diff --git a/README.md b/README.md
index 956263e..3d38adb 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,8 @@ The **ListHelper** collection is a comprehensive set of custom nodes for ComfyUI
 1. [AudioListCombine](#audiolistcombine-node)
 2. [NumberListGenerator](#numberlistgenerator-node)
 3. [PromptSplitByDelimiter](#promptsplitbydelimiter-node)
+4. OpenRouterLLM (For Free Bananas)
+![Demo](Readme/demo6.jpg)
 
 ---
 
diff --git a/Readme/demo6.jpg b/Readme/demo6.jpg
new file mode 100644
index 0000000..2b6d69f
Binary files /dev/null and b/Readme/demo6.jpg differ
diff --git a/models.json b/models.json
new file mode 100644
index 0000000..7b5df14
--- /dev/null
+++ b/models.json
@@ -0,0 +1,15 @@
+{
+  "models": [
+    "google/gemini-2.5-flash-image-preview:free",
+    "google/gemma-3-27b-it:free",
+    "qwen/qwen2.5-vl-32b-instruct:free",
+    "mistralai/mistral-small-3.2-24b-instruct:free",
+    "meta-llama/llama-4-maverick:free",
+    "openai/gpt-oss-20b:free",
+    "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+    "moonshotai/kimi-dev-72b:free",
+    "qwen/qwen2.5-vl-72b-instruct:free",
+    "moonshotai/kimi-k2:free",
+    "deepseek/deepseek-chat-v3.1:free"
+  ]
+}
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
index a9b3bf0..1b21797 100644
--- a/nodes.py
+++ b/nodes.py
@@ -24,6 +24,7 @@ from comfy.cli_args import args
 from typing import List, Dict, Any, Tuple
 from random import Random
 from datetime import datetime
+from .openrouter_llm import OpenRouterLLM
 
 class AudioListGenerator:
     @classmethod
@@ -1069,6 +1070,7 @@ NODE_CLASS_MAPPINGS = {
     "LoadVideoPath": LoadVideoPath,
     "SaveVideoPath": SaveVideoPath,
     "FrameMatch": FrameMatch,
+    "OpenRouterLLM": OpenRouterLLM,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1082,5 +1084,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LoadVideoPath": "LoadVideoPath",
     "SaveVideoPath": "SaveVideoPath",
     "FrameMatch": "FrameMatch",
+    "OpenRouterLLM": "OpenRouter LLM",
 }
 
diff --git a/openrouter_llm.py b/openrouter_llm.py
new file mode 100644
index 0000000..5ff9cbe
--- /dev/null
+++ b/openrouter_llm.py
@@ -0,0 +1,495 @@
+import requests
+import json
+import base64
+import os
+import io
+import re
+import torch
+from PIL import Image
+import numpy as np
+
+class OpenRouterLLM:
+    """
+    OpenRouter LLM節點，用於處理OpenRouter的LLM類型
+    支援圖片和文字輸入輸出，API金鑰管理，動態模型管理
+    """
+    
+    @classmethod
+    def _load_models(cls):
+        """載入模型列表從models.json檔案"""
+        models_file = os.path.join(os.path.dirname(__file__), "models.json")
+        try:
+            with open(models_file, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+                return data.get('models', [])
+        except Exception as e:
+            print(f"⚠️ 載入models.json失敗: {e}")
+            # 返回默認模型列表
+            return [
+                "google/gemini-2.5-flash-image-preview:free",
+                "google/gemma-3-27b-it:free",
+                "qwen/qwen2.5-vl-32b-instruct:free",
+                "mistralai/mistral-small-3.2-24b-instruct:free",
+                "meta-llama/llama-4-maverick:free",
+                "openai/gpt-oss-20b:free",
+                "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+                "moonshotai/kimi-dev-72b:free",
+                "qwen/qwen2.5-vl-72b-instruct:free",
+                "moonshotai/kimi-k2:free"
+            ]
+    
+    @classmethod
+    def _save_models(cls, models_list):
+        """保存模型列表到models.json檔案"""
+        models_file = os.path.join(os.path.dirname(__file__), "models.json")
+        try:
+            with open(models_file, 'w', encoding='utf-8') as f:
+                json.dump({"models": models_list}, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"❌ 保存models.json失敗: {e}")
+    
+    def _add_custom_model(self, model_name):
+        """添加自定義模型到models.json檔案"""
+        try:
+            current_models = self._load_models()
+            
+            # 檢查模型是否已存在
+            if model_name not in current_models:
+                current_models.append(model_name)
+                self._save_models(current_models)
+                print(f"✅ 已添加新模型: {model_name}")
+            else:
+                print(f"ℹ️ 模型已存在: {model_name}")
+        except Exception as e:
+            print(f"❌ 添加自定義模型失敗: {e}")
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        # 從JSON檔案載入模型列表
+        text_models = cls._load_models()
+        
+        # 在列表末尾添加"Add Custom Model..."選項
+        text_models_with_add = text_models + ["Add Custom Model..."]
+        
+        return {
+            "required": {
+                "api_key": ("STRING", {"multiline": False, "default": "", "placeholder": "輸入您的OpenRouter API金鑰"}),
+                "user_prompt": ("STRING", {"multiline": True, "default": "Please analyze the provided content."}),
+                "text_model": (text_models_with_add, {"default": text_models_with_add[0]}),
+            },
+            "optional": {
+                "custom_model": ("STRING", {"multiline": False, "default": "", "placeholder": "輸入自定義模型名稱 (選擇Add Custom Model...時使用)"}),
+                "system_prompt": ("STRING", {"multiline": True, "default": ""}),
+                "image_input_1": ("IMAGE",),
+                "image_input_2": ("IMAGE",),
+                "image_input_3": ("IMAGE",),
+            }
+        }
+    
+    RETURN_TYPES = ("IMAGE", "STRING")
+    RETURN_NAMES = ("image_output", "text_output")
+    FUNCTION = "process_llm"
+    CATEGORY = "ListHelper"
+    
+    def __init__(self):
+        self.config_file = "config.json"
+        
+    def _load_config(self):
+        """載入配置文件"""
+        try:
+            if os.path.exists(self.config_file):
+                with open(self.config_file, 'r', encoding='utf-8') as f:
+                    return json.load(f)
+        except Exception as e:
+            print(f"載入配置文件失敗: {e}")
+        return {}
+    
+    def _save_config(self, config):
+        """保存配置文件"""
+        try:
+            with open(self.config_file, 'w', encoding='utf-8') as f:
+                json.dump(config, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"保存配置文件失敗: {e}")
+    
+    def _get_api_key(self, api_key_input):
+        """獲取API金鑰"""
+        config = self._load_config()
+        
+        # 如果輸入了新的API金鑰，則更新配置
+        if api_key_input.strip():
+            config['openrouter_api_key'] = api_key_input.strip()
+            self._save_config(config)
+            return api_key_input.strip()
+        
+        # 否則使用配置文件中的金鑰
+        return config.get('openrouter_api_key', '')
+    
+    def _tensor_to_base64(self, tensor):
+        """將ComfyUI圖像tensor轉換為base64編碼"""
+        # tensor shape: [H, W, C] (0-1 range)
+        if tensor.dim() == 4:
+            tensor = tensor.squeeze(0)  # 移除批次維度
+        
+        # 轉換為numpy並調整範圍到0-255
+        numpy_image = (tensor.cpu().numpy() * 255).astype(np.uint8)
+        
+        # 轉換為PIL圖像
+        pil_image = Image.fromarray(numpy_image)
+        
+        # 轉換為base64
+        buffer = io.BytesIO()
+        pil_image.save(buffer, format='PNG')
+        image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+        
+        return f"data:image/png;base64,{image_base64}"
+    
+    def _base64_to_tensor(self, base64_str):
+        """將base64編碼轉換為ComfyUI圖像tensor"""
+        try:
+            # 移除各種可能的前綴
+            prefixes = [
+                'data:image/png;base64,',
+                'data:image/jpeg;base64,',
+                'data:image/jpg;base64,',
+            ]
+            
+            original_str = base64_str
+            for prefix in prefixes:
+                if base64_str.startswith(prefix):
+                    base64_str = base64_str[len(prefix):]
+                    break
+            
+            # 清理base64字符串（移除可能的換行符和空格）
+            base64_str = re.sub(r'[\n\r\s]', '', base64_str)
+            
+            # 解碼base64
+            image_bytes = base64.b64decode(base64_str)
+            
+            # 轉換為PIL圖像
+            pil_image = Image.open(io.BytesIO(image_bytes))
+            
+            # 確保是RGB模式
+            if pil_image.mode != 'RGB':
+                pil_image = pil_image.convert('RGB')
+            
+            # 轉換為numpy數組
+            numpy_image = np.array(pil_image).astype(np.float32) / 255.0
+            
+            # 轉換為tensor並添加批次維度 [1, H, W, C]
+            tensor = torch.from_numpy(numpy_image).unsqueeze(0)
+            
+            return tensor
+            
+        except Exception as e:
+            print(f"❌ base64轉tensor失敗: {e}")
+            return torch.zeros(1, 1, 1, 3)
+    
+    def _url_to_tensor(self, image_url):
+        """從URL下載圖像並轉換為ComfyUI圖像tensor"""
+        try:
+            # 下載圖像
+            response = requests.get(image_url, timeout=30)
+            response.raise_for_status()
+            
+            # 轉換為PIL圖像
+            pil_image = Image.open(io.BytesIO(response.content))
+            
+            # 確保是RGB模式
+            if pil_image.mode != 'RGB':
+                pil_image = pil_image.convert('RGB')
+            
+            # 轉換為numpy數組
+            numpy_image = np.array(pil_image).astype(np.float32) / 255.0
+            
+            # 轉換為tensor並添加批次維度 [1, H, W, C]
+            tensor = torch.from_numpy(numpy_image).unsqueeze(0)
+            
+            return tensor
+            
+        except Exception as e:
+            print(f"❌ 從URL載入圖像失敗: {e}")
+            return torch.zeros(1, 1, 1, 3)
+    
+    def _call_openrouter_api(self, api_key, model, messages):
+        """調用OpenRouter API"""
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "HTTP-Referer": "",
+            "X-Title": "ComfyUI"
+        }
+        
+        data = {
+            "model": model,
+            "messages": messages
+        }
+        
+        try:
+            response = requests.post(
+                "https://openrouter.ai/api/v1/chat/completions",
+                headers=headers,
+                data=json.dumps(data),
+                timeout=60
+            )
+            
+            if response.status_code == 200:
+                return response.json()
+            elif response.status_code == 429:
+                # Rate limit錯誤
+                try:
+                    error_data = response.json()
+                    return {"error": {"code": 429, "message": error_data.get("error", {}).get("message", "Rate limit exceeded")}}
+                except:
+                    return {"error": {"code": 429, "message": "Rate limit exceeded"}}
+            else:
+                try:
+                    error_data = response.json()
+                    return {"error": error_data.get("error", {"message": f"HTTP {response.status_code}"})}
+                except:
+                    return {"error": {"message": f"HTTP {response.status_code}: {response.text[:200]}"}}
+                
+        except Exception as e:
+            print(f"❌ API調用異常: {e}")
+            return None
+    
+    def process_llm(self, api_key, user_prompt, text_model, custom_model="", system_prompt="",
+                   image_input_1=None, image_input_2=None, image_input_3=None):
+        """處理LLM請求"""
+        
+        # 獲取API金鑰
+        actual_api_key = self._get_api_key(api_key)
+        if not actual_api_key:
+            return (torch.zeros(1, 1, 1, 3), "❌ 錯誤: 請提供OpenRouter API金鑰")
+        
+        # 處理模型選擇
+        if text_model == "Add Custom Model...":
+            if not custom_model or not custom_model.strip():
+                return (torch.zeros(1, 1, 1, 3), "❌ 請在custom_model欄位輸入自定義模型名稱")
+            
+            # 使用自定義模型
+            selected_model = custom_model.strip()
+            
+            # 將新模型添加到models.json中
+            self._add_custom_model(selected_model)
+        else:
+            # 使用選擇的預設模型
+            selected_model = text_model
+        
+        # 準備消息內容
+        messages = []
+        
+        # 添加系統提示（如果有的話）
+        if system_prompt and system_prompt.strip():
+            messages.append({"role": "system", "content": system_prompt.strip()})
+        
+        # 構建用戶消息
+        user_content = []
+        
+        # 添加文字內容
+        if user_prompt and user_prompt.strip():
+            user_content.append({
+                "type": "text",
+                "text": user_prompt.strip()
+            })
+        
+        # 檢查是否有圖像輸入
+        has_images = any([image_input_1 is not None, image_input_2 is not None, image_input_3 is not None])
+        
+        # 確定使用的模型
+        if has_images:
+            # 有圖像輸入時，強制使用gemini-2.5-flash-image-preview:free
+            actual_model = "google/gemini-2.5-flash-image-preview:free"
+        else:
+            # 無圖像輸入時，使用用戶選擇的模型
+            actual_model = selected_model
+        
+        # 檢查是否使用圖像生成模型（無論是否有圖像輸入）
+        is_image_model = actual_model == "google/gemini-2.5-flash-image-preview:free"
+        
+        if has_images:
+            # 有圖像輸入的情況
+            # 為圖像生成添加特殊提示
+            if not user_prompt or not user_prompt.strip():
+                combined_text = "請分析這些圖像並生成一個相關的新圖像。請返回生成的圖像。"
+            else:
+                combined_text = user_prompt.strip()
+                if "生成" in combined_text or "創建" in combined_text or "製作" in combined_text:
+                    combined_text += "\n\n請返回生成的圖像。"
+            
+            # 重新添加文字內容
+            user_content = [{
+                "type": "text",
+                "text": combined_text
+            }]
+            
+            # 添加圖像到用戶消息
+            for i, image_input in enumerate([image_input_1, image_input_2, image_input_3], 1):
+                if image_input is not None:
+                    try:
+                        base64_image = self._tensor_to_base64(image_input)
+                        user_content.append({
+                            "type": "image_url",
+                            "image_url": {
+                                "url": base64_image
+                            }
+                        })
+                    except Exception as e:
+                        print(f"❌ 處理圖像{i}失敗: {e}")
+        elif is_image_model:
+            # 純文字模式但使用圖像生成模型
+            combined_text = user_prompt.strip() if user_prompt else ""
+            if combined_text and ("生成" in combined_text or "創建" in combined_text or "製作" in combined_text or "畫" in combined_text or "繪製" in combined_text):
+                combined_text += "\n\n請生成並返回相應的圖像。"
+            elif not combined_text:
+                combined_text = "請生成一張圖像。"
+            
+            # 添加文字內容
+            user_content.append({
+                "type": "text",
+                "text": combined_text
+            })
+        
+        # 添加用戶消息
+        if len(user_content) > 1:
+            # 多媒體內容（包含圖像）
+            messages.append({
+                "role": "user", 
+                "content": user_content
+            })
+        elif user_content:
+            # 純文字內容
+            messages.append({
+                "role": "user",
+                "content": user_content[0]["text"]
+            })
+        else:
+            # 空內容
+            messages.append({
+                "role": "user",
+                "content": "請分析提供的內容。"
+            })
+        
+        
+        # 調用API
+        response = self._call_openrouter_api(actual_api_key, actual_model, messages)
+        
+        if not response:
+            return (torch.zeros(1, 1, 1, 3), "❌ API調用失敗")
+        
+        # 檢查rate limit錯誤
+        if 'error' in response:
+            error_info = response['error']
+            if isinstance(error_info, dict) and 'code' in error_info:
+                if error_info['code'] == 429 or 'rate_limit' in str(error_info).lower():
+                    error_msg = f"⚠️ Rate Limit達到: {error_info.get('message', '請稍後再試')}"
+                    print(error_msg)
+                    return (torch.zeros(1, 1, 1, 3), error_msg)
+                else:
+                    error_msg = f"❌ API錯誤: {error_info.get('message', str(error_info))}"
+                    return (torch.zeros(1, 1, 1, 3), error_msg)
+            else:
+                error_msg = f"❌ API錯誤: {str(error_info)}"
+                return (torch.zeros(1, 1, 1, 3), error_msg)
+        
+        if 'choices' not in response or not response['choices']:
+            return (torch.zeros(1, 1, 1, 3), "❌ API回應格式異常")
+        
+        # 獲取回應訊息
+        message = response['choices'][0]['message']
+        response_content = message.get('content', '')
+        
+        # 檢查回應是否包含圖像數據
+        output_image = torch.zeros(1, 1, 1, 3)  # 預設空圖像
+        output_text = response_content
+        found_image = False
+        
+        # 首先檢查message.images字段
+        if 'images' in message and message['images']:
+            image_data = message['images'][0]
+            
+            try:
+                if isinstance(image_data, str):
+                    if image_data.startswith('data:image/'):
+                        output_image = self._base64_to_tensor(image_data)
+                        found_image = True
+                    elif image_data.startswith('http'):
+                        output_image = self._url_to_tensor(image_data)
+                        found_image = True
+                    else:
+                        # 可能是純 base64，添加 data URL 前綴
+                        full_data_url = f"data:image/png;base64,{image_data}"
+                        output_image = self._base64_to_tensor(full_data_url)
+                        found_image = True
+                        
+                elif isinstance(image_data, dict):
+                    # 處理嵌套的 image_url 字典格式
+                    if 'type' in image_data and image_data['type'] == 'image_url' and 'image_url' in image_data:
+                        nested_image_url = image_data['image_url']
+                        if isinstance(nested_image_url, dict) and 'url' in nested_image_url:
+                            image_url_value = nested_image_url['url']
+                            if str(image_url_value).startswith('data:image/'):
+                                output_image = self._base64_to_tensor(str(image_url_value))
+                            else:
+                                output_image = self._url_to_tensor(str(image_url_value))
+                            found_image = True
+                    # 處理直接包含 url 的格式
+                    elif 'url' in image_data:
+                        url_value = str(image_data['url'])
+                        if url_value.startswith('data:image/'):
+                            output_image = self._base64_to_tensor(url_value)
+                        else:
+                            output_image = self._url_to_tensor(url_value)
+                        found_image = True
+                    # 處理直接包含 data 的格式
+                    elif 'data' in image_data:
+                        output_image = self._base64_to_tensor(str(image_data['data']))
+                        found_image = True
+                    
+            except Exception as e:
+                print(f"❌ 處理圖像失敗: {e}")
+                found_image = False
+        
+        # 如果在message.images中沒有找到圖像，檢查content中的嵌入圖像
+        if not found_image and is_image_model:
+            # 檢查URL和base64格式的圖像
+            url_patterns = [
+                r'https?://[^\s\)]+\.(?:png|jpg|jpeg|gif|webp)',
+                r'!\[.*?\]\((https?://[^\s\)]+\.(?:png|jpg|jpeg|gif|webp))\)',
+                r'<img[^>]+src=["\']([^"\']+)["\'][^>]*>',
+            ]
+            
+            base64_patterns = [
+                r'data:image/png;base64,([A-Za-z0-9+/=\n\r]+)',
+                r'data:image/jpeg;base64,([A-Za-z0-9+/=\n\r]+)',
+                r'data:image/jpg;base64,([A-Za-z0-9+/=\n\r]+)',
+            ]
+            
+            # 檢查URL格式
+            for pattern in url_patterns:
+                matches = re.findall(pattern, response_content, re.DOTALL)
+                if matches:
+                    try:
+                        output_image = self._url_to_tensor(matches[0])
+                        found_image = True
+                        output_text = re.sub(pattern, "[Generated Image]", response_content, flags=re.DOTALL)
+                        break
+                    except Exception as e:
+                        continue
+            
+            # 檢查base64格式
+            if not found_image:
+                for pattern in base64_patterns:
+                    matches = re.findall(pattern, response_content, re.DOTALL)
+                    if matches:
+                        try:
+                            clean_base64 = re.sub(r'[\n\r\s]', '', matches[0])
+                            base64_image = f"data:image/png;base64,{clean_base64}"
+                            output_image = self._base64_to_tensor(base64_image)
+                            found_image = True
+                            output_text = re.sub(pattern, "[Generated Image]", response_content, flags=re.DOTALL)
+                            break
+                        except Exception as e:
+                            continue
+        
+        return (output_image, output_text)
\ No newline at end of file