diff --git a/graphiti_core/llm_client/azure_openai_client.py b/graphiti_core/llm_client/azure_openai_client.py
index 3d4225c1..ad58303d 100644
--- a/graphiti_core/llm_client/azure_openai_client.py
+++ b/graphiti_core/llm_client/azure_openai_client.py
@@ -38,8 +38,16 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         azure_client: AsyncAzureOpenAI,
         config: LLMConfig | None = None,
         max_tokens: int = DEFAULT_MAX_TOKENS,
+        reasoning: str | None = None,
+        verbosity: str | None = None,
     ):
-        super().__init__(config, cache=False, max_tokens=max_tokens)
+        super().__init__(
+            config,
+            cache=False,
+            max_tokens=max_tokens,
+            reasoning=reasoning,
+            verbosity=verbosity,
+        )
         self.client = azure_client
 
     async def _create_structured_completion(
@@ -49,15 +57,29 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         temperature: float | None,
         max_tokens: int,
         response_model: type[BaseModel],
+        reasoning: str | None,
+        verbosity: str | None,
     ):
-        """Create a structured completion using Azure OpenAI's beta parse API."""
-        return await self.client.beta.chat.completions.parse(
-            model=model,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            response_format=response_model,  # type: ignore
-        )
+        """Create a structured completion using Azure OpenAI's responses.parse API."""
+        supports_reasoning = self._supports_reasoning_features(model)
+        request_kwargs = {
+            'model': model,
+            'input': messages,
+            'max_output_tokens': max_tokens,
+            'text_format': response_model,  # type: ignore
+        }
+
+        temperature_value = temperature if not supports_reasoning else None
+        if temperature_value is not None:
+            request_kwargs['temperature'] = temperature_value
+
+        if supports_reasoning and reasoning:
+            request_kwargs['reasoning'] = {'effort': reasoning}  # type: ignore
+
+        if supports_reasoning and verbosity:
+            request_kwargs['text'] = {'verbosity': verbosity}  # type: ignore
+
+        return await self.client.responses.parse(**request_kwargs)
 
     async def _create_completion(
         self,
@@ -68,10 +90,23 @@ class AzureOpenAILLMClient(BaseOpenAIClient):
         response_model: type[BaseModel] | None = None,
     ):
         """Create a regular completion with JSON format using Azure OpenAI."""
-        return await self.client.chat.completions.create(
-            model=model,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            response_format={'type': 'json_object'},
-        )
+        supports_reasoning = self._supports_reasoning_features(model)
+
+        request_kwargs = {
+            'model': model,
+            'messages': messages,
+            'max_tokens': max_tokens,
+            'response_format': {'type': 'json_object'},
+        }
+
+        temperature_value = temperature if not supports_reasoning else None
+        if temperature_value is not None:
+            request_kwargs['temperature'] = temperature_value
+
+        return await self.client.chat.completions.create(**request_kwargs)
+
+    @staticmethod
+    def _supports_reasoning_features(model: str) -> bool:
+        """Return True when the Azure model supports reasoning/verbosity options."""
+        reasoning_prefixes = ('o1', 'o3', 'gpt-5')
+        return model.startswith(reasoning_prefixes)
diff --git a/tests/llm_client/test_azure_openai_client.py b/tests/llm_client/test_azure_openai_client.py
new file mode 100644
index 00000000..fcb37d33
--- /dev/null
+++ b/tests/llm_client/test_azure_openai_client.py
@@ -0,0 +1,109 @@
+from types import SimpleNamespace
+
+import pytest
+from pydantic import BaseModel
+
+from graphiti_core.llm_client.azure_openai_client import AzureOpenAILLMClient
+from graphiti_core.llm_client.config import LLMConfig
+
+
+class DummyResponses:
+    def __init__(self):
+        self.parse_calls: list[dict] = []
+
+    async def parse(self, **kwargs):
+        self.parse_calls.append(kwargs)
+        return SimpleNamespace(output_text='{}')
+
+
+class DummyChatCompletions:
+    def __init__(self):
+        self.create_calls: list[dict] = []
+
+    async def create(self, **kwargs):
+        self.create_calls.append(kwargs)
+        message = SimpleNamespace(content='{}')
+        choice = SimpleNamespace(message=message)
+        return SimpleNamespace(choices=[choice])
+
+
+class DummyChat:
+    def __init__(self):
+        self.completions = DummyChatCompletions()
+
+
+class DummyAzureClient:
+    def __init__(self):
+        self.responses = DummyResponses()
+        self.chat = DummyChat()
+
+
+class DummyResponseModel(BaseModel):
+    foo: str
+
+
+@pytest.mark.asyncio
+async def test_structured_completion_strips_reasoning_for_unsupported_models():
+    dummy_client = DummyAzureClient()
+    client = AzureOpenAILLMClient(
+        azure_client=dummy_client,
+        config=LLMConfig(),
+        reasoning='minimal',
+        verbosity='low',
+    )
+
+    await client._create_structured_completion(
+        model='gpt-4.1',
+        messages=[],
+        temperature=0.4,
+        max_tokens=64,
+        response_model=DummyResponseModel,
+        reasoning='minimal',
+        verbosity='low',
+    )
+
+    assert len(dummy_client.responses.parse_calls) == 1
+    call_args = dummy_client.responses.parse_calls[0]
+    assert call_args['model'] == 'gpt-4.1'
+    assert call_args['input'] == []
+    assert call_args['max_output_tokens'] == 64
+    assert call_args['text_format'] is DummyResponseModel
+    assert call_args['temperature'] == 0.4
+    assert 'reasoning' not in call_args
+    assert 'text' not in call_args
+
+
+@pytest.mark.asyncio
+async def test_reasoning_fields_forwarded_for_supported_models():
+    dummy_client = DummyAzureClient()
+    client = AzureOpenAILLMClient(
+        azure_client=dummy_client,
+        config=LLMConfig(),
+        reasoning='intense',
+        verbosity='high',
+    )
+
+    await client._create_structured_completion(
+        model='o1-custom',
+        messages=[],
+        temperature=0.7,
+        max_tokens=128,
+        response_model=DummyResponseModel,
+        reasoning='intense',
+        verbosity='high',
+    )
+
+    call_args = dummy_client.responses.parse_calls[0]
+    assert 'temperature' not in call_args
+    assert call_args['reasoning'] == {'effort': 'intense'}
+    assert call_args['text'] == {'verbosity': 'high'}
+
+    await client._create_completion(
+        model='o1-custom',
+        messages=[],
+        temperature=0.7,
+        max_tokens=128,
+    )
+
+    create_args = dummy_client.chat.completions.create_calls[0]
+    assert 'temperature' not in create_args