diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py
index 7930a035..b4e2c127 100644
--- a/src/plugins/models/utils_model.py
+++ b/src/plugins/models/utils_model.py
@@ -19,13 +19,20 @@ logger = get_module_logger("model_utils")
 class LLMRequest:
     # 定义需要转换的模型列表，作为类变量避免重复
     MODELS_NEEDING_TRANSFORMATION = [
-        "o3-mini",
-        "o1-mini",
-        "o1-preview",
+        "o1",
         "o1-2024-12-17",
-        "o1-preview-2024-09-12",
-        "o3-mini-2025-01-31",
+        "o1-mini",
         "o1-mini-2024-09-12",
+        "o1-preview",
+        "o1-preview-2024-09-12",
+        "o1-pro",
+        "o1-pro-2025-03-19",
+        "o3",
+        "o3-2025-04-16",
+        "o3-mini",
+        "o3-mini-2025-01-31",
+        "o4-mini",
+        "o4-mini-2025-04-16"
     ]
 
     def __init__(self, model, **kwargs):
@@ -38,7 +45,9 @@ class LLMRequest:
             logger.error(f"配置错误：找不到对应的配置项 - {str(e)}")
             raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
         self.model_name = model["name"]
-        self.params = kwargs
+        # 从 kwargs 中提取 request_type，如果没有提供则默认为 "default"
+        self.request_type = kwargs.pop("request_type", "default")
+        self.params = self._init_transform_params(kwargs)
 
         self.stream = model.get("stream", False)
         self.pri_in = model.get("pri_in", 0)
@@ -47,8 +56,27 @@ class LLMRequest:
         # 获取数据库实例
         self._init_database()
 
-        # 从 kwargs 中提取 request_type，如果没有提供则默认为 "default"
-        self.request_type = kwargs.pop("request_type", "default")
+    def _init_transform_params(self, raw: dict) -> dict:
+        """
+        把 raw kwargs：
+          - 如果 model 需要转换，就把 max_tokens→max_completion_tokens，并丢掉 temperature
+          - 其它一律保留
+        """
+        need = {m.lower() for m in self.MODELS_NEEDING_TRANSFORMATION}
+        is_o = any(self.model_name.lower().startswith(m) for m in need)
+
+        newp = {}
+        for k, v in raw.items():
+            # 对于 o 系列要丢掉 temperature
+            if is_o and k == "temperature":
+                continue
+            # 对于 o 系列把 max_tokens 改名
+            if is_o and k == "max_tokens":
+                newp["max_completion_tokens"] = v
+            # 其余都原样保留
+            else:
+                newp[k] = v
+        return newp
 
     @staticmethod
     def _init_database():
@@ -510,55 +538,37 @@ class LLMRequest:
         logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
         raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
 
-    async def _transform_parameters(self, params: dict) -> dict:
-        """
-        根据模型名称转换参数：
-        - 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temperature' 参数，
-        并将 'max_tokens' 重命名为 'max_completion_tokens'
-        """
-        # 复制一份参数，避免直接修改原始数据
-        new_params = dict(params)
-
-        if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION:
-            # 删除 'temperature' 参数（如果存在）
-            new_params.pop("temperature", None)
-            # 如果存在 'max_tokens'，则重命名为 'max_completion_tokens'
-            if "max_tokens" in new_params:
-                new_params["max_completion_tokens"] = new_params.pop("max_tokens")
-        return new_params
-
     async def _build_payload(self, prompt: str, image_base64: str = None, image_format: str = None) -> dict:
         """构建请求体"""
         # 复制一份参数，避免直接修改 self.params
-        params_copy = await self._transform_parameters(self.params)
+        params_copy = self.params
+        # 准备 messages
         if image_base64:
-            payload = {
-                "model": self.model_name,
-                "messages": [
-                    {
-                        "role": "user",
-                        "content": [
-                            {"type": "text", "text": prompt},
-                            {
-                                "type": "image_url",
-                                "image_url": {"url": f"data:image/{image_format.lower()};base64,{image_base64}"},
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/{image_format.lower()};base64,{image_base64}"
                             },
-                        ],
-                    }
-                ],
-                "max_tokens": global_config.max_response_length,
-                **params_copy,
-            }
+                        },
+                    ],
+                }
+            ]
         else:
-            payload = {
-                "model": self.model_name,
-                "messages": [{"role": "user", "content": prompt}],
-                "max_tokens": global_config.max_response_length,
-                **params_copy,
-            }
-        # 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
-        if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
-            payload["max_completion_tokens"] = payload.pop("max_tokens")
+            messages = [{"role": "user", "content": prompt}]
+        # 基础 payload（暂不加 max_tokens）
+        payload = {
+            "model": self.model_name,
+            "messages": messages,
+            **params_copy,
+        }
+        # 如果用户既没传 max_tokens，也没传 max_completion_tokens，就补上一个默认值
+        if "max_tokens" not in params_copy and "max_completion_tokens" not in params_copy:
+            payload["max_tokens"] = global_config.max_response_length
         return payload
 
     def _default_response_handler(
@@ -648,16 +658,20 @@ class LLMRequest:
 
     async def generate_response_async(self, prompt: str, **kwargs) -> Union[str, Tuple]:
         """异步方式根据输入的提示生成模型的响应"""
-        # 构建请求体
+        # 构建基础 payload，不硬编码 max_tokens
         data = {
             "model": self.model_name,
             "messages": [{"role": "user", "content": prompt}],
-            "max_tokens": global_config.max_response_length,
             **self.params,
             **kwargs,
         }
-
-        response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt)
+        # 如果调用者既没传 max_tokens 也没传 max_completion_tokens，就补默认值
+        if "max_tokens" not in data and "max_completion_tokens" not in data:
+            data["max_tokens"] = global_config.max_response_length
+        # 发请求
+        response = await self._execute_request(
+            endpoint="/chat/completions", payload=data, prompt=prompt
+        )
         # 原样返回响应，不做处理
         return response