diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index 383c1cd3e5..aff70a6e62 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -387,7 +387,7 @@ class AnthropicConfig(BaseConfig):
             _input_schema["additionalProperties"] = True
             _input_schema["properties"] = {}
         else:
-            _input_schema["properties"] = {"values": json_schema}
+            _input_schema.update(json_schema)
 
         _tool = AnthropicMessagesTool(
             name=RESPONSE_FORMAT_TOOL_NAME, input_schema=_input_schema
diff --git a/tests/llm_translation/base_llm_unit_tests.py b/tests/llm_translation/base_llm_unit_tests.py
index 32f631daad..f3614fdb4c 100644
--- a/tests/llm_translation/base_llm_unit_tests.py
+++ b/tests/llm_translation/base_llm_unit_tests.py
@@ -20,6 +20,7 @@ from litellm.utils import (
     get_optional_params,
     ProviderConfigManager,
 )
+from litellm.main import stream_chunk_builder
 from typing import Union
 
 # test_example.py
@@ -338,7 +339,7 @@ class BaseLLMChatTest(ABC):
 
     @pytest.mark.flaky(retries=6, delay=1)
     def test_json_response_pydantic_obj(self):
-        litellm.set_verbose = True
+        litellm._turn_on_debug()
         from pydantic import BaseModel
         from litellm.utils import supports_response_schema
 
@@ -995,3 +996,72 @@ class BaseOSeriesModelsTest(ABC):  # test across azure/openai
             ), "temperature should not be in the request body"
         except Exception as e:
             pytest.fail(f"Error occurred: {e}")
+
+
+class BaseAnthropicChatTest(ABC):
+    """
+    Ensures consistent result across anthropic model usage
+    """
+
+    @abstractmethod
+    def get_base_completion_call_args(self) -> dict:
+        """Must return the base completion call args"""
+        pass
+
+    @property
+    def completion_function(self):
+        return litellm.completion
+
+    def test_anthropic_response_format_streaming_vs_non_streaming(self):
+        litellm.set_verbose = True
+        args = {
+            "messages": [
+                {
+                    "content": "Your goal is to summarize the previous agent's thinking process into short descriptions to let user better understand the research progress. If no information is available, just say generic phrase like 'Doing some research...' with the given output format. Make sure to adhere to the output format no matter what, even if you don't have any information or you are not allowed to respond to the given input information (then just say generic phrase like 'Doing some research...').",
+                    "role": "system",
+                },
+                {
+                    "role": "user",
+                    "content": "Here is the input data (previous agent's output): \n\n Let's try to refine our search further, focusing more on the technical aspects of home automation and home energy system management:",
+                },
+            ],
+            "response_format": {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "final_output",
+                    "strict": True,
+                    "schema": {
+                        "description": 'Progress report for the thinking process\n\nThis model represents a snapshot of the agent\'s current progress during\nthe thinking process, providing a brief description of the current activity.\n\nAttributes:\n    agent_doing: Brief description of what the agent is currently doing.\n                Should be kept under 10 words. Example: "Learning about home automation"',
+                        "properties": {
+                            "agent_doing": {"title": "Agent Doing", "type": "string"}
+                        },
+                        "required": ["agent_doing"],
+                        "title": "ThinkingStep",
+                        "type": "object",
+                        "additionalProperties": False,
+                    },
+                },
+            },
+        }
+
+        base_completion_call_args = self.get_base_completion_call_args()
+
+        response = self.completion_function(
+            **base_completion_call_args, **args, stream=True
+        )
+
+        chunks = []
+        for chunk in response:
+            print(f"chunk: {chunk}")
+            chunks.append(chunk)
+
+        print(f"chunks: {chunks}")
+        built_response = stream_chunk_builder(chunks=chunks)
+
+        non_stream_response = self.completion_function(
+            **base_completion_call_args, **args, stream=False
+        )
+
+        assert json.loads(built_response.choices[0].message.content) == json.loads(
+            non_stream_response.choices[0].message.content
+        ), f"Got={json.loads(built_response.choices[0].message.content)}, Expected={json.loads(non_stream_response.choices[0].message.content)}"
diff --git a/tests/llm_translation/test_anthropic_completion.py b/tests/llm_translation/test_anthropic_completion.py
index da47e745e7..8f8f4084bb 100644
--- a/tests/llm_translation/test_anthropic_completion.py
+++ b/tests/llm_translation/test_anthropic_completion.py
@@ -36,7 +36,7 @@ from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk
 from litellm.llms.anthropic.common_utils import process_anthropic_headers
 from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
 from httpx import Headers
-from base_llm_unit_tests import BaseLLMChatTest
+from base_llm_unit_tests import BaseLLMChatTest, BaseAnthropicChatTest
 
 
 def streaming_format_tests(chunk: dict, idx: int):
@@ -462,7 +462,7 @@ def test_create_json_tool_call_for_response_format():
 from litellm import completion
 
 
-class TestAnthropicCompletion(BaseLLMChatTest):
+class TestAnthropicCompletion(BaseLLMChatTest, BaseAnthropicChatTest):
     def get_base_completion_call_args(self) -> dict:
         return {"model": "anthropic/claude-3-5-sonnet-20240620"}