diff --git a/letta/schemas/llm_config.py b/letta/schemas/llm_config.py
index 65f8fce2..ecb2c663 100644
--- a/letta/schemas/llm_config.py
+++ b/letta/schemas/llm_config.py
@@ -185,31 +185,50 @@ class LLMConfig(BaseModel):
             + (f" [ip={self.model_endpoint}]" if self.model_endpoint else "")
         )
 
+    @classmethod
+    def is_openai_reasoning_model(cls, config: "LLMConfig") -> bool:
+        return config.model_endpoint_type == "openai" and (
+            config.model.startswith("o1") or config.model.startswith("o3") or config.model.startswith("o4")
+        )
+
+    @classmethod
+    def is_anthropic_reasoning_model(cls, config: "LLMConfig") -> bool:
+        return config.model_endpoint_type == "anthropic" and (
+            config.model.startswith("claude-opus-4")
+            or config.model.startswith("claude-sonnet-4")
+            or config.model.startswith("claude-3-7-sonnet")
+        )
+
+    @classmethod
+    def is_google_vertex_reasoning_model(cls, config: "LLMConfig") -> bool:
+        return config.model_endpoint_type == "google_vertex" and (
+            config.model.startswith("gemini-2.5-flash") or config.model.startswith("gemini-2.5-pro")
+        )
+
     @classmethod
     def apply_reasoning_setting_to_config(cls, config: "LLMConfig", reasoning: bool):
         if not reasoning:
+            if cls.is_openai_reasoning_model(config) or config.model.startswith("gemini-2.5-pro"):
+                raise ValueError("Reasoning cannot be disabled for OpenAI o1/o3 models")
             config.put_inner_thoughts_in_kwargs = False
             config.enable_reasoner = False
 
         else:
             config.enable_reasoner = True
-            if (
-                config.model_endpoint_type == "anthropic"
-                and ("claude-opus-4" in config.model or "claude-sonnet-4" in config.model or "claude-3-7-sonnet" in config.model)
-            ) or (
-                config.model_endpoint_type == "google_vertex" and ("gemini-2.5-flash" in config.model or "gemini-2.0-pro" in config.model)
-            ):
+            if cls.is_anthropic_reasoning_model(config):
                 config.put_inner_thoughts_in_kwargs = False
                 if config.max_reasoning_tokens == 0:
                     config.max_reasoning_tokens = 1024
-            elif config.model_endpoint_type == "openai" and (
-                config.model.startswith("o1") or config.model.startswith("o3") or config.model.startswith("o4")
-            ):
+            elif cls.is_google_vertex_reasoning_model(config):
+                # Handle as non-reasoner until we support summary
+                config.put_inner_thoughts_in_kwargs = True
+                if config.max_reasoning_tokens == 0:
+                    config.max_reasoning_tokens = 1024
+            elif cls.is_openai_reasoning_model(config):
                 config.put_inner_thoughts_in_kwargs = False
                 if config.reasoning_effort is None:
                     config.reasoning_effort = "medium"
             else:
                 config.put_inner_thoughts_in_kwargs = True
-                config.enable_reasoner = False
 
         return config
diff --git a/tests/test_providers.py b/tests/test_providers.py
index 0b7c50e7..cb7a54bd 100644
--- a/tests/test_providers.py
+++ b/tests/test_providers.py
@@ -1,5 +1,8 @@
+from typing import Literal, Optional
+
 import pytest
 
+from letta.schemas.llm_config import LLMConfig
 from letta.schemas.providers import (
     AnthropicProvider,
     AzureProvider,
@@ -314,3 +317,51 @@ async def test_provider_llm_models_consistency():
         assert model.handle.startswith(f"{provider.name}/")
         assert model.provider_name == provider.name
         assert model.context_window > 0
+
+
+@pytest.mark.parametrize(
+    "handle, expected_enable_reasoner, expected_put_inner_thoughts_in_kwargs, expected_max_reasoning_tokens, expected_reasoning_effort, expected_exception",
+    [
+        ("openai/gpt-4o-mini", True, True, 0, None, None),
+        ("openai/gpt-4o-mini", False, False, 0, None, None),
+        ("openai/o3-mini", True, False, 0, "medium", None),
+        ("openai/o3-mini", False, False, 0, None, ValueError),
+        ("anthropic/claude-3.5-sonnet", True, True, 0, None, None),
+        ("anthropic/claude-3.5-sonnet", False, False, 0, None, None),
+        ("anthropic/claude-3-7-sonnet", True, False, 1024, None, None),
+        ("anthropic/claude-3-7-sonnet", False, False, 0, None, None),
+        ("anthropic/claude-sonnet-4", True, False, 1024, None, None),
+        ("anthropic/claude-sonnet-4", False, False, 0, None, None),
+        ("google_vertex/gemini-2.0-flash", True, True, 0, None, None),
+        ("google_vertex/gemini-2.0-flash", False, False, 0, None, None),
+        ("google_vertex/gemini-2.5-flash", True, True, 1024, None, None),
+        ("google_vertex/gemini-2.5-flash", False, False, 0, None, None),
+        ("google_vertex/gemini-2.5-pro", True, True, 1024, None, None),
+        ("google_vertex/gemini-2.5-pro", False, False, 0, None, ValueError),
+    ],
+)
+def test_reasoning_toggle_by_provider(
+    handle: str,
+    expected_enable_reasoner: bool,
+    expected_put_inner_thoughts_in_kwargs: bool,
+    expected_max_reasoning_tokens: int,
+    expected_reasoning_effort: Optional[Literal["minimal", "low", "medium", "high"]],
+    expected_exception: Optional[Exception],
+):
+    model_endpoint_type, model = handle.split("/")
+    config = LLMConfig(
+        model_endpoint_type=model_endpoint_type,
+        model=model,
+        handle=handle,
+        context_window=1024,
+    )
+    if expected_exception:
+        with pytest.raises(expected_exception):
+            LLMConfig.apply_reasoning_setting_to_config(config, reasoning=expected_enable_reasoner)
+    else:
+        new_config = LLMConfig.apply_reasoning_setting_to_config(config, reasoning=expected_enable_reasoner)
+
+        assert new_config.enable_reasoner == expected_enable_reasoner
+        assert new_config.put_inner_thoughts_in_kwargs == expected_put_inner_thoughts_in_kwargs
+        assert new_config.reasoning_effort == expected_reasoning_effort
+        assert new_config.max_reasoning_tokens == expected_max_reasoning_tokens