diff --git a/examples/basic_modules/llm.py b/examples/basic_modules/llm.py
index 3fd7352c7..fa82a45dc 100644
--- a/examples/basic_modules/llm.py
+++ b/examples/basic_modules/llm.py
@@ -167,14 +167,14 @@
 # Scenario 7: Using LLMFactory with MiniMax (OpenAI-compatible API)
 # Prerequisites:
 # 1. Get your API key from the MiniMax platform.
-# 2. Available models: MiniMax-M2.7 (flagship), MiniMax-M2.7-highspeed (low-latency),
-#    MiniMax-M2.5, MiniMax-M2.5-highspeed.
+# 2. Available models: MiniMax-M3 (flagship, default), MiniMax-M2.7,
+#    MiniMax-M2.7-highspeed (low-latency).
 
 cfg_mm = LLMConfigFactory.model_validate(
     {
         "backend": "minimax",
         "config": {
-            "model_name_or_path": "MiniMax-M2.7",
+            "model_name_or_path": "MiniMax-M3",
             "api_key": "your-minimax-api-key",
             "api_base": "https://api.minimax.io/v1",
             "temperature": 0.7,
diff --git a/src/memos/api/config.py b/src/memos/api/config.py
index 69efedeb3..3de2947d9 100644
--- a/src/memos/api/config.py
+++ b/src/memos/api/config.py
@@ -288,7 +288,7 @@ def qwen_config() -> dict[str, Any]:
     def minimax_config() -> dict[str, Any]:
         """Get MiniMax configuration."""
         return {
-            "model_name_or_path": os.getenv("MOS_CHAT_MODEL", "MiniMax-M2.7"),
+            "model_name_or_path": os.getenv("MOS_CHAT_MODEL", "MiniMax-M3"),
             "temperature": float(os.getenv("MOS_CHAT_TEMPERATURE", "0.8")),
             "max_tokens": int(os.getenv("MOS_MAX_TOKENS", "8000")),
             "top_p": float(os.getenv("MOS_TOP_P", "0.9")),