drop_params=drop_unsupported_reasoning,
             )
 
+        readable_messages: Final = _messages_readable_by_model(
+            messages=cast(list[AllMessageValues], messages),  # cast-ok: completion types messages as a bare List
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            model_info=validated_model_info,
+            base_model=deployment_base_model,
+            drop_params=drop_unsupported_reasoning,
+            ensure_alternating_roles=ensure_alternating_roles,
+            user_continue_message=user_continue_message,
+            assistant_continue_message=assistant_continue_message,
+        )
+        messages = (  # rebind-ok: completion() rewrites messages step by step
+            messages if readable_messages is None else list(readable_messages)
+        )
+
         # For logging - save the values of the litellm-specific params passed in
         requested_litellm_params: Final = get_litellm_params(
             acompletion=acompletion,
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 1256fef9181..c6bbb63b6fc 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -4049,6 +4049,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
@@ -12437,7 +12438,8 @@
         "supported_output_modalities": [
             "text"
         ],
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_pdf_input": false
     },
     "azure_ai/FLUX-1.1-pro": {
         "deprecation_date": "2027-01-05",
@@ -12502,6 +12504,7 @@
         "output_cost_per_token": 1.85e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12516,6 +12519,7 @@
         "output_cost_per_token": 3.828e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12531,6 +12535,7 @@
         "output_cost_per_token": 3.52e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12546,6 +12551,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12560,6 +12566,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12574,6 +12581,7 @@
         "output_cost_per_token": 7.26e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12594,6 +12602,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12616,6 +12625,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12638,6 +12648,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12660,6 +12671,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12687,6 +12699,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12703,6 +12716,7 @@
         "output_cost_per_token": 1.32e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12724,6 +12738,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12744,6 +12759,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12765,6 +12781,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12833,6 +12850,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12847,6 +12865,7 @@
         "output_cost_per_token": 7.1e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": {
@@ -12859,6 +12878,7 @@
         "output_cost_per_token": 1e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12872,6 +12892,7 @@
         "output_cost_per_token": 7.8e-07,
         "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12884,6 +12905,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -12895,6 +12917,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -12906,6 +12929,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12918,6 +12942,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12930,6 +12955,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12942,6 +12968,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
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+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_vision": true
     },
@@ -67478,7 +67564,8 @@
         "output_cost_per_token": 5.4e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/deepseek-v3-0324": {
         "deprecation_date": "2026-07-13",
@@ -67486,7 +67573,8 @@
         "output_cost_per_token": 4.56e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/deepseek-v3.1": {
         "deprecation_date": "2026-07-13",
@@ -67494,7 +67582,8 @@
         "output_cost_per_token": 4.94e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-3": {
         "deprecation_date": "2026-05-01",
@@ -67502,7 +67591,8 @@
         "output_cost_per_token": 1.5e-05,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-3-mini": {
         "deprecation_date": "2026-05-01",
@@ -67510,7 +67600,8 @@
         "output_cost_per_token": 1.27e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-4-fast-non-reasoning": {
         "deprecation_date": "2026-05-01",
@@ -67518,7 +67609,8 @@
         "output_cost_per_token": 5e-07,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-4-fast-reasoning": {
         "deprecation_date": "2026-05-01",
@@ -67526,7 +67618,8 @@
         "output_cost_per_token": 5e-07,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": {
         "input_cost_per_token": 7.2e-08,
@@ -73196,6 +73289,7 @@
         "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
         "supported_regions": ["global"],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -73811,6 +73905,7 @@
         "output_cost_per_token": 3.5e-06,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -74865,6 +74960,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": false,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -75113,6 +75209,7 @@
         "output_cost_per_token_above_200k_tokens": 1.2e-05,
         "source": "https://docs.x.ai/developers/models/grok-4.7",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
diff --git a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
index 0f66135d038..8e2fea415ef 100644
--- a/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py
@@ -647,9 +647,10 @@ class PromptSecurityGuardrail(CustomGuardrail):
     async def _process_document_item(self, item: dict, user_api_key_alias: str | None) -> dict:
         """Process and sanitize document/file items."""
         doc_data: Final = item.get("document") or item.get("file") or item
+        url_key: Final = "file_data" if isinstance(doc_data, dict) and not doc_data.get("url") else "url"
 
         if isinstance(doc_data, dict):
-            url = doc_data.get("url", "")
+            url = doc_data.get(url_key) or ""
             doc_content = doc_data.get("data", "")
         else:
             url = doc_data if isinstance(doc_data, str) else ""
@@ -698,7 +699,7 @@ class PromptSecurityGuardrail(CustomGuardrail):
                     if url.startswith("data:") and header:
                         sanitized_url: Final = f"{header},{sanitized_encoded}"
                         if isinstance(doc_data, dict):
-                            doc_data["url"] = sanitized_url
+                            doc_data[url_key] = sanitized_url
                     elif isinstance(doc_data, dict):
                         doc_data["data"] = sanitized_encoded
 
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index e9984a35ad1..448bd022989 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -4147,7 +4147,12 @@ class CustomPricingLiteLLMParams(MirroredPricingParams):
 DEPLOYMENT_SCOPED_PRICING_FIELDS: Final[frozenset[str]] = frozenset({"off_peak_pricing"})
 
 DEPLOYMENT_SCOPED_CAPABILITY_FIELDS: Final[frozenset[str]] = frozenset(
-    {"supports_regex_lookaround", "supports_assistant_prefill", "supports_bedrock_native_structured_output"}
+    {
+        "supports_regex_lookaround",
+        "supports_assistant_prefill",
+        "supports_bedrock_native_structured_output",
+        "supports_pdf_input",
+    }
 )
 
 DEPLOYMENT_SCOPED_TOKEN_COUNT_FIELDS: Final[frozenset[str]] = frozenset({"token_count_framing", "token_count_model"})
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 1256fef9181..c6bbb63b6fc 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -4049,6 +4049,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
@@ -12437,7 +12438,8 @@
         "supported_output_modalities": [
             "text"
         ],
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_pdf_input": false
     },
     "azure_ai/FLUX-1.1-pro": {
         "deprecation_date": "2027-01-05",
@@ -12502,6 +12504,7 @@
         "output_cost_per_token": 1.85e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12516,6 +12519,7 @@
         "output_cost_per_token": 3.828e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12531,6 +12535,7 @@
         "output_cost_per_token": 3.52e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12546,6 +12551,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12560,6 +12566,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12574,6 +12581,7 @@
         "output_cost_per_token": 7.26e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12594,6 +12602,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12616,6 +12625,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12638,6 +12648,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12660,6 +12671,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12687,6 +12699,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12703,6 +12716,7 @@
         "output_cost_per_token": 1.32e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12724,6 +12738,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12744,6 +12759,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12765,6 +12781,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12833,6 +12850,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12847,6 +12865,7 @@
         "output_cost_per_token": 7.1e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": {
@@ -12859,6 +12878,7 @@
         "output_cost_per_token": 1e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12872,6 +12892,7 @@
         "output_cost_per_token": 7.8e-07,
         "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12884,6 +12905,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -12895,6 +12917,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -12906,6 +12929,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12918,6 +12942,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12930,6 +12955,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12942,6 +12968,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12954,6 +12981,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12966,6 +12994,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12978,6 +13007,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.4e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12990,6 +13020,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -13002,6 +13033,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -13015,6 +13047,7 @@
         "output_cost_per_token": 5e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -13027,7 +13060,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
-        "supports_function_calling": true
+        "supports_function_calling": true,
+        "supports_pdf_input": false
     },
     "azure_ai/Phi-4-multimodal-instruct": {
         "deprecation_date": "2027-01-17",
@@ -13041,6 +13075,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_audio_input": true,
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_vision": true
     },
     "azure_ai/Phi-4-mini-reasoning": {
@@ -13052,7 +13087,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
-        "supports_function_calling": true
+        "supports_function_calling": true,
+        "supports_pdf_input": false
     },
     "azure_ai/Phi-4-reasoning": {
         "deprecation_date": "2027-01-17",
@@ -13064,6 +13100,7 @@
         "output_cost_per_token": 5e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
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         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-4-fast-reasoning": {
         "deprecation_date": "2026-05-01",
@@ -67526,7 +67618,8 @@
         "output_cost_per_token": 5e-07,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": {
         "input_cost_per_token": 7.2e-08,
@@ -73196,6 +73289,7 @@
         "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
         "supported_regions": ["global"],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -73811,6 +73905,7 @@
         "output_cost_per_token": 3.5e-06,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -74865,6 +74960,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": false,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -75113,6 +75209,7 @@
         "output_cost_per_token_above_200k_tokens": 1.2e-05,
         "source": "https://docs.x.ai/developers/models/grok-4.7",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
diff --git a/tests/integration/messages_endpoint/chat_bridge/test_anthropic_messages_pdf_document_wire.py b/tests/integration/messages_endpoint/chat_bridge/test_anthropic_messages_pdf_document_wire.py
new file mode 100644
index 00000000000..130bab24f4c
--- /dev/null
+++ b/tests/integration/messages_endpoint/chat_bridge/test_anthropic_messages_pdf_document_wire.py
@@ -0,0 +1,421 @@
+import json
+import uuid
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Final
+
+import anthropic
+import pytest
+from integration._support.client import Gateway, Scenario, string_value
+from integration._support.pdf_document import LETTER, Page, document, encoded, pdf_bytes
+from integration._support.process import owned_proxy_process
+from integration._support.wire import Reply, Request, Wire, wire_server
+from pydantic import JsonValue, TypeAdapter
+
+_API_KEY: Final = "synthetic-pdf-document-key"
+_ANSWER: Final = "The codeword is PELICAN-42."
+_TITLE: Final = "Quarterly report"
+_URL: Final = "https://example.com/brief.pdf"
+_SAP_TOKEN_TARGET: Final = "/oauth/token"
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_PARTS: Final = TypeAdapter(list[dict[str, JsonValue]])
+
+
+def _pdf(text: str) -> str:
+    return encoded(pdf_bytes((Page(text=text), LETTER)))
+
+
+def _chat_completion(identity: str, model: str) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "id": identity,
+                "object": "chat.completion",
+                "created": 1,
+                "model": model,
+                "choices": [
+                    {"index": 0, "message": {"role": "assistant", "content": _ANSWER}, "finish_reason": "stop"}
+                ],
+                "usage": {"prompt_tokens": 914, "completion_tokens": 9, "total_tokens": 923},
+            }
+        ).encode()
+    )
+
+
+def _only_body(wire: Wire) -> dict[str, JsonValue]:
+    received: Final = tuple(
+        request
+        for request in wire.drain()
+        if request.method == "POST" and not request.target.startswith(_SAP_TOKEN_TARGET)
+    )
+    assert len(received) == 1, received
+    return _JSON_OBJECT.validate_json(received[0].body)
+
+
+def _user_parts(body: dict[str, JsonValue], key: str) -> list[dict[str, JsonValue]]:
+    turns: Final = _PARTS.validate_python(body[key])
+    assert len(turns) == 1, turns
+    return _PARTS.validate_python(turns[0]["content" if key == "messages" else "parts"])
+
+
+def test_anthropic_sdk_pdf_documents_reach_azure_as_file_parts_named_by_title(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    backend: Final = f"gpt-pdf-{marker[:8]}"
+    identity: Final = f"chatcmpl-{marker}"
+    titled: Final = _pdf(f"PELICAN-42 {marker}")
+    untitled: Final = _pdf(f"ORCHID-77 {marker}")
+    question: Final = f"What codeword does each document carry? {marker}"
+    with wire_server(lambda _: _chat_completion(identity, backend)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(
+            model=f"azure/{backend}", api_base=wire.url, api_key=_API_KEY, api_version="2024-10-21"
+        )
+        client: Final = anthropic.Anthropic(base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0)
+        message: Final = client.messages.create(
+            model=model,
+            max_tokens=64,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "title": _TITLE,
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": titled},
+                        },
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": untitled},
+                        },
+                        {"type": "document", "source": {"type": "url", "url": _URL}},
+                        {"type": "text", "text": question},
+                    ],
+                }
+            ],
+        )
+        assert message.id == identity
+        assert [(block.type, getattr(block, "text", None)) for block in message.content] == [("text", _ANSWER)]
+        assert _user_parts(_only_body(wire), "messages") == [
+            {"type": "file", "file": {"filename": _TITLE, "file_data": f"data:application/pdf;base64,{titled}"}},
+            {
+                "type": "file",
+                "file": {"filename": "document.pdf", "file_data": f"data:application/pdf;base64,{untitled}"},
+            },
+            {"type": "image_url", "image_url": {"url": _URL}},
+            {"type": "text", "text": question},
+        ]
+
+
+@dataclass(frozen=True, slots=True)
+class _Reader:
+    deploy: Callable[[Scenario, Wire], str]
+    reply: Callable[[Request], Reply]
+    pdf_parts: Callable[[dict[str, JsonValue]], Sequence[JsonValue]]
+
+
+def _gemini_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "candidates": [
+                    {"content": {"parts": [{"text": _ANSWER}], "role": "model"}, "finishReason": "STOP", "index": 0}
+                ],
+                "usageMetadata": {"promptTokenCount": 300, "candidatesTokenCount": 9, "totalTokenCount": 309},
+                "modelVersion": "gemini-2.5-flash",
+            }
+        ).encode()
+    )
+
+
+def _bedrock_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "output": {"message": {"role": "assistant", "content": [{"text": _ANSWER}]}},
+                "stopReason": "end_turn",
+                "usage": {"inputTokens": 300, "outputTokens": 9, "totalTokens": 309},
+                "metrics": {"latencyMs": 1},
+            }
+        ).encode()
+    )
+
+
+def _anthropic_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "id": "msg_pdf",
+                "type": "message",
+                "role": "assistant",
+                "model": "claude-sonnet-4-5",
+                "content": [{"type": "text", "text": _ANSWER}],
+                "stop_reason": "end_turn",
+                "usage": {"input_tokens": 300, "output_tokens": 9},
+            }
+        ).encode()
+    )
+
+
+def _sap_reply(request: Request) -> Reply:
+    if request.target.startswith(_SAP_TOKEN_TARGET):
+        return Reply(body=json.dumps({"access_token": "scripted", "expires_in": 3600}).encode())
+    match request.target:
+        case "/v2/lm/deployments":
+            deployment: Final = {
+                "scenarioId": "orchestration",
+                "configurationId": "orchestration-config",
+                "deploymentUrl": f"http://{request.headers['host']}/sap-deployment",
+                "createdAt": "2026-01-01T00:00:00Z",
+            }
+            return Reply(body=json.dumps({"resources": [deployment]}).encode())
+        case "/v2/lm/configurations/orchestration-config":
+            return Reply(body=json.dumps({"executableId": "orchestration"}).encode())
+        case _:
+            completion: Final = _JSON_OBJECT.validate_json(_chat_completion("chatcmpl-sap-pdf", "claude").body)
+            return Reply(body=json.dumps({"final_result": completion}).encode())
+
+
+def _snowflake(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(model="snowflake/claude-sonnet-4-5", api_base=wire.url, api_key=_API_KEY)
+
+
+def _sap(scenario: Scenario, wire: Wire) -> str:
+    service_key: Final = {
+        "clientid": "scripted",
+        "clientsecret": _API_KEY,
+        "url": wire.url,
+        "serviceurls": {"AI_API_URL": wire.url},
+    }
+    return scenario.model(model="sap/anthropic--claude-4.5-sonnet", api_base=wire.url, api_key=json.dumps(service_key))
+
+
+def _snowflake_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    return tuple(part.get("source") for part in _user_parts(body, "messages") if part.get("type") == "document")
+
+
+def _sap_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    config: Final = _JSON_OBJECT.validate_python(body["config"])
+    templating: Final = _JSON_OBJECT.validate_python(
+        _JSON_OBJECT.validate_python(config["modules"])["prompt_templating"]
+    )
+    prompt: Final = _JSON_OBJECT.validate_python(templating["prompt"])
+    turns: Final = _PARTS.validate_python(prompt["template"])
+    assert len(turns) == 1, turns
+    parts: Final = _PARTS.validate_python(turns[0]["content"])
+    return tuple(
+        _JSON_OBJECT.validate_python(part["image_url"]).get("url") for part in parts if part.get("type") == "image_url"
+    )
+
+
+def _gemini(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(model="gemini/gemini-2.5-flash", api_base=wire.url, api_key=_API_KEY)
+
+
+def _bedrock(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(
+        model="bedrock/converse/us.amazon.nova-2-lite-v1:0",
+        api_key=None,
+        aws_access_key_id="AKIDSYNTHETIC",
+        aws_secret_access_key="synthetic-secret",
+        aws_region_name="us-east-1",
+        aws_bedrock_runtime_endpoint=wire.url,
+    )
+
+
+def _gemini_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    return tuple(part.get("inline_data") for part in _user_parts(body, "contents") if "inline_data" in part)
+
+
+def _bedrock_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    documents: Final = tuple(
+        _JSON_OBJECT.validate_python(part["document"]) for part in _user_parts(body, "messages") if "document" in part
+    )
+    return tuple({"format": item.get("format"), "source": item.get("source")} for item in documents)
+
+
+@pytest.mark.parametrize(
+    ("reader", "expected"),
+    [
+        pytest.param(
+            _Reader(_gemini, _gemini_reply, _gemini_pdf_parts),
+            lambda data: [{"mime_type": "application/pdf", "data": data}],
+            id="gemini",
+        ),
+        pytest.param(
+            _Reader(_bedrock, _bedrock_reply, _bedrock_pdf_parts),
+            lambda data: [{"format": "pdf", "source": {"bytes": data}}],
+            id="bedrock-converse",
+        ),
+        pytest.param(
+            _Reader(_snowflake, _anthropic_reply, _snowflake_pdf_parts),
+            lambda data: [{"type": "base64", "media_type": "application/pdf", "data": data}],
+            id="snowflake-claude",
+        ),
+        pytest.param(
+            _Reader(_sap, _sap_reply, _sap_pdf_parts),
+            lambda data: [f"data:application/pdf;base64,{data}"],
+            id="sap-orchestration",
+        ),
+    ],
+)
+def test_messages_pdf_still_reaches_providers_that_read_pdfs_in_their_own_document_shape(
+    gateway: Gateway, reader: _Reader, expected: Callable[[str], Sequence[JsonValue]]
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    data: Final = _pdf(f"PELICAN-42 {marker}")
+    with wire_server(reader.reply) as wire, gateway.scenario() as scenario:
+        model: Final = reader.deploy(scenario, wire)
+        response: Final = gateway.request(
+            "POST",
+            "/v1/messages",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            document({"type": "base64", "media_type": "application/pdf", "data": data}, title=_TITLE),
+                            {"type": "text", "text": f"What is the codeword? {marker}"},
+                        ],
+                    }
+                ],
+            },
+            headers={"anthropic-version": "2023-06-01"},
+        )
+        assert response.status_code == 200, response.text
+        assert _JSON_OBJECT.validate_json(response.content)["content"] == [{"type": "text", "text": _ANSWER}]
+        assert list(reader.pdf_parts(_only_body(wire))) == list(expected(data))
+
+
+def _prompt_security(job: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        match (request.method, request.target.partition("?")[0]):
+            case ("POST", "/api/sanitizeFile"):
+                return Reply(body=json.dumps({"jobId": job}).encode())
+            case ("GET", "/api/sanitizeFile"):
+                return Reply(
+                    body=json.dumps(
+                        {
+                            "status": "done",
+                            "content": "",
+                            "metadata": {"action": "block", "violations": ["Malicious File"]},
+                        }
+                    ).encode()
+                )
+            case _:
+                return Reply(body=json.dumps({"result": {"prompt": {"action": "allow"}}}).encode())
+
+    return respond
+
+
+def _delete_guardrail(gateway: Gateway, identity: str) -> None:
+    response: Final = gateway.request("DELETE", f"/guardrails/{identity}")
+    assert response.status_code == 200, response.text
+
+
+def test_prompt_security_still_sanitizes_a_messages_pdf_and_blocks_it_before_the_provider(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    pdf: Final = pdf_bytes((Page(text=f"PELICAN-42 {marker}"), LETTER))
+    with (
+        wire_server(lambda _: _chat_completion(f"chatcmpl-{marker}", "gpt-pdf")) as provider,
+        wire_server(_prompt_security(f"job-{marker}")) as sink,
+        gateway.scenario() as scenario,
+    ):
+        model: Final = scenario.model(
+            model=f"azure/gpt-pdf-{marker[:8]}", api_base=provider.url, api_key=_API_KEY, api_version="2024-10-21"
+        )
+        name: Final = f"pdf-prompt-security-{marker}"
+        created: Final = gateway.post(
+            "/guardrails",
+            {
+                "guardrail": {
+                    "guardrail_name": name,
+                    "litellm_params": {
+                        "guardrail": "prompt_security",
+                        "mode": "pre_call",
+                        "api_key": "synthetic-prompt-security-key",
+                        "api_base": sink.url,
+                    },
+                }
+            },
+        )
+        scenario.cleanups.callback(_delete_guardrail, gateway, string_value(created["guardrail_id"]))
+        response: Final = gateway.request(
+            "POST",
+            "/v1/messages",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "guardrails": [name],
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            document({"type": "base64", "media_type": "application/pdf", "data": encoded(pdf)}),
+                            {"type": "text", "text": f"Summarize this PDF {marker}"},
+                        ],
+                    }
+                ],
+            },
+            headers={"anthropic-version": "2023-06-01"},
+        )
+        assert response.status_code == 400, response.text
+        assert "Malicious File" in response.text
+        assert provider.drain() == ()
+        uploads: Final = tuple(
+            request for request in sink.drain() if (request.method, request.target) == ("POST", "/api/sanitizeFile")
+        )
+        assert len(uploads) == 1, uploads
+        assert pdf in uploads[0].body
+
+
+def _gigachat_reply(file_id: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if request.target.endswith("/files"):
+            return Reply(body=json.dumps({"id": file_id, "object": "file", "purpose": "general"}).encode())
+        return _chat_completion(f"chatcmpl-{file_id}", "GigaChat-Pro")
+
+    return respond
+
+
+def test_anthropic_sdk_pdf_document_reaches_gigachat_as_an_uploaded_attachment(
+    gateway: Gateway, tmp_path: Path
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    file_id: Final = f"file-{marker}"
+    pdf: Final = pdf_bytes((Page(text=f"PELICAN-42 {marker}"), LETTER))
+    with (
+        wire_server(_gigachat_reply(file_id)) as wire,
+        owned_proxy_process(gateway, tmp_path, {"GIGACHAT_ACCESS_TOKEN": _API_KEY}) as owned,
+        owned.gateway.scenario() as scenario,
+    ):
+        model: Final = scenario.model(model="gigachat/GigaChat-Pro", api_base=f"{wire.url}/v1", api_key=_API_KEY)
+        client: Final = anthropic.Anthropic(
+            base_url=str(owned.gateway.client.base_url), api_key=owned.gateway.key, max_retries=0
+        )
+        message: Final = client.messages.create(
+            model=model,
+            max_tokens=64,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": encoded(pdf)},
+                        },
+                        {"type": "text", "text": f"What is the codeword? {marker}"},
+                    ],
+                }
+            ],
+        )
+        assert [(block.type, getattr(block, "text", None)) for block in message.content] == [("text", _ANSWER)]
+    upload, chat = (request for request in wire.drain() if request.method == "POST")
+    assert upload.target == "/v1/files", upload.target
+    assert upload.headers["authorization"] == f"Bearer {_API_KEY}", sorted(upload.headers)
+    assert pdf in upload.body
+    assert b"application/pdf" in upload.body
+    assert chat.target == "/v1/chat/completions", chat.target
+    turns: Final = _PARTS.validate_python(_JSON_OBJECT.validate_json(chat.body)["messages"])
+    assert turns[-1]["attachments"] == [file_id], chat.body
diff --git a/tests/integration/providers/test_chat_provider_annotations_wire.py b/tests/integration/providers/test_chat_provider_annotations_wire.py
new file mode 100644
index 00000000000..0b18d0837eb
--- /dev/null
+++ b/tests/integration/providers/test_chat_provider_annotations_wire.py
@@ -0,0 +1,190 @@
+import json
+import uuid
+from collections.abc import Callable, Iterator
+from itertools import chain
+from typing import Final
+
+import httpx
+from integration._support.client import Gateway
+from integration._support.wire import Reply, Request, Wire, wire_server
+from integration.caching.response_cache_case import await_entry, redis_store
+from pydantic import JsonValue, TypeAdapter
+from redis import Redis
+
+_BACKEND: Final = "z-ai/glm-5.2"
+_API_KEY: Final = "synthetic-openrouter-key"
+_ANSWER: Final = "The report covers revenue."
+_URL_CITATION: Final[dict[str, JsonValue]] = {
+    "type": "url_citation",
+    "url_citation": {
+        "url": "https://example.com/report",
+        "title": "Report",
+        "start_index": 0,
+        "end_index": 10,
+        "content": "cited passage",
+    },
+}
+_FILE_ANNOTATION: Final[dict[str, JsonValue]] = {
+    "type": "file",
+    "file": {"hash": "sha-report", "name": "report.pdf", "content": [{"type": "text", "text": "parsed page one"}]},
+}
+_USAGE: Final[dict[str, JsonValue]] = {"prompt_tokens": 40, "completion_tokens": 6, "total_tokens": 46}
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_CHOICES: Final = TypeAdapter(list[dict[str, JsonValue]])
+
+
+def _completion(identity: str) -> bytes:
+    return json.dumps(
+        {
+            "id": identity,
+            "object": "chat.completion",
+            "created": 1,
+            "model": _BACKEND,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": _ANSWER,
+                        "annotations": [_URL_CITATION, _FILE_ANNOTATION],
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": _USAGE,
+        }
+    ).encode()
+
+
+def _frame(frame: dict[str, JsonValue]) -> bytes:
+    return b"data: " + json.dumps(frame).encode() + b"\n\n"
+
+
+def _stream(identity: str) -> Reply:
+    chunk: Final[dict[str, JsonValue]] = {
+        "id": identity,
+        "object": "chat.completion.chunk",
+        "created": 1,
+        "model": _BACKEND,
+    }
+    return Reply(
+        content_type="text/event-stream",
+        chunks=(
+            _frame({**chunk, "choices": [{"index": 0, "delta": {"role": "assistant", "content": _ANSWER}}]}),
+            _frame({**chunk, "choices": [{"index": 0, "delta": {"annotations": [_URL_CITATION, _FILE_ANNOTATION]}}]}),
+            _frame({**chunk, "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}], "usage": _USAGE}),
+            b"data: [DONE]\n\n",
+        ),
+    )
+
+
+def _responder(identity: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if _JSON_OBJECT.validate_json(request.body).get("stream") is True:
+            return _stream(identity)
+        return Reply(body=_completion(identity))
+
+    return respond
+
+
+def _chat_body(model: str, marker: str, stream: bool) -> dict[str, JsonValue]:
+    return {
+        "model": model,
+        "messages": [{"role": "user", "content": f"Summarize the report {marker}"}],
+        "stream": stream,
+    }
+
+
+def _posted(wire: Wire) -> int:
+    return sum(1 for request in wire.drain() if request.method == "POST")
+
+
+def _message(response: httpx.Response) -> dict[str, JsonValue]:
+    assert response.status_code == 200, response.text
+    choices: Final = _CHOICES.validate_python(_JSON_OBJECT.validate_json(response.content)["choices"])
+    return _JSON_OBJECT.validate_python(choices[0]["message"])
+
+
+def _assert_split(message: dict[str, JsonValue]) -> None:
+    assert message["content"] == _ANSWER, message
+    assert message["annotations"] == [_URL_CITATION], message
+    assert _JSON_OBJECT.validate_python(message["provider_specific_fields"])["annotations"] == [_FILE_ANNOTATION]
+
+
+def test_file_annotation_reaches_the_caller_in_provider_specific_fields_and_survives_a_cache_hit(
+    gateway: Gateway,
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    identity: Final = f"gen-{marker}"
+    with wire_server(_responder(identity)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(model=f"openrouter/{_BACKEND}", api_base=wire.url, api_key=_API_KEY)
+        first: Final = gateway.request("POST", "/v1/chat/completions", _chat_body(model, marker, stream=False))
+        _assert_split(_message(first))
+        assert _posted(wire) == 1
+        await_entry(redis_store(), identity)
+        cached: Final = gateway.request("POST", "/v1/chat/completions", _chat_body(model, marker, stream=False))
+        assert cached.headers.get("x-litellm-cache-key"), dict(cached.headers)
+        _assert_split(_message(cached))
+        assert _posted(wire) == 0
+
+
+def _deltas(response: httpx.Response) -> Iterator[dict[str, JsonValue]]:
+    for line in response.iter_lines():
+        if not line.startswith("data: ") or line == "data: [DONE]":
+            continue
+        choices = _CHOICES.validate_python(_JSON_OBJECT.validate_json(line.removeprefix("data: "))["choices"])
+        yield from (_JSON_OBJECT.validate_python(choice["delta"]) for choice in choices)
+
+
+def _streamed(gateway: Gateway, body: dict[str, JsonValue]) -> tuple[dict[str, JsonValue], ...]:
+    with gateway.client.stream(
+        "POST", "/v1/chat/completions", json=body, headers={"Authorization": f"Bearer {gateway.key}"}
+    ) as response:
+        assert response.status_code == 200, response.read()
+        return tuple(_deltas(response))
+
+
+def _listed(value: JsonValue) -> list[JsonValue]:
+    return value if isinstance(value, list) else []
+
+
+def _provider_fields(delta: dict[str, JsonValue]) -> dict[str, JsonValue]:
+    return _JSON_OBJECT.validate_python(delta.get("provider_specific_fields") or {})
+
+
+def _annotations(deltas: tuple[dict[str, JsonValue], ...]) -> list[JsonValue]:
+    return list(chain.from_iterable(_listed(delta.get("annotations")) for delta in deltas))
+
+
+def _provider_annotations(deltas: tuple[dict[str, JsonValue], ...]) -> list[JsonValue]:
+    return list(chain.from_iterable(_listed(_provider_fields(delta).get("annotations")) for delta in deltas))
+
+
+def _text(deltas: tuple[dict[str, JsonValue], ...]) -> str:
+    return "".join(str(delta.get("content") or "") for delta in deltas)
+
+
+def _cached_message(store: Redis, key: str) -> dict[str, JsonValue]:
+    raw: Final = store.get(key)
+    assert isinstance(raw, bytes), key
+    response: Final = _JSON_OBJECT.validate_json(raw)["response"]
+    stored: Final = _JSON_OBJECT.validate_json(response) if isinstance(response, str) else response
+    choices: Final = _CHOICES.validate_python(_JSON_OBJECT.validate_python(stored)["choices"])
+    return _JSON_OBJECT.validate_python(choices[0]["message"])
+
+
+def test_streamed_annotations_reach_the_caller_as_sent_and_the_cached_stream_splits_them(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    identity: Final = f"gen-{marker}"
+    with wire_server(_responder(identity)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(model=f"openrouter/{_BACKEND}", api_base=wire.url, api_key=_API_KEY)
+        live: Final = _streamed(gateway, _chat_body(model, marker, stream=True))
+        assert _text(live) == _ANSWER, live
+        assert _annotations(live) == [_URL_CITATION, _FILE_ANNOTATION], live
+        assert _posted(wire) == 1
+        store: Final = redis_store()
+        _assert_split(_cached_message(store, await_entry(store, identity)))
+        replayed: Final = _streamed(gateway, _chat_body(model, marker, stream=True))
+        assert _text(replayed) == _ANSWER, replayed
+        assert _provider_annotations(replayed) == [_FILE_ANNOTATION], replayed
+        assert _posted(wire) == 0
diff --git a/tests/integration/providers/test_pdf_input_support_wire.py b/tests/integration/providers/test_pdf_input_support_wire.py
new file mode 100644
index 00000000000..b35815438f7
--- /dev/null
+++ b/tests/integration/providers/test_pdf_input_support_wire.py
@@ -0,0 +1,390 @@
+import json
+import uuid
+from collections.abc import Callable, Mapping, Sequence
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Final
+
+import httpx
+import pytest
+from integration._support.client import Gateway, Scenario
+from integration._support.openai_wire import chat_reply
+from integration._support.pdf_document import (
+    LETTER,
+    chat_body,
+    chat_file,
+    messages_body,
+    pdf_data_url,
+    pdf_document,
+    responses_body,
+    responses_input_file,
+)
+from integration._support.wire import Reply, Request, Wire, wire_server
+from pydantic import JsonValue, TypeAdapter
+
+_API_KEY: Final = "synthetic-pdf-gate-key"
+_ANSWER: Final = "The report covers revenue."
+_FILENAME: Final = "report.pdf"
+_PDF: Final = pdf_data_url((LETTER,))
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_MESSAGES: Final = TypeAdapter(list[JsonValue])
+_COST_MAP_PATH: Final = Path(__file__).parents[3] / "litellm" / "model_prices_and_context_window_backup.json"
+
+
+@dataclass(frozen=True, slots=True)
+class _Entrypoint:
+    path: str
+    body: Callable[[str, str], Mapping[str, JsonValue]]
+    stream: bool = False
+    provider: str = "hosted_vllm"
+
+
+_ENTRYPOINTS: Final = (
+    pytest.param(
+        _Entrypoint("/v1/chat/completions", lambda model, text: chat_body(model, [chat_file(_PDF, _FILENAME)], text)),
+        id="chat-completions",
+    ),
+    pytest.param(
+        _Entrypoint(
+            "/v1/chat/completions",
+            lambda model, text: chat_body(model, [chat_file(_PDF, _FILENAME)], text, stream=True),
+            stream=True,
+        ),
+        id="chat-completions-stream",
+    ),
+    pytest.param(
+        _Entrypoint("/v1/messages", lambda model, text: messages_body(model, [pdf_document((LETTER,))], text)),
+        id="messages",
+    ),
+    pytest.param(
+        _Entrypoint(
+            "/v1/responses",
+            lambda model, text: responses_body(model, [responses_input_file(_PDF, _FILENAME)], text),
+            provider="lm_studio",
+        ),
+        id="responses-bridge",
+    ),
+)
+
+
+def _azure_ai_chat_model_pdf_input(entry: JsonValue) -> bool | None:
+    match entry:
+        case {"litellm_provider": "azure_ai", "mode": "chat", "supports_pdf_input": bool() as supports}:
+            return supports
+        case _:
+            return None
+
+
+def _azure_ai_chat_model(reads_pdfs: bool) -> str:
+    entries: Final = _JSON_OBJECT.validate_json(_COST_MAP_PATH.read_bytes())
+    matching: Final = sorted(
+        name for name, entry in entries.items() if _azure_ai_chat_model_pdf_input(entry) is reads_pdfs
+    )
+    assert matching, f"the cost map marks no azure_ai chat model with supports_pdf_input {reads_pdfs}"
+    return matching[0]
+
+
+def _ollama_reply(backend: str) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "model": backend,
+                "created_at": "2026-01-01T00:00:00Z",
+                "message": {"role": "assistant", "content": _ANSWER},
+                "done": True,
+                "done_reason": "stop",
+                "prompt_eval_count": 5,
+                "eval_count": 3,
+            }
+        ).encode()
+    )
+
+
+def _responder(backend: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if request.target.endswith("/api/chat"):
+            return _ollama_reply(backend)
+        body: Final = _JSON_OBJECT.validate_json(request.body)
+        return chat_reply(f"chatcmpl-{uuid.uuid4().hex}", backend, _ANSWER, stream=body.get("stream") is True)
+
+    return respond
+
+
+def _post(gateway: Gateway, path: str, body: Mapping[str, JsonValue]) -> httpx.Response:
+    return gateway.request("POST", path, body, headers={"anthropic-version": "2023-06-01"})
+
+
+def _post_streaming(gateway: Gateway, path: str, body: Mapping[str, JsonValue]) -> httpx.Response:
+    with gateway.client.stream(
+        "POST", path, json=dict(body), headers={"Authorization": f"Bearer {gateway.key}"}
+    ) as response:
+        response.read()
+        return response
+
+
+def _send(gateway: Gateway, entrypoint: _Entrypoint, model: str, text: str) -> httpx.Response:
+    body: Final = entrypoint.body(model, text)
+    if entrypoint.stream:
+        return _post_streaming(gateway, entrypoint.path, body)
+    return _post(gateway, entrypoint.path, body)
+
+
+def _error_message(response: httpx.Response) -> str:
+    match _JSON_OBJECT.validate_json(response.content):
+        case {"error": {"message": str() as message}}:
+            return message
+        case _:
+            raise AssertionError(response.text)
+
+
+def _assert_refused_before_the_provider(response: httpx.Response, wire: Wire, backend: str) -> None:
+    assert response.status_code == 400, response.text
+    message: Final = _error_message(response)
+    assert "PDF" in message and backend in message, message
+    assert wire.drain() == ()
+
+
+def _bodies(wire: Wire) -> tuple[dict[str, JsonValue], ...]:
+    return tuple(_JSON_OBJECT.validate_json(request.body) for request in wire.drain() if request.method == "POST")
+
+
+def _only_body(wire: Wire) -> dict[str, JsonValue]:
+    bodies: Final = _bodies(wire)
+    assert len(bodies) == 1, bodies
+    return bodies[0]
+
+
+def _assert_reaches_the_provider(gateway: Gateway, wire: Wire, model: str) -> None:
+    response: Final = _post(gateway, "/v1/chat/completions", chat_body(model, [], f"text only {uuid.uuid4().hex}"))
+    assert response.status_code == 200, response.text
+    assert len(_bodies(wire)) == 1
+
+
+def _openai_compatible(
+    scenario: Scenario,
+    wire: Wire,
+    backend: str,
+    model_info: Mapping[str, JsonValue],
+    provider: str = "hosted_vllm",
+    **parameters: JsonValue,
+) -> str:
+    return scenario.model(
+        model=f"{provider}/{backend}",
+        api_base=f"{wire.url}/v1",
+        api_key=_API_KEY,
+        model_info=model_info,
+        **parameters,
+    )
+
+
+def _backend() -> str:
+    return f"pdf-gate-{uuid.uuid4().hex[:12]}"
+
+
+@pytest.mark.parametrize("entrypoint", _ENTRYPOINTS)
+def test_pdf_to_a_deployment_marked_unable_to_read_it_is_refused_before_the_provider(
+    gateway: Gateway, entrypoint: _Entrypoint
+) -> None:
+    backend: Final = _backend()
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, entrypoint.provider)
+        response: Final = _send(gateway, entrypoint, model, f"Summarize the report {uuid.uuid4().hex}")
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _base_model_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = f"pdf-gate-{uuid.uuid4().hex[:12]}"
+    model: Final = scenario.model(
+        model=f"azure_ai/{backend}",
+        api_base=wire.url,
+        api_key=_API_KEY,
+        model_info={"base_model": _azure_ai_chat_model(reads_pdfs=False)},
+    )
+    return model, backend
+
+
+def _litellm_params_base_model_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = _backend()
+    model: Final = scenario.model(
+        model=f"azure_ai/{backend}",
+        api_base=wire.url,
+        api_key=_API_KEY,
+        base_model=_azure_ai_chat_model(reads_pdfs=False),
+    )
+    return model, backend
+
+
+def _cost_map_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    marked: Final = _azure_ai_chat_model(reads_pdfs=False)
+    return scenario.model(model=marked, api_base=wire.url, api_key=_API_KEY), marked.removeprefix("azure_ai/")
+
+
+def _ollama_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = _backend()
+    return scenario.model(model=f"ollama_chat/{backend}", api_base=wire.url, api_key=None), backend
+
+
+@pytest.mark.parametrize(
+    "deploy",
+    [
+        pytest.param(_base_model_deployment, id="model-info-base-model"),
+        pytest.param(_litellm_params_base_model_deployment, id="litellm-params-base-model"),
+        pytest.param(_cost_map_deployment, id="cost-map"),
+        pytest.param(_ollama_deployment, id="ollama"),
+    ],
+)
+def test_pdf_to_a_model_its_declaration_marks_unable_is_refused_before_the_provider(
+    gateway: Gateway, deploy: Callable[[Scenario, Wire], tuple[str, str]]
+) -> None:
+    with wire_server(_responder("scripted")) as wire, gateway.scenario() as scenario:
+        model, backend = deploy(scenario, wire)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            chat_body(model, [chat_file(_PDF, _FILENAME)], f"Summarize the report {uuid.uuid4().hex}"),
+        )
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _pdf_turns(question: str) -> list[JsonValue]:
+    return [
+        {"role": "user", "content": [chat_file(_PDF, _FILENAME), {"type": "text", "text": question}]},
+        {"role": "assistant", "content": "It is a quarterly report."},
+        {"role": "user", "content": [chat_file(_PDF, "appendix.pdf")]},
+        {"role": "user", "content": "Who wrote it?"},
+    ]
+
+
+def _answer(response: httpx.Response) -> JsonValue:
+    match _JSON_OBJECT.validate_json(response.content):
+        case {"choices": [{"message": {"content": content}}]}:
+            return content
+        case _:
+            raise AssertionError(response.text)
+
+
+def _turns_without_pdfs(question: str) -> list[JsonValue]:
+    return [
+        {"role": "user", "content": [{"type": "text", "text": question}]},
+        {"role": "assistant", "content": "It is a quarterly report."},
+        {"role": "user", "content": "Who wrote it?"},
+    ]
+
+
+@pytest.mark.parametrize(
+    ("deployment_params", "request_params"),
+    [
+        pytest.param({"drop_params": True}, {}, id="deployment-drop-params"),
+        pytest.param({}, {"drop_params": True}, id="request-drop-params"),
+    ],
+)
+def test_drop_params_sends_the_conversation_without_the_pdfs_a_model_cannot_read(
+    gateway: Gateway, deployment_params: Mapping[str, JsonValue], request_params: Mapping[str, JsonValue]
+) -> None:
+    backend: Final = _backend()
+    question: Final = f"Summarize the report {uuid.uuid4().hex}"
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, **deployment_params)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            {"model": model, "max_tokens": 64, "messages": _pdf_turns(question), **request_params},
+        )
+        assert response.status_code == 200, response.text
+        assert _answer(response) == _ANSWER
+        assert _only_body(wire)["messages"] == _turns_without_pdfs(question)
+
+
+def test_drop_params_refuses_a_request_left_with_no_user_turn_once_the_pdf_is_dropped(gateway: Gateway) -> None:
+    backend: Final = _backend()
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, drop_params=True)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "messages": [
+                    {"role": "system", "content": f"Answer from the attachment {uuid.uuid4().hex}"},
+                    {"role": "user", "content": [chat_file(_PDF, _FILENAME)]},
+                ],
+            },
+        )
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _file_and_question(question: str) -> list[JsonValue]:
+    return [{"role": "user", "content": [chat_file(_PDF, _FILENAME), {"type": "text", "text": question}]}]
+
+
+@pytest.mark.parametrize(
+    ("model_info", "deployment_params", "keeps_pdf"),
+    [
+        pytest.param({}, {}, True, id="undeclared"),
+        pytest.param({}, {"drop_params": True}, False, id="undeclared-drop-params"),
+        pytest.param({"supports_pdf_input": True}, {"drop_params": True}, True, id="declared-drop-params"),
+        pytest.param(
+            {},
+            {"drop_params": True, "base_model": _azure_ai_chat_model(reads_pdfs=True)},
+            True,
+            id="base-model-reads-drop-params",
+        ),
+    ],
+)
+def test_azure_ai_model_outside_the_cost_map_gets_the_pdf_unless_drop_params_finds_no_declaration(
+    gateway: Gateway, model_info: Mapping[str, JsonValue], deployment_params: Mapping[str, JsonValue], keeps_pdf: bool
+) -> None:
+    backend: Final = _backend()
+    question: Final = f"Summarize the report {uuid.uuid4().hex}"
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(
+            model=f"azure_ai/{backend}",
+            api_base=wire.url,
+            api_key=_API_KEY,
+            model_info=model_info,
+            **deployment_params,
+        )
+        response: Final = _post(
+            gateway, "/v1/chat/completions", chat_body(model, [chat_file(_PDF, _FILENAME)], question)
+        )
+        assert response.status_code == 200, response.text
+        expected: Final[list[JsonValue]] = (
+            _file_and_question(question) if keeps_pdf else [{"role": "user", "content": question}]
+        )
+        assert _only_body(wire)["messages"] == expected
+
+
+def _sibling_pdf_request(gateway: Gateway, model: str) -> httpx.Response:
+    return _post(
+        gateway,
+        "/v1/chat/completions",
+        chat_body(model, [chat_file(_PDF, _FILENAME)], f"Summarize the report {uuid.uuid4().hex}"),
+    )
+
+
+def _forwarded_file_parts(bodies: Sequence[dict[str, JsonValue]]) -> tuple[JsonValue, ...]:
+    return tuple(_MESSAGES.validate_python(body["messages"])[0] for body in bodies)
+
+
+def test_pdf_opt_out_on_one_deployment_leaves_its_sibling_on_the_same_model_reading_pdfs(gateway: Gateway) -> None:
+    backend: Final = _backend()
+    burst: Final = 6
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        sibling: Final = _openai_compatible(scenario, wire, backend, {})
+        marked: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False})
+        with ThreadPoolExecutor(max_workers=burst) as pool:
+            responses: Final = tuple(pool.map(lambda _: _sibling_pdf_request(gateway, sibling), range(burst)))
+        assert [response.status_code for response in responses] == [200] * burst, [r.text for r in responses]
+        forwarded: Final = _forwarded_file_parts(_bodies(wire))
+        assert len(forwarded) == burst, forwarded
+        assert all(
+            _MESSAGES.validate_python(_JSON_OBJECT.validate_python(message)["content"])[0] == chat_file(_PDF, _FILENAME)
+            for message in forwarded
+        ), forwarded
+        refused: Final = _sibling_pdf_request(gateway, marked)
+        _assert_refused_before_the_provider(refused, wire, backend)
diff --git a/tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py b/tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py
index 0c601a6b2c8..cea12230fbd 100644
--- a/tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py
+++ b/tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py
@@ -2968,3 +2968,54 @@ def test_convert_to_model_response_object_function_output():
     assert result.usage.completion_tokens_details == CompletionTokensDetailsWrapper(reasoning_tokens=0)
 
     assert result._hidden_params is not None
+
+
+_URL_CITATION: Final = {
+    "type": "url_citation",
+    "url_citation": {
+        "url": "https://example.com",
+        "title": "Example",
+        "content": "cited snippet",
+        "start_index": 0,
+        "end_index": 4,
+    },
+}
+_FILE_ANNOTATION: Final = {
+    "type": "file",
+    "file": {"hash": "abc123", "name": "report.pdf", "content": [{"type": "text", "text": "parsed page"}]},
+}
+
+
+def _message_with_annotations(annotations: list[dict[str, object]]) -> Message:
+    response: Final = {
+        "id": "gen-1",
+        "object": "chat.completion",
+        "created": 1712697600,
+        "model": "z-ai/glm-5.3",
+        "choices": [
+            {
+                "index": 0,
+                "finish_reason": "stop",
+                "message": {"role": "assistant", "content": "done", "annotations": annotations},
+            }
+        ],
+        "usage": {"completion_tokens": 1, "prompt_tokens": 1, "total_tokens": 2},
+    }
+    return convert_to_model_response_object(
+        response_object=response, model_response_object=ModelResponse(), response_type="completion"
+    ).choices[0].message
+
+
+def test_annotations_typed_other_than_url_citation_move_to_provider_specific_fields() -> None:
+    untyped_annotation: Final = {"start_index": 0, "end_index": 4}
+    message: Final = _message_with_annotations([_URL_CITATION, untyped_annotation, _FILE_ANNOTATION])
+
+    assert message.annotations == [_URL_CITATION, untyped_annotation]
+    assert message.provider_specific_fields["annotations"] == [_FILE_ANNOTATION]
+
+
+def test_url_citation_annotations_keep_provider_keys() -> None:
+    message: Final = _message_with_annotations([_URL_CITATION])
+
+    assert message.annotations == [_URL_CITATION]
+    assert "annotations" not in (message.provider_specific_fields or {})
diff --git a/tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py b/tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py
index 51ec71aaea0..c30644e7fdc 100644
--- a/tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py
+++ b/tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py
@@ -5286,6 +5286,40 @@ def test_eager_input_streaming_tool_reaches_bedrock_converse_as_beta():
     assert data["toolConfig"]["tools"][0]["toolSpec"]["inputSchema"]["json"] == EAGER_INPUT_SCHEMA
 
 
+@pytest.mark.parametrize(
+    ("title", "expected_filename"),
+    [(None, "document.pdf"), ("Quarterly report", "Quarterly report")],
+)
+def test_user_pdf_document_reaches_openai_compatible_providers_as_file_part(
+    title: str | None, expected_filename: str
+) -> None:
+    document: Final = _base64_pdf_block() | ({"title": title} if title else {})
+    request_messages: Final = _run_chat_completions_pipeline(
+        [
+            AnthropicMessagesUserMessageParam(
+                role="user",
+                content=[document, {"type": "text", "text": "What is the codeword?"}],
+            )
+        ]
+    )
+
+    assert request_messages == [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "file",
+                    "file": {
+                        "filename": expected_filename,
+                        "file_data": f"data:application/pdf;base64,{TOOL_RESULT_PDF_B64}",
+                    },
+                },
+                {"type": "text", "text": "What is the codeword?"},
+            ],
+        }
+    ]
+
+
 _CHAT_BRIDGE_MODEL: Final = "azure/gpt-5.6"
 _CHAT_BRIDGE_API_BASE: Final = "https://tool-result-text.openai.azure.com"
 _CHAT_BRIDGE_URL: Final = r".*tool-result-text\.openai\.azure\.com/openai/deployments/gpt-5\.6/chat/completions.*"
diff --git a/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py b/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py
index 42dc222e420..6269fd367fe 100644
--- a/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py
+++ b/tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py
@@ -1101,6 +1101,58 @@ def test_map_azure_model_group(model_group_header, expected_model):
     assert config._map_azure_model_group(model_group_header) == expected_model
 
 
+def test_azure_ai_sends_file_parts_instead_of_flattening_them_to_text() -> None:
+    file_part: Final = {
+        "type": "file",
+        "file": {"filename": "document.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"},
+    }
+    content: Final = [file_part, {"type": "text", "text": "What is the codeword?"}]
+
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="Kimi-K2.6",
+        messages=[{"role": "user", "content": content}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": content}]
+
+
+def test_azure_ai_passes_dict_message_content_through_for_the_provider_to_judge() -> None:
+    content: Final = {"type": "text", "text": "Say hi"}
+
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="gpt-5.6-sol",
+        messages=[{"role": "user", "content": content}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": content}]
+
+
+@pytest.mark.parametrize(
+    "file",
+    [
+        {"filename": "notes.txt", "file_data": "data:text/plain;base64,aGVsbG8="},
+        {"file_id": "file-abc"},
+    ],
+    ids=["text_data_url", "file_id_only"],
+)
+def test_azure_ai_flattens_a_non_pdf_file_part_to_its_text_as_before(file: dict[str, str]) -> None:
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="Llama-3.3-70B-Instruct",
+        messages=[{"role": "user", "content": [{"type": "text", "text": "summarize"}, {"type": "file", "file": file}]}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": "summarize"}]
+
+
 def test_translate_developer_role_hoists_a_later_developer_message_for_a_model_that_rejects_it(
     declare_mid_conversation_system: Callable[[str, bool], None],
 ) -> None:
diff --git a/tests/unit/llms/gigachat/chat/test_gigachat_chat_transformation.py b/tests/unit/llms/gigachat/chat/test_gigachat_chat_transformation.py
index f2224e84ff3..115d81bfb90 100644
--- a/tests/unit/llms/gigachat/chat/test_gigachat_chat_transformation.py
+++ b/tests/unit/llms/gigachat/chat/test_gigachat_chat_transformation.py
@@ -8,6 +8,7 @@ get_model_response_iterator, and get_error_class.
 """
 
 import json
+from typing import Final
 from unittest.mock import MagicMock, patch
 
 import httpx
@@ -512,6 +513,20 @@ class TestTransformMessages:
         assert result[0]["content"] == "look"
         assert "file-456" in result[0]["attachments"]
 
+    def test_list_content_with_pdf_file_part_uploads_it_as_an_attachment(self) -> None:
+        pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+        content: Final = [
+            {"type": "text", "text": "summarize"},
+            {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+        ]
+        with patch.object(self.config, "_upload_image", return_value="file-789") as upload:
+            result: Final = self.config._transform_messages([
+                {"role": "user", "content": content}
+            ])
+        upload.assert_called_once_with(pdf_data_url)
+        assert result[0]["content"] == "summarize"
+        assert result[0]["attachments"] == ["file-789"]
+
 
 class TestTransformRequest:
     def setup_method(self):
diff --git a/tests/unit/llms/oci/chat/test_oci_chat_transformation.py b/tests/unit/llms/oci/chat/test_oci_chat_transformation.py
index 8c96bd91049..0535279d177 100644
--- a/tests/unit/llms/oci/chat/test_oci_chat_transformation.py
+++ b/tests/unit/llms/oci/chat/test_oci_chat_transformation.py
@@ -1,4 +1,5 @@
 import datetime
+from typing import Final
 import httpx
 import pytest
 import json
@@ -2164,3 +2165,32 @@ class TestOpenAIPrefixMaxTokensKey:
         chat_request = _oci_chat_request("openai.gpt-5", {"max_tokens": 64})
         assert chat_request["maxCompletionTokens"] == 64
         assert "maxTokens" not in chat_request
+
+
+def test_transform_request_sends_a_pdf_file_part_as_an_image_data_url() -> None:
+    pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+    transformed_request: Final = OCIChatConfig().transform_request(
+        model="google.gemini-2.5-flash",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Summarize this."},
+                    {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+                ],
+            }
+        ],
+        optional_params={"oci_compartment_id": TEST_COMPARTMENT_ID},
+        litellm_params={},
+        headers={},
+    )
+
+    assert transformed_request["chatRequest"]["messages"] == [
+        {
+            "role": "USER",
+            "content": [
+                {"type": "TEXT", "text": "Summarize this."},
+                {"type": "IMAGE", "imageUrl": {"url": pdf_data_url}},
+            ],
+        }
+    ]
diff --git a/tests/unit/llms/sap/chat/test_sap_transformation.py b/tests/unit/llms/sap/chat/test_sap_transformation.py
index 41e75b7bfc7..f95bf9b7845 100644
--- a/tests/unit/llms/sap/chat/test_sap_transformation.py
+++ b/tests/unit/llms/sap/chat/test_sap_transformation.py
@@ -1,4 +1,6 @@
 import warnings
+from typing import Final
+
 import pytest
 from pydantic import ValidationError
 
@@ -908,6 +910,22 @@ class TestCacheControl:
         assert "cache_control" not in content[0]
         assert content[1].get("cache_control") == {"type": "ephemeral"}
 
+    def test_pdf_file_part_reaches_sap_as_an_image_url_data_url(self) -> None:
+        pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+        messages: Final = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Summarize this."},
+                    {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+                ],
+            }
+        ]
+        body: Final = self._transform("anthropic--claude-3-5-sonnet", messages)
+        content: Final = self._template(body)[0]["content"]
+        assert content[1]["type"] == "image_url"
+        assert content[1]["image_url"]["url"] == pdf_data_url
+
     def test_message_level_cache_control_folded_onto_trailing_image_block(self):
         messages = [
             {
diff --git a/tests/unit/llms/snowflake/chat/test_snowflake_chat_transformation.py b/tests/unit/llms/snowflake/chat/test_snowflake_chat_transformation.py
index 36c319d9524..a31f15551e9 100644
--- a/tests/unit/llms/snowflake/chat/test_snowflake_chat_transformation.py
+++ b/tests/unit/llms/snowflake/chat/test_snowflake_chat_transformation.py
@@ -18,6 +18,7 @@ import pytest
 import litellm
 from litellm import completion, acompletion
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.anthropic.messages.adapters.transformation import LiteLLMAnthropicMessagesAdapter
 from litellm.llms.snowflake.chat.transformation import SnowflakeConfig, SnowflakeStreamingHandler
 from litellm.types.utils import ModelResponse
 
@@ -479,6 +480,35 @@ class TestSnowflakeCortexClaudeFixes:
             }
         ]
 
+    def test_messages_api_pdf_reaches_cortex_claude_as_a_document_block(self) -> None:
+        translated: Final = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai(
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": "ZmFrZQ=="},
+                            "cache_control": {"type": "ephemeral"},
+                        },
+                        {"type": "text", "text": "What is the codeword?"},
+                    ],
+                }
+            ],
+            model="snowflake/claude-sonnet-4-6",
+        )
+
+        body: Final = self._transform(translated)
+
+        assert body["messages"][0]["content"] == [
+            {
+                "type": "document",
+                "source": {"type": "base64", "media_type": "application/pdf", "data": "ZmFrZQ=="},
+                "cache_control": {"type": "ephemeral"},
+            },
+            {"type": "text", "text": "What is the codeword?"},
+        ]
+
     def test_multipart_tool_result_preserves_text_and_converts_image(self):
         body = self._transform(
             [
diff --git a/tests/unit/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py b/tests/unit/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py
index 85a2124ab02..98d0d314977 100644
--- a/tests/unit/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py
+++ b/tests/unit/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py
@@ -13,6 +13,7 @@ These tests verify that:
 
 import json
 import os
+from typing import Final
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -422,3 +423,52 @@ async def test_vertex_ai_gemma_vision_passthrough():
         assert isinstance(content, list), f"Expected list content, got: {content}"
         image_parts = [p for p in content if p.get("type") == "image_url"]
         assert image_parts, f"No image_url part in forwarded message content: {content}"
+
+
+@pytest.mark.asyncio
+async def test_vertex_ai_gemma_pdf_file_part_is_sent_as_image_url() -> None:
+    pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQKJcfsj6IKMSAwIG9iago8PD4+CmVuZG9iagp0cmFpbGVyCjw8Pj4KJSVFT0YK"
+    messages: Final = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What does the PDF say?"},
+                {"type": "file", "file": {"file_data": pdf_data_url, "filename": "report.pdf"}},
+            ],
+        }
+    ]
+
+    mock_response: Final = MagicMock()
+    mock_response.status_code = 200
+    mock_response.headers = {}
+    mock_response.json.return_value = _MOCK_RESPONSE_JSON
+
+    mock_vertexai: Final = MagicMock()
+    mock_vertexai.preview = MagicMock()
+
+    with (
+        patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler") as mock_http_handler,
+        patch(
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
+            return_value=("fake-token", "test-project"),
+        ),
+        patch.dict(
+            "sys.modules",
+            {"vertexai": mock_vertexai, "vertexai.preview": mock_vertexai.preview},
+        ),
+        patch.dict(litellm.model_cost, _GEMMA_MODEL_COST_ENTRY, clear=False),
+    ):
+        mock_http_handler.return_value.post = AsyncMock(return_value=mock_response)
+
+        await litellm.acompletion(
+            model="vertex_ai/google/gemma-4-26b-a4b-it-maas",
+            messages=messages,
+            vertex_ai_project="test-project",
+        )
+
+        body: Final = json.loads(mock_http_handler.return_value.post.call_args.kwargs["data"])
+        user_msg: Final = next(m for m in body["messages"] if m["role"] == "user")
+        assert user_msg["content"] == [
+            {"type": "text", "text": "What does the PDF say?"},
+            {"type": "image_url", "image_url": {"url": pdf_data_url}},
+        ]
diff --git a/tests/unit/proxy/guardrails/test_prompt_security_guardrails.py b/tests/unit/proxy/guardrails/test_prompt_security_guardrails.py
index e66e19dd1b4..b08646f1611 100644
--- a/tests/unit/proxy/guardrails/test_prompt_security_guardrails.py
+++ b/tests/unit/proxy/guardrails/test_prompt_security_guardrails.py
@@ -1,6 +1,7 @@
 import asyncio
 import base64
 from collections.abc import Mapping, Sequence
+from typing import Final
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -8,6 +9,7 @@ from fastapi.exceptions import HTTPException
 from httpx import ReadTimeout, Request, Response
 
 import litellm
+from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import (
     PromptSecurityGuardrail,
@@ -1247,3 +1249,54 @@ async def test_check_tool_results_enabled(monkeypatch: pytest.MonkeyPatch):
 
     assert "indirect_prompt_injection" in str(excinfo.value.detail)
 
+
+@pytest.mark.asyncio
+async def test_messages_api_pdf_reaches_file_sanitization_and_is_blocked() -> None:
+    sanitize_url: Final = "https://test.prompt.security/api/sanitizeFile"
+    upload: Final = AsyncMock(
+        return_value=Response(200, json={"jobId": "pdf-job"}, request=Request("POST", sanitize_url))
+    )
+    poll: Final = AsyncMock(
+        return_value=Response(
+            200,
+            json={"status": "done", "content": "", "metadata": {"action": "block", "violations": ["Malicious File"]}},
+            request=Request("GET", sanitize_url),
+        )
+    )
+    guardrail: Final = PromptSecurityGuardrail(
+        api_key="test-key",
+        api_base="https://test.prompt.security",
+        guardrail_name="test-guard",
+        event_hook="pre_call",
+        default_on=True,
+    )
+    guardrail.poll_interval = 0
+    pdf: Final = b"%PDF-1.4 pelican"
+    request: Final = {
+        "model": "gpt-5.6",
+        "max_tokens": 64,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "document",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "application/pdf",
+                            "data": base64.b64encode(pdf).decode(),
+                        },
+                    },
+                    {"type": "text", "text": "Summarize this PDF"},
+                ],
+            }
+        ],
+    }
+
+    with patch.object(guardrail.async_handler, "post", upload), patch.object(guardrail.async_handler, "get", poll):
+        with pytest.raises(HTTPException) as blocked:
+            await AnthropicMessagesHandler().process_input_messages(request, guardrail)
+
+    assert blocked.value.status_code == 400
+    assert "Malicious File" in str(blocked.value.detail)
+    assert upload.call_args.kwargs["files"]["file"][1] == pdf
diff --git a/tests/unit/test_main.py b/tests/unit/test_main.py
index e119fe133d5..e10352e0b3d 100644
--- a/tests/unit/test_main.py
+++ b/tests/unit/test_main.py
@@ -23,7 +23,7 @@ import respx
 from openai import APITimeoutError, AsyncOpenAI, OpenAI
 from openai._legacy_response import LegacyAPIResponse
 from openai.types.chat.chat_completion import ChatCompletion
-from pydantic import TypeAdapter
+from pydantic import BaseModel, ConfigDict, TypeAdapter
 
 import litellm
 from litellm import (
@@ -10200,6 +10200,309 @@ async def test_atranscription_ignores_a_non_string_base_url_when_api_base_is_set
     assert response.text == "four score"
 
 
+_PDF_FILE_PART: Final = {
+    "type": "file",
+    "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"},
+}
+_SUMMARIZE_TEXT_PART: Final = {"type": "text", "text": "Summarize this."}
+
+
+def _pdf_messages() -> list[dict[str, object]]:
+    return [{"role": "user", "content": [_SUMMARIZE_TEXT_PART, _PDF_FILE_PART]}]
+
+
+class _SentChatRequest(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    messages: list[dict[str, object]]
+
+
+def _sent_messages(respx_mock: respx.MockRouter) -> list[dict[str, object]]:
+    request: Final[httpx.Request] = respx_mock.calls[0].request
+    return _SentChatRequest.model_validate_json(request.content).messages
+
+
+def _mock_pdf_chat_route(respx_mock: respx.MockRouter, api_base: str) -> respx.Route:
+    return respx_mock.post(url__regex=rf"{api_base}/chat/completions.*").mock(
+        return_value=httpx.Response(
+            status_code=200,
+            json={
+                "id": "chatcmpl-pdf",
+                "object": "chat.completion",
+                "created": 1712697600,
+                "model": "qa-pdf-model",
+                "choices": [{"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}],
+                "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
+            },
+        )
+    )
+
+
+def test_drop_params_drops_a_pdf_the_model_cannot_read(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12347/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="openai/qa-text-only-model",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        model_info={"supports_pdf_input": False},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": [_SUMMARIZE_TEXT_PART]}]
+
+
+@pytest.mark.parametrize(
+    "pdf_file",
+    [
+        {"filename": "report.pdf", "file_data": "JVBERi0xLjQK"},
+        {"file_id": "file-report", "format": "application/pdf"},
+        {"file_id": "file-report", "filename": "Report.PDF"},
+    ],
+    ids=["bare_base64_named_pdf", "file_id_with_pdf_format", "file_id_named_pdf"],
+)
+def test_drop_params_drops_a_pdf_file_part_without_a_data_url(
+    respx_mock: respx.MockRouter, pdf_file: dict[str, str]
+) -> None:
+    api_base: Final = "http://localhost:12350/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="openai/qa-text-only-model",
+        messages=[{"role": "user", "content": [_SUMMARIZE_TEXT_PART, {"type": "file", "file": pdf_file}]}],
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        model_info={"supports_pdf_input": False},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": [_SUMMARIZE_TEXT_PART]}]
+
+
+def test_drop_params_drops_a_user_turn_that_held_only_a_pdf(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12352/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="openai/qa-text-only-model",
+        messages=[
+            {"role": "user", "content": [_PDF_FILE_PART]},
+            {"role": "user", "content": [_SUMMARIZE_TEXT_PART]},
+        ],
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        model_info={"supports_pdf_input": False},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": [_SUMMARIZE_TEXT_PART]}]
+
+
+def test_drop_params_keeps_roles_alternating_after_dropping_a_pdf_only_turn(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12353/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="openai/qa-text-only-model",
+        messages=[
+            {"role": "user", "content": [_SUMMARIZE_TEXT_PART]},
+            {"role": "assistant", "content": "Send the file."},
+            {"role": "user", "content": [_PDF_FILE_PART]},
+            {"role": "assistant", "content": "Got it."},
+            {"role": "user", "content": [_SUMMARIZE_TEXT_PART]},
+        ],
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        model_info={"supports_pdf_input": False},
+        drop_params=True,
+        ensure_alternating_roles=True,
+    )
+
+    sent_roles: Final = [message["role"] for message in _sent_messages(respx_mock)]
+    assert sent_roles == ["user", "assistant", "user", "assistant", "user"]
+
+
+def test_drop_params_keeps_an_image_data_url_whose_filename_says_pdf(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12351/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+    image_part: Final = {
+        "type": "file",
+        "file": {"filename": "scan.pdf", "file_data": "data:image/png;base64,iVBORw0KGgo="},
+    }
+
+    litellm.completion(
+        model="openai/qa-text-only-model",
+        messages=[{"role": "user", "content": [_SUMMARIZE_TEXT_PART, image_part]}],
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        model_info={"supports_pdf_input": False},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": [_SUMMARIZE_TEXT_PART, image_part]}]
+
+
+def test_pdf_to_a_model_that_cannot_read_it_is_rejected_without_drop_params(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12348/v1"
+    route: Final = _mock_pdf_chat_route(respx_mock, api_base)
+
+    with pytest.raises(litellm.BadRequestError, match="does not accept PDF input"):
+        litellm.completion(
+            model="openai/qa-text-only-model",
+            messages=_pdf_messages(),
+            api_base=api_base,
+            api_key="fake_openai_api_key",
+            model_info={"supports_pdf_input": False},
+        )
+
+    assert not route.called
+
+
+def test_pdf_to_a_model_with_unknown_pdf_support_is_sent_even_with_drop_params(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12349/v1"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="openai/qa-unlisted-model",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_openai_api_key",
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == _pdf_messages()
+
+
+def test_pdf_to_ollama_is_rejected_without_drop_params() -> None:
+    with pytest.raises(litellm.BadRequestError, match="does not accept PDF input"):
+        litellm.completion(model="ollama_chat/llama3.2", messages=_pdf_messages(), api_base="http://127.0.0.1:9")
+
+
+def _azure_ai_model_without_pdf_input() -> str:
+    return next(
+        name
+        for name, entry in litellm.model_cost.items()
+        if entry.get("litellm_provider") == "azure_ai" and entry.get("supports_pdf_input") is False
+    )
+
+
+def test_pdf_to_a_deployment_whose_base_model_cannot_read_it_is_rejected() -> None:
+    with pytest.raises(litellm.BadRequestError, match="does not accept PDF input"):
+        litellm.completion(
+            model="azure_ai/qa-custom-deployment",
+            messages=_pdf_messages(),
+            api_base="http://127.0.0.1:9",
+            api_key="fake_azure_ai_api_key",
+            model_info={"base_model": _azure_ai_model_without_pdf_input()},
+        )
+
+
+def test_drop_params_drops_a_pdf_a_deployments_base_model_cannot_read(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12353"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="azure_ai/qa-custom-deployment",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_azure_ai_api_key",
+        model_info={"base_model": _azure_ai_model_without_pdf_input()},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": _SUMMARIZE_TEXT_PART["text"]}]
+
+
+def _azure_ai_model_with_pdf_input() -> str:
+    return next(
+        name
+        for name, entry in litellm.model_cost.items()
+        if entry.get("litellm_provider") == "azure_ai" and entry.get("supports_pdf_input") is True
+    )
+
+
+def test_pdf_to_a_deployment_whose_base_model_param_cannot_read_it_is_rejected() -> None:
+    with pytest.raises(litellm.BadRequestError, match="does not accept PDF input"):
+        litellm.completion(
+            model="azure_ai/qa-custom-deployment",
+            messages=_pdf_messages(),
+            api_base="http://127.0.0.1:9",
+            api_key="fake_azure_ai_api_key",
+            base_model=_azure_ai_model_without_pdf_input(),
+        )
+
+
+def test_drop_params_keeps_a_pdf_a_deployments_base_model_param_reads(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12357"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="azure_ai/qa-custom-deployment",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_azure_ai_api_key",
+        base_model=_azure_ai_model_with_pdf_input(),
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == _pdf_messages()
+
+
+def test_drop_params_rejects_a_request_whose_only_user_turn_was_a_pdf(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12354/v1"
+    route: Final = _mock_pdf_chat_route(respx_mock, api_base)
+
+    with pytest.raises(litellm.BadRequestError, match="no user message is left"):
+        litellm.completion(
+            model="openai/qa-text-only-model",
+            messages=[
+                {"role": "system", "content": "You read documents."},
+                {"role": "user", "content": [_PDF_FILE_PART]},
+            ],
+            api_base=api_base,
+            api_key="fake_openai_api_key",
+            model_info={"supports_pdf_input": False},
+            drop_params=True,
+        )
+
+    assert not route.called
+
+
+def test_drop_params_drops_a_pdf_an_unlisted_azure_ai_model_never_declared_it_reads(
+    respx_mock: respx.MockRouter,
+) -> None:
+    api_base: Final = "http://localhost:12355"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="azure_ai/qa-unlisted-deployment",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_azure_ai_api_key",
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == [{"role": "user", "content": _SUMMARIZE_TEXT_PART["text"]}]
+
+
+def test_drop_params_keeps_a_pdf_an_azure_ai_deployment_declares_it_reads(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12356"
+    _mock_pdf_chat_route(respx_mock, api_base)
+
+    litellm.completion(
+        model="azure_ai/qa-unlisted-deployment",
+        messages=_pdf_messages(),
+        api_base=api_base,
+        api_key="fake_azure_ai_api_key",
+        model_info={"supports_pdf_input": True},
+        drop_params=True,
+    )
+
+    assert _sent_messages(respx_mock) == _pdf_messages()
+
+
 @pytest.mark.parametrize("stream", (False, True))
 @pytest.mark.parametrize("asynchronous", (False, True))
 @pytest.mark.parametrize("profile,custom_price", ((None, False), ("us.openai.gpt-6-sol", False), (None, True)))
diff --git a/tests/unit/test_router_model_cost_isolation.py b/tests/unit/test_router_model_cost_isolation.py
index 20b0055f729..91f0b2339c7 100644
--- a/tests/unit/test_router_model_cost_isolation.py
+++ b/tests/unit/test_router_model_cost_isolation.py
@@ -614,6 +614,63 @@ def test_capability_flag_stays_on_the_deployment_that_set_it(flag: str) -> None:
         _restore_model_cost_entries(model_keys)
 
 
+def test_pdf_opt_out_on_one_deployment_does_not_block_its_sibling(respx_mock: respx.MockRouter) -> None:
+    api_base: Final = "http://localhost:12361/v1"
+    backend_model: Final = "openai/qa-shared-pdf-backend"
+    pdf_messages: Final = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Summarize this."},
+                {
+                    "type": "file",
+                    "file": {"filename": "report.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"},
+                },
+            ],
+        }
+    ]
+    route: Final = respx_mock.post(url__regex=rf"{api_base}/chat/completions.*").mock(
+        return_value=httpx.Response(
+            status_code=200,
+            json={
+                "id": "chatcmpl-pdf",
+                "object": "chat.completion",
+                "created": 1712697600,
+                "model": "qa-shared-pdf-backend",
+                "choices": [{"index": 0, "message": {"role": "assistant", "content": "ok"}, "finish_reason": "stop"}],
+                "usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
+            },
+        )
+    )
+    model_keys: Final = {
+        key: litellm.model_cost.get(key) for key in ("qa-pdf-marked", "qa-pdf-unmarked", backend_model)
+    }
+    try:
+        router: Final = Router(
+            model_list=[
+                {
+                    "model_name": "pdf-blocked",
+                    "litellm_params": {"model": backend_model, "api_base": api_base, "api_key": "fake-key"},
+                    "model_info": {"id": "qa-pdf-marked", "supports_pdf_input": False},
+                },
+                {
+                    "model_name": "pdf-allowed",
+                    "litellm_params": {"model": backend_model, "api_base": api_base, "api_key": "fake-key"},
+                    "model_info": {"id": "qa-pdf-unmarked"},
+                },
+            ],
+        )
+
+        with pytest.raises(litellm.BadRequestError, match="does not accept PDF input"):
+            router.completion(model="pdf-blocked", messages=pdf_messages)
+        router.completion(model="pdf-allowed", messages=pdf_messages)
+
+        assert route.call_count == 1
+        assert json.loads(route.calls[0].request.content)["messages"] == pdf_messages
+    finally:
+        _restore_model_cost_entries(model_keys)
+
+
 def test_token_count_settings_stay_on_the_deployment_that_set_them() -> None:
     """A deployment's ``token_count_model`` and ``token_count_framing`` must not land on
     the shared ``{provider}/{model}`` key, or a sibling deployment of that model would count with them."""
diff --git a/tests/unit/test_stream_chunk_builder_annotations.py b/tests/unit/test_stream_chunk_builder_annotations.py
index 9c7ad4126b0..c710697b39c 100644
--- a/tests/unit/test_stream_chunk_builder_annotations.py
+++ b/tests/unit/test_stream_chunk_builder_annotations.py
@@ -6,6 +6,8 @@ annotation chunk, losing any annotations that arrived in later chunks.
 This fix merges annotations from ALL chunks.
 """
 
+from typing import Final
+
 from litellm import stream_chunk_builder
 from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices
 
@@ -189,3 +191,46 @@ def test_stream_chunk_builder_no_annotations():
 
     message = response["choices"][0]["message"]
     assert not hasattr(message, "annotations") or message.annotations is None
+
+
+def test_stream_chunk_builder_moves_typed_provider_annotations_like_non_streaming() -> None:
+    url_citation: Final = {
+        "type": "url_citation",
+        "url_citation": {"url": "https://example.com", "title": "Example", "start_index": 0, "end_index": 5},
+    }
+    file_annotation: Final = {"type": "file", "file": {"hash": "abc123", "name": "report.pdf"}}
+    chunks: Final = [
+        ModelResponseStream(
+            id="chatcmpl-test",
+            created=1700000000,
+            model="test-model",
+            object="chat.completion.chunk",
+            choices=[
+                StreamingChoices(
+                    finish_reason=None,
+                    index=0,
+                    delta=Delta(content="Hello", role="assistant", provider_specific_fields={"region": "eu"}),
+                )
+            ],
+        ),
+        ModelResponseStream(
+            id="chatcmpl-test",
+            created=1700000000,
+            model="test-model",
+            object="chat.completion.chunk",
+            choices=[
+                StreamingChoices(
+                    finish_reason="stop",
+                    index=0,
+                    delta=Delta(content=None, annotations=[url_citation, file_annotation]),
+                )
+            ],
+        ),
+    ]
+
+    response: Final = stream_chunk_builder(chunks=chunks)
+    assert response is not None
+
+    message: Final = response["choices"][0]["message"]
+    assert message.annotations == [url_citation]
+    assert message.provider_specific_fields == {"region": "eu", "annotations": [file_annotation]}