diff --git a/litellm/constants.py b/litellm/constants.py
index d346bc46f51..c1e1e68ef46 100644
--- a/litellm/constants.py
+++ b/litellm/constants.py
@@ -111,15 +111,19 @@ _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600  # 1 hour, re-use the same httpx client fo
 # Aiohttp connection pooling - prevents memory leaks from unbounded connection growth
 # Set to 0 for unlimited (not recommended for production)
 AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 300))
-AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT_PER_HOST", 50))
+AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int(
+    os.getenv("AIOHTTP_CONNECTOR_LIMIT_PER_HOST", 50)
+)
 AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120))
 AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300))
 # enable_cleanup_closed is only needed for Python versions with the SSL leak bug
 # Fixed in Python 3.12.7+ and 3.13.1+ (see https://github.com/python/cpython/pull/118960)
 # Reference: https://github.com/aio-libs/aiohttp/blob/master/aiohttp/connector.py#L74-L78
-AIOHTTP_NEEDS_CLEANUP_CLOSED = (
-    (3, 13, 0) <= sys.version_info < (3, 13, 1) or sys.version_info < (3, 12, 7)
-)
+AIOHTTP_NEEDS_CLEANUP_CLOSED = (3, 13, 0) <= sys.version_info < (
+    3,
+    13,
+    1,
+) or sys.version_info < (3, 12, 7)
 
 # WebSocket constants
 # Default to None (unlimited) to match OpenAI's official agents SDK behavior
@@ -157,7 +161,9 @@ REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer"
 REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer"
 REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_team_spend_update_buffer"
 REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_org_spend_update_buffer"
-REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_end_user_spend_update_buffer"
+REDIS_DAILY_END_USER_SPEND_UPDATE_BUFFER_KEY = (
+    "litellm_daily_end_user_spend_update_buffer"
+)
 REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_agent_spend_update_buffer"
 REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer"
 MAX_REDIS_BUFFER_DEQUEUE_COUNT = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100))
@@ -283,7 +289,9 @@ MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int(
 DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000))
 #### Networking settings ####
 request_timeout: float = float(os.getenv("REQUEST_TIMEOUT", 6000))  # time in seconds
-DEFAULT_A2A_AGENT_TIMEOUT: float = float(os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000))  # 10 minutes
+DEFAULT_A2A_AGENT_TIMEOUT: float = float(
+    os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000)
+)  # 10 minutes
 STREAM_SSE_DONE_STRING: str = "[DONE]"
 STREAM_SSE_DATA_PREFIX: str = "data: "
 ### SPEND TRACKING ###
@@ -319,8 +327,12 @@ DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE = os.getenv(
     "DD_TRACER_STREAMING_CHUNK_YIELD_RESOURCE", "streaming.chunk.yield"
 )
 
-EMAIL_BUDGET_ALERT_TTL = int(os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60))  # 24 hours in seconds
-EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE = float(os.getenv("EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE", 0.8))  # 80% of max budget
+EMAIL_BUDGET_ALERT_TTL = int(
+    os.getenv("EMAIL_BUDGET_ALERT_TTL", 24 * 60 * 60)
+)  # 24 hours in seconds
+EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE = float(
+    os.getenv("EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE", 0.8)
+)  # 80% of max budget
 ############### LLM Provider Constants ###############
 ### ANTHROPIC CONSTANTS ###
 ANTHROPIC_TOKEN_COUNTING_BETA_VERSION = os.getenv(
@@ -1067,7 +1079,15 @@ known_tokenizer_config = {
 }
 
 
-OPENAI_FINISH_REASONS = ["stop", "length", "function_call", "content_filter", "null", "finish_reason_unspecified", "malformed_function_call"]
+OPENAI_FINISH_REASONS = [
+    "stop",
+    "length",
+    "function_call",
+    "content_filter",
+    "null",
+    "finish_reason_unspecified",
+    "malformed_function_call",
+]
 HUMANLOOP_PROMPT_CACHE_TTL_SECONDS = int(
     os.getenv("HUMANLOOP_PROMPT_CACHE_TTL_SECONDS", 60)
 )  # 1 minute
@@ -1333,12 +1353,13 @@ MICROSOFT_USER_EMAIL_ATTRIBUTE = str(
 MICROSOFT_USER_DISPLAY_NAME_ATTRIBUTE = str(
     os.getenv("MICROSOFT_USER_DISPLAY_NAME_ATTRIBUTE", "displayName")
 )
-MICROSOFT_USER_ID_ATTRIBUTE = str(
-    os.getenv("MICROSOFT_USER_ID_ATTRIBUTE", "id")
-)
+MICROSOFT_USER_ID_ATTRIBUTE = str(os.getenv("MICROSOFT_USER_ID_ATTRIBUTE", "id"))
 MICROSOFT_USER_FIRST_NAME_ATTRIBUTE = str(
     os.getenv("MICROSOFT_USER_FIRST_NAME_ATTRIBUTE", "givenName")
 )
 MICROSOFT_USER_LAST_NAME_ATTRIBUTE = str(
     os.getenv("MICROSOFT_USER_LAST_NAME_ATTRIBUTE", "surname")
 )
+LITELLM_CONSTANT_STREAM_CHUNK_SIZE = int(
+    os.getenv("LITELLM_CONSTANT_STREAM_CHUNK_SIZE", 10)
+)
diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py
index 9cb0a00d9fc..843bbfdea61 100644
--- a/litellm/litellm_core_utils/core_helpers.py
+++ b/litellm/litellm_core_utils/core_helpers.py
@@ -1,11 +1,11 @@
 # What is this?
 ## Helper utilities
-from typing import TYPE_CHECKING, Any, Iterable, List, Literal, Optional, Union
+from typing import TYPE_CHECKING, Any, Iterable, List, Literal, Optional, Union, cast
 
 import httpx
 
 from litellm._logging import verbose_logger
-from litellm.types.llms.openai import AllMessageValues
+from litellm.types.llms.openai import AllMessageValues, OpenAIChatCompletionFinishReason
 
 if TYPE_CHECKING:
     from opentelemetry.trace import Span as _Span
@@ -60,6 +60,8 @@ def safe_divide(
 
 def map_finish_reason(
     finish_reason: str,
+) -> (
+    OpenAIChatCompletionFinishReason
 ):  # openai supports 5 stop sequences - 'stop', 'length', 'function_call', 'content_filter', 'null'
     # anthropic mapping
     if finish_reason == "stop_sequence":
@@ -96,7 +98,7 @@ def map_finish_reason(
         return "tool_calls"
     elif finish_reason == "content_filtered":
         return "content_filter"
-    return finish_reason
+    return cast(OpenAIChatCompletionFinishReason, finish_reason)
 
 
 def remove_index_from_tool_calls(
diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py
index d5bd054118d..c3c560a666a 100644
--- a/litellm/llms/bedrock/chat/converse_handler.py
+++ b/litellm/llms/bedrock/chat/converse_handler.py
@@ -4,6 +4,7 @@ from typing import Any, Optional, Union
 import httpx
 
 import litellm
+from litellm.constants import LITELLM_CONSTANT_STREAM_CHUNK_SIZE
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
@@ -29,7 +30,7 @@ def make_sync_call(
     logging_obj: LiteLLMLoggingObject,
     json_mode: Optional[bool] = False,
     fake_stream: bool = False,
-    stream_chunk_size: int = 1024,
+    stream_chunk_size: int = LITELLM_CONSTANT_STREAM_CHUNK_SIZE,
 ):
     if client is None:
         client = _get_httpx_client()  # Create a new client if none provided
@@ -67,7 +68,9 @@ def make_sync_call(
         )
     else:
         decoder = AWSEventStreamDecoder(model=model)
-        completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size))
+        completion_stream = decoder.iter_bytes(
+            response.iter_bytes(chunk_size=stream_chunk_size)
+        )
 
     # LOGGING
     logging_obj.post_call(
@@ -103,7 +106,7 @@ class BedrockConverseLLM(BaseAWSLLM):
         fake_stream: bool = False,
         json_mode: Optional[bool] = False,
         api_key: Optional[str] = None,
-        stream_chunk_size: int = 1024,
+        stream_chunk_size: int = LITELLM_CONSTANT_STREAM_CHUNK_SIZE,
     ) -> CustomStreamWrapper:
         request_data = await litellm.AmazonConverseConfig()._async_transform_request(
             model=model,
@@ -121,7 +124,7 @@ class BedrockConverseLLM(BaseAWSLLM):
             endpoint_url=api_base,
             data=data,
             headers=headers,
-            api_key=api_key
+            api_key=api_key,
         )
 
         ## LOGGING
@@ -181,7 +184,7 @@ class BedrockConverseLLM(BaseAWSLLM):
             headers=headers,
         )
         data = json.dumps(request_data)
-        
+
         prepped = self.get_request_headers(
             credentials=credentials,
             aws_region_name=litellm_params.get("aws_region_name") or "us-west-2",
@@ -189,7 +192,7 @@ class BedrockConverseLLM(BaseAWSLLM):
             endpoint_url=api_base,
             data=data,
             headers=headers,
-            api_key=api_key
+            api_key=api_key,
         )
 
         ## LOGGING
@@ -263,7 +266,9 @@ class BedrockConverseLLM(BaseAWSLLM):
     ):
         ## SETUP ##
         stream = optional_params.pop("stream", None)
-        stream_chunk_size = optional_params.pop("stream_chunk_size", 1024)
+        stream_chunk_size = optional_params.pop(
+            "stream_chunk_size", LITELLM_CONSTANT_STREAM_CHUNK_SIZE
+        )
         unencoded_model_id = optional_params.pop("model_id", None)
         fake_stream = optional_params.pop("fake_stream", False)
         json_mode = optional_params.get("json_mode", False)
@@ -279,7 +284,6 @@ class BedrockConverseLLM(BaseAWSLLM):
             custom_llm_provider="bedrock",
         )
 
-
         ### SET REGION NAME ###
         aws_region_name = self._get_aws_region_name(
             optional_params=optional_params,
@@ -303,9 +307,9 @@ class BedrockConverseLLM(BaseAWSLLM):
         aws_external_id = optional_params.pop("aws_external_id", None)
         optional_params.pop("aws_region_name", None)
 
-        litellm_params[
-            "aws_region_name"
-        ] = aws_region_name  # [DO NOT DELETE] important for async calls
+        litellm_params["aws_region_name"] = (
+            aws_region_name  # [DO NOT DELETE] important for async calls
+        )
 
         credentials: Credentials = self.get_credentials(
             aws_access_key_id=aws_access_key_id,
@@ -379,7 +383,7 @@ class BedrockConverseLLM(BaseAWSLLM):
                 timeout=timeout,
                 client=client,
                 credentials=credentials,
-                api_key=api_key
+                api_key=api_key,
             )  # type: ignore
 
         ## TRANSFORMATION ##
@@ -392,7 +396,7 @@ class BedrockConverseLLM(BaseAWSLLM):
             headers=extra_headers,
         )
         data = json.dumps(_data)
-        
+
         prepped = self.get_request_headers(
             credentials=credentials,
             aws_region_name=aws_region_name,
@@ -400,7 +404,7 @@ class BedrockConverseLLM(BaseAWSLLM):
             endpoint_url=proxy_endpoint_url,
             data=data,
             headers=headers,
-            api_key=api_key
+            api_key=api_key,
         )
 
         ## LOGGING
diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py
index dfa1f02a155..931e5a76b4c 100644
--- a/litellm/llms/bedrock/chat/invoke_handler.py
+++ b/litellm/llms/bedrock/chat/invoke_handler.py
@@ -6,15 +6,7 @@ import copy
 import time
 import types
 from functools import partial
-from typing import (
-    AsyncIterator,
-    Callable,
-    Iterator,
-    Optional,
-    Tuple,
-    cast,
-    get_args,
-)
+from typing import AsyncIterator, Callable, Iterator, Optional, Tuple, cast, get_args
 
 import httpx  # type: ignore
 
@@ -22,6 +14,7 @@ import litellm
 from litellm import verbose_logger
 from litellm._uuid import uuid
 from litellm.caching.caching import InMemoryCache
+from litellm.constants import LITELLM_CONSTANT_STREAM_CHUNK_SIZE
 from litellm.litellm_core_utils.core_helpers import map_finish_reason
 from litellm.litellm_core_utils.litellm_logging import Logging
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
@@ -51,11 +44,7 @@ from litellm.types.llms.openai import (
     ChatCompletionToolCallFunctionChunk,
     ChatCompletionUsageBlock,
 )
-from litellm.types.utils import (
-    ChatCompletionMessageToolCall,
-    Choices,
-    Delta,
-)
+from litellm.types.utils import ChatCompletionMessageToolCall, Choices, Delta
 from litellm.types.utils import GenericStreamingChunk as GChunk
 from litellm.types.utils import (
     ModelResponse,
@@ -192,17 +181,19 @@ async def make_call(
     fake_stream: bool = False,
     json_mode: Optional[bool] = False,
     bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None,
-    stream_chunk_size: int = 1024,
+    stream_chunk_size: int = LITELLM_CONSTANT_STREAM_CHUNK_SIZE,
 ):
     try:
         if client is None:
             client = get_async_httpx_client(
                 llm_provider=litellm.LlmProviders.BEDROCK,
-                params={"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
-                if logging_obj
-                and logging_obj.litellm_params
-                and logging_obj.litellm_params.get("ssl_verify")
-                else None,
+                params=(
+                    {"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
+                    if logging_obj
+                    and logging_obj.litellm_params
+                    and logging_obj.litellm_params.get("ssl_verify")
+                    else None
+                ),
             )  # Create a new client if none provided
 
         response = await client.post(
@@ -287,16 +278,18 @@ def make_sync_call(
     fake_stream: bool = False,
     json_mode: Optional[bool] = False,
     bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None,
-    stream_chunk_size: int = 1024,
+    stream_chunk_size: int = LITELLM_CONSTANT_STREAM_CHUNK_SIZE,
 ):
     try:
         if client is None:
             client = _get_httpx_client(
-                params={"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
-                if logging_obj
-                and logging_obj.litellm_params
-                and logging_obj.litellm_params.get("ssl_verify")
-                else None
+                params=(
+                    {"ssl_verify": logging_obj.litellm_params.get("ssl_verify")}
+                    if logging_obj
+                    and logging_obj.litellm_params
+                    and logging_obj.litellm_params.get("ssl_verify")
+                    else None
+                )
             )
 
         response = client.post(
@@ -406,9 +399,9 @@ class BedrockLLM(BaseAWSLLM):
 
         # Claude 3+ indicators (all use Messages API)
         messages_api_indicators = [
-            "claude-3",      # Claude 3.x models
-            "claude-opus-4", # Claude Opus 4
-            "claude-sonnet-4", # Claude Sonnet 4
+            "claude-3",  # Claude 3.x models
+            "claude-opus-4",  # Claude Opus 4
+            "claude-sonnet-4",  # Claude Sonnet 4
             "claude-haiku-4",  # Claude Haiku 4
         ]
 
@@ -546,9 +539,9 @@ class BedrockLLM(BaseAWSLLM):
                             content=None,
                         )
                         model_response.choices[0].message = _message  # type: ignore
-                        model_response._hidden_params[
-                            "original_response"
-                        ] = outputText  # allow user to access raw anthropic tool calling response
+                        model_response._hidden_params["original_response"] = (
+                            outputText  # allow user to access raw anthropic tool calling response
+                        )
                     if (
                         _is_function_call is True
                         and stream is not None
@@ -559,8 +552,10 @@ class BedrockLLM(BaseAWSLLM):
                         )
                         # return an iterator
                         streaming_model_response = ModelResponse(stream=True)
-                        streaming_model_response.choices[0].finish_reason = getattr(
-                            model_response.choices[0], "finish_reason", "stop"
+                        cast(
+                            Choices, streaming_model_response.choices[0]
+                        ).finish_reason = map_finish_reason(
+                            getattr(model_response.choices[0], "finish_reason", "stop")
                         )
                         # streaming_model_response.choices = [litellm.utils.StreamingChoices()]
                         streaming_choice = litellm.utils.StreamingChoices()
@@ -605,8 +600,8 @@ class BedrockLLM(BaseAWSLLM):
                                 logging_obj=logging_obj,
                             )
 
-                    model_response.choices[0].finish_reason = map_finish_reason(
-                        completion_response.get("stop_reason", "")
+                    cast(Choices, model_response.choices[0]).finish_reason = (
+                        map_finish_reason(completion_response.get("stop_reason", ""))
                     )
                     _usage = litellm.Usage(
                         prompt_tokens=completion_response["usage"]["input_tokens"],
@@ -641,8 +636,8 @@ class BedrockLLM(BaseAWSLLM):
 
                     # Set finish reason
                     if "finish_reason" in choice:
-                        model_response.choices[0].finish_reason = map_finish_reason(
-                            choice["finish_reason"]
+                        cast(Choices, model_response.choices[0]).finish_reason = (
+                            map_finish_reason(choice["finish_reason"])
                         )
 
                     # Set usage if available
@@ -781,7 +776,9 @@ class BedrockLLM(BaseAWSLLM):
 
         ## SETUP ##
         stream = optional_params.pop("stream", None)
-        stream_chunk_size = optional_params.pop("stream_chunk_size", 1024)
+        stream_chunk_size = optional_params.pop(
+            "stream_chunk_size", LITELLM_CONSTANT_STREAM_CHUNK_SIZE
+        )
 
         provider = self.get_bedrock_invoke_provider(model)
         modelId = self.get_bedrock_model_id(
@@ -881,9 +878,9 @@ class BedrockLLM(BaseAWSLLM):
                     ):  # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in
                         inference_params[k] = v
                 if stream is True:
-                    inference_params[
-                        "stream"
-                    ] = True  # cohere requires stream = True in inference params
+                    inference_params["stream"] = (
+                        True  # cohere requires stream = True in inference params
+                    )
                 data = json.dumps({"prompt": prompt, **inference_params})
         elif provider == "anthropic":
             if self.is_claude_messages_api_model(model):
@@ -1222,7 +1219,7 @@ class BedrockLLM(BaseAWSLLM):
         logger_fn=None,
         headers={},
         client: Optional[AsyncHTTPHandler] = None,
-        stream_chunk_size: int = 1024,
+        stream_chunk_size: int = LITELLM_CONSTANT_STREAM_CHUNK_SIZE,
     ) -> CustomStreamWrapper:
         # The call is not made here; instead, we prepare the necessary objects for the stream.
 
diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
index a7065caece2..cb74ef89da4 100644
--- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
+++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
@@ -12,6 +12,7 @@ from typing import (
 
 import httpx
 
+from litellm.constants import LITELLM_CONSTANT_STREAM_CHUNK_SIZE
 from litellm.llms.anthropic.common_utils import AnthropicModelInfo
 from litellm.llms.anthropic.experimental_pass_through.messages.transformation import (
     AnthropicMessagesConfig,
@@ -114,9 +115,7 @@ class AmazonAnthropicClaudeMessagesConfig(
             stream=stream,
         )
 
-    def _remove_ttl_from_cache_control(
-        self, anthropic_messages_request: Dict
-    ) -> None:
+    def _remove_ttl_from_cache_control(self, anthropic_messages_request: Dict) -> None:
         """
         Remove `ttl` field from cache_control in messages.
         Bedrock doesn't support the ttl field in cache_control.
@@ -132,7 +131,10 @@ class AmazonAnthropicClaudeMessagesConfig(
                         for item in content:
                             if isinstance(item, dict) and "cache_control" in item:
                                 cache_control = item["cache_control"]
-                                if isinstance(cache_control, dict) and "ttl" in cache_control:
+                                if (
+                                    isinstance(cache_control, dict)
+                                    and "ttl" in cache_control
+                                ):
                                     cache_control.pop("ttl", None)
 
     def _supports_extended_thinking_on_bedrock(self, model: str) -> bool:
@@ -154,10 +156,18 @@ class AmazonAnthropicClaudeMessagesConfig(
 
         # Supported models on Bedrock for extended thinking
         supported_patterns = [
-            "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5",  # Opus 4.5
-            "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1",  # Opus 4.1
-            "opus-4", "opus_4",                               # Opus 4
-            "sonnet-4", "sonnet_4",                           # Sonnet 4
+            "opus-4.5",
+            "opus_4.5",
+            "opus-4-5",
+            "opus_4_5",  # Opus 4.5
+            "opus-4.1",
+            "opus_4.1",
+            "opus-4-1",
+            "opus_4_1",  # Opus 4.1
+            "opus-4",
+            "opus_4",  # Opus 4
+            "sonnet-4",
+            "sonnet_4",  # Sonnet 4
         ]
 
         return any(pattern in model_lower for pattern in supported_patterns)
@@ -230,7 +240,9 @@ class AmazonAnthropicClaudeMessagesConfig(
             input_examples_used: Whether input examples are used
             beta_set: The set of beta headers to modify in-place
         """
-        if tool_search_used and not (programmatic_tool_calling_used or input_examples_used):
+        if tool_search_used and not (
+            programmatic_tool_calling_used or input_examples_used
+        ):
             beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER)
             if "opus-4" in model.lower() or "opus_4" in model.lower():
                 beta_set.add("tool-search-tool-2025-10-19")
@@ -242,13 +254,13 @@ class AmazonAnthropicClaudeMessagesConfig(
     ) -> None:
         """
         Convert Anthropic output_format to inline schema in message content.
-        
+
         Bedrock Invoke doesn't support the output_format parameter, so we embed
         the schema directly into the user message content as text instructions.
-        
+
         This approach adds the schema to the last user message, instructing the model
         to respond in the specified JSON format.
-        
+
         Args:
             output_format: The output_format dict with 'type' and 'schema'
             anthropic_messages_request: The request dict to modify in-place
@@ -256,40 +268,37 @@ class AmazonAnthropicClaudeMessagesConfig(
         Ref: https://aws.amazon.com/blogs/machine-learning/structured-data-response-with-amazon-bedrock-prompt-engineering-and-tool-use/
         """
         import json
-        
+
         # Extract schema from output_format
         schema = output_format.get("schema")
         if not schema:
             return
-        
+
         # Get messages from the request
         messages = anthropic_messages_request.get("messages", [])
         if not messages:
             return
-        
+
         # Find the last user message
         last_user_message_idx = None
         for idx in range(len(messages) - 1, -1, -1):
             if messages[idx].get("role") == "user":
                 last_user_message_idx = idx
                 break
-        
+
         if last_user_message_idx is None:
             return
-        
+
         last_user_message = messages[last_user_message_idx]
         content = last_user_message.get("content", [])
-        
+
         # Ensure content is a list
         if isinstance(content, str):
             content = [{"type": "text", "text": content}]
             last_user_message["content"] = content
-        
+
         # Add schema as text content to the message
-        schema_text = {
-            "type": "text",
-            "text": json.dumps(schema)
-        }
+        schema_text = {"type": "text", "text": json.dumps(schema)}
         content.append(schema_text)
 
     def transform_anthropic_messages_request(
@@ -336,14 +345,14 @@ class AmazonAnthropicClaudeMessagesConfig(
                 output_format=output_format,
                 anthropic_messages_request=anthropic_messages_request,
             )
-            
+
         # 6. AUTO-INJECT beta headers based on features used
         anthropic_model_info = AnthropicModelInfo()
         tools = anthropic_messages_optional_request_params.get("tools")
         messages_typed = cast(List[AllMessageValues], messages)
         tool_search_used = anthropic_model_info.is_tool_search_used(tools)
-        programmatic_tool_calling_used = anthropic_model_info.is_programmatic_tool_calling_used(
-            tools
+        programmatic_tool_calling_used = (
+            anthropic_model_info.is_programmatic_tool_calling_used(tools)
         )
         input_examples_used = anthropic_model_info.is_input_examples_used(tools)
 
@@ -376,8 +385,7 @@ class AmazonAnthropicClaudeMessagesConfig(
 
         if beta_set:
             anthropic_messages_request["anthropic_beta"] = list(beta_set)
-        
-            
+
         return anthropic_messages_request
 
     def get_async_streaming_response_iterator(
@@ -395,7 +403,7 @@ class AmazonAnthropicClaudeMessagesConfig(
         )
         # Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients.
         return self.bedrock_sse_wrapper(
-            completion_stream=completion_stream, 
+            completion_stream=completion_stream,
             litellm_logging_obj=litellm_logging_obj,
             request_body=request_body,
         )
@@ -414,14 +422,14 @@ class AmazonAnthropicClaudeMessagesConfig(
         from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import (
             BaseAnthropicMessagesStreamingIterator,
         )
+
         handler = BaseAnthropicMessagesStreamingIterator(
             litellm_logging_obj=litellm_logging_obj,
             request_body=request_body,
         )
-        
+
         async for chunk in handler.async_sse_wrapper(completion_stream):
             yield chunk
-        
 
 
 class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder):
@@ -433,7 +441,7 @@ class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder):
         Iterator to return Bedrock invoke response in anthropic /messages format
         """
         super().__init__(model=model)
-        self.DEFAULT_CHUNK_SIZE = 1024
+        self.DEFAULT_CHUNK_SIZE = LITELLM_CONSTANT_STREAM_CHUNK_SIZE
 
     def _chunk_parser(
         self, chunk_data: dict
diff --git a/litellm/llms/sagemaker/chat/transformation.py b/litellm/llms/sagemaker/chat/transformation.py
index 2b458fbc438..d5629568dd3 100644
--- a/litellm/llms/sagemaker/chat/transformation.py
+++ b/litellm/llms/sagemaker/chat/transformation.py
@@ -12,6 +12,7 @@ from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast
 import httpx
 from httpx._models import Headers
 
+from litellm.constants import LITELLM_CONSTANT_STREAM_CHUNK_SIZE
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
@@ -154,7 +155,7 @@ class SagemakerChatConfig(OpenAIGPTConfig, BaseAWSLLM):
 
         custom_stream_decoder = AWSEventStreamDecoder(model="", is_messages_api=True)
         completion_stream = custom_stream_decoder.iter_bytes(
-            response.iter_bytes(chunk_size=1024)
+            response.iter_bytes(chunk_size=LITELLM_CONSTANT_STREAM_CHUNK_SIZE)
         )
 
         streaming_response = CustomStreamWrapper(
@@ -204,7 +205,7 @@ class SagemakerChatConfig(OpenAIGPTConfig, BaseAWSLLM):
 
         custom_stream_decoder = AWSEventStreamDecoder(model="", is_messages_api=True)
         completion_stream = custom_stream_decoder.aiter_bytes(
-            response.aiter_bytes(chunk_size=1024)
+            response.aiter_bytes(chunk_size=LITELLM_CONSTANT_STREAM_CHUNK_SIZE)
         )
 
         streaming_response = CustomStreamWrapper(