diff --git a/examples/llm_hello_world.py b/examples/llm_hello_world.py
index 9340f7d5a..62fc2ed68 100644
--- a/examples/llm_hello_world.py
+++ b/examples/llm_hello_world.py
@@ -14,9 +14,9 @@ from metagpt.logs import logger
 async def main():
     llm = LLM()
     # llm type check
-    id_ques = "what's your name"
-    logger.info(f"{id_ques}: ")
-    logger.info(await llm.aask(id_ques))
+    question = "what's your name"
+    logger.info(f"{question}: ")
+    logger.info(await llm.aask(question))
     logger.info("\n\n")
 
     logger.info(
diff --git a/metagpt/provider/base_llm.py b/metagpt/provider/base_llm.py
index 2f57b15aa..7cf3faac0 100644
--- a/metagpt/provider/base_llm.py
+++ b/metagpt/provider/base_llm.py
@@ -75,7 +75,7 @@ class BaseLLM(ABC):
             local_calc_usage (bool): some models don't calculate usage, it will overwrite LLMConfig.calc_usage
         """
         calc_usage = self.config.calc_usage and local_calc_usage
-        model = model if model else self.model
+        model = model or self.model
         usage = usage.model_dump() if isinstance(usage, BaseModel) else usage
         if calc_usage and self.cost_manager:
             try:
@@ -83,7 +83,7 @@ class BaseLLM(ABC):
                 completion_tokens = int(usage.get("completion_tokens", 0))
                 self.cost_manager.update_cost(prompt_tokens, completion_tokens, model)
             except Exception as e:
-                logger.error(f"{self.__class__.__name__} updats costs failed! exp: {e}")
+                logger.error(f"{self.__class__.__name__} updates costs failed! exp: {e}")
 
     def get_costs(self) -> Costs:
         if not self.cost_manager:
diff --git a/metagpt/provider/dashscope_api.py b/metagpt/provider/dashscope_api.py
index 7d7dcb7b7..f2b3a19a1 100644
--- a/metagpt/provider/dashscope_api.py
+++ b/metagpt/provider/dashscope_api.py
@@ -37,7 +37,7 @@ from metagpt.provider.base_llm import BaseLLM, LLMConfig
 from metagpt.provider.llm_provider_registry import LLMType, register_provider
 from metagpt.provider.openai_api import log_and_reraise
 from metagpt.utils.cost_manager import CostManager
-from metagpt.utils.token_counter import DashScope_TOKEN_COSTS
+from metagpt.utils.token_counter import DASHSCOPE_TOKEN_COSTS
 
 
 def build_api_arequest(
@@ -167,7 +167,7 @@ class DashScopeLLM(BaseLLM):
     def __init_dashscope(self):
         self.model = self.config.model
         self.api_key = self.config.api_key
-        self.token_costs = DashScope_TOKEN_COSTS
+        self.token_costs = DASHSCOPE_TOKEN_COSTS
         self.aclient: AGeneration = AGeneration
 
         # check support system_message models
diff --git a/metagpt/provider/qianfan_api.py b/metagpt/provider/qianfan_api.py
index 6f94b9cea..4cbb76566 100644
--- a/metagpt/provider/qianfan_api.py
+++ b/metagpt/provider/qianfan_api.py
@@ -22,8 +22,8 @@ from metagpt.provider.llm_provider_registry import register_provider
 from metagpt.provider.openai_api import log_and_reraise
 from metagpt.utils.cost_manager import CostManager
 from metagpt.utils.token_counter import (
-    QianFan_EndPoint_TOKEN_COSTS,
-    QianFan_MODEL_TOKEN_COSTS,
+    QIANFAN_ENDPOINT_TOKEN_COSTS,
+    QIANFAN_MODEL_TOKEN_COSTS,
 )
 
 
@@ -74,8 +74,8 @@ class QianFanLLM(BaseLLM):
         assert not (self.config.model and self.config.endpoint), "Only set `model` or `endpoint` in the config"
         assert self.config.model or self.config.endpoint, "Should set one of `model` or `endpoint` in the config"
 
-        self.token_costs = copy.deepcopy(QianFan_MODEL_TOKEN_COSTS)
-        self.token_costs.update(QianFan_EndPoint_TOKEN_COSTS)
+        self.token_costs = copy.deepcopy(QIANFAN_MODEL_TOKEN_COSTS)
+        self.token_costs.update(QIANFAN_ENDPOINT_TOKEN_COSTS)
 
         # self deployed model on the cloud not to calculate usage, it charges resource pool rental fee
         self.calc_usage = self.config.calc_usage and self.config.endpoint is None
@@ -103,8 +103,8 @@ class QianFanLLM(BaseLLM):
 
     def _update_costs(self, usage: dict):
         """update each request's token cost"""
-        model_or_endpoint = self.config.model if self.config.model else self.config.endpoint
-        local_calc_usage = True if model_or_endpoint in self.token_costs else False
+        model_or_endpoint = self.config.model or self.config.endpoint
+        local_calc_usage = model_or_endpoint in self.token_costs
         super()._update_costs(usage, model_or_endpoint, local_calc_usage)
 
     def get_choice_text(self, resp: JsonBody) -> str:
diff --git a/metagpt/utils/token_counter.py b/metagpt/utils/token_counter.py
index 220014d8f..167a1d755 100644
--- a/metagpt/utils/token_counter.py
+++ b/metagpt/utils/token_counter.py
@@ -42,7 +42,7 @@ TOKEN_COSTS = {
 QianFan Token Price https://cloud.baidu.com/doc/WENXINWORKSHOP/s/hlrk4akp7#tokens%E5%90%8E%E4%BB%98%E8%B4%B9
 Due to QianFan has multi price strategies, we unify `Tokens post-payment` as a statistical method.
 """
-QianFan_MODEL_TOKEN_COSTS = {
+QIANFAN_MODEL_TOKEN_COSTS = {
     "ERNIE-Bot-4": {"prompt": 0.017, "completion": 0.017},
     "ERNIE-Bot-8k": {"prompt": 0.0034, "completion": 0.0067},
     "ERNIE-Bot": {"prompt": 0.0017, "completion": 0.0017},
@@ -66,35 +66,35 @@ QianFan_MODEL_TOKEN_COSTS = {
     "Yi-34B-Chat": {"prompt": 0.0, "completion": 0.0},
 }
 
-QianFan_EndPoint_TOKEN_COSTS = {
-    "completions_pro": QianFan_MODEL_TOKEN_COSTS["ERNIE-Bot-4"],
-    "ernie_bot_8k": QianFan_MODEL_TOKEN_COSTS["ERNIE-Bot-8k"],
-    "completions": QianFan_MODEL_TOKEN_COSTS["ERNIE-Bot"],
-    "eb-instant": QianFan_MODEL_TOKEN_COSTS["ERNIE-Bot-turbo"],
-    "ai_apaas": QianFan_MODEL_TOKEN_COSTS["EB-turbo-AppBuilder"],
-    "ernie_speed": QianFan_MODEL_TOKEN_COSTS["ERNIE-Speed"],
-    "bloomz_7b1": QianFan_MODEL_TOKEN_COSTS["BLOOMZ-7B"],
-    "llama_2_7b": QianFan_MODEL_TOKEN_COSTS["Llama-2-7B-Chat"],
-    "llama_2_13b": QianFan_MODEL_TOKEN_COSTS["Llama-2-13B-Chat"],
-    "llama_2_70b": QianFan_MODEL_TOKEN_COSTS["Llama-2-70B-Chat"],
-    "chatglm2_6b_32k": QianFan_MODEL_TOKEN_COSTS["ChatGLM2-6B-32K"],
-    "aquilachat_7b": QianFan_MODEL_TOKEN_COSTS["AquilaChat-7B"],
-    "mixtral_8x7b_instruct": QianFan_MODEL_TOKEN_COSTS["Mixtral-8x7B-Instruct"],
-    "sqlcoder_7b": QianFan_MODEL_TOKEN_COSTS["SQLCoder-7B"],
-    "codellama_7b_instruct": QianFan_MODEL_TOKEN_COSTS["CodeLlama-7B-Instruct"],
-    "xuanyuan_70b_chat": QianFan_MODEL_TOKEN_COSTS["XuanYuan-70B-Chat-4bit"],
-    "qianfan_bloomz_7b_compressed": QianFan_MODEL_TOKEN_COSTS["Qianfan-BLOOMZ-7B-compressed"],
-    "qianfan_chinese_llama_2_7b": QianFan_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-7B"],
-    "qianfan_chinese_llama_2_13b": QianFan_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-13B"],
-    "chatlaw": QianFan_MODEL_TOKEN_COSTS["ChatLaw"],
-    "yi_34b_chat": QianFan_MODEL_TOKEN_COSTS["Yi-34B-Chat"],
+QIANFAN_ENDPOINT_TOKEN_COSTS = {
+    "completions_pro": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-4"],
+    "ernie_bot_8k": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-8k"],
+    "completions": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot"],
+    "eb-instant": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-turbo"],
+    "ai_apaas": QIANFAN_MODEL_TOKEN_COSTS["EB-turbo-AppBuilder"],
+    "ernie_speed": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Speed"],
+    "bloomz_7b1": QIANFAN_MODEL_TOKEN_COSTS["BLOOMZ-7B"],
+    "llama_2_7b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-7B-Chat"],
+    "llama_2_13b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-13B-Chat"],
+    "llama_2_70b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-70B-Chat"],
+    "chatglm2_6b_32k": QIANFAN_MODEL_TOKEN_COSTS["ChatGLM2-6B-32K"],
+    "aquilachat_7b": QIANFAN_MODEL_TOKEN_COSTS["AquilaChat-7B"],
+    "mixtral_8x7b_instruct": QIANFAN_MODEL_TOKEN_COSTS["Mixtral-8x7B-Instruct"],
+    "sqlcoder_7b": QIANFAN_MODEL_TOKEN_COSTS["SQLCoder-7B"],
+    "codellama_7b_instruct": QIANFAN_MODEL_TOKEN_COSTS["CodeLlama-7B-Instruct"],
+    "xuanyuan_70b_chat": QIANFAN_MODEL_TOKEN_COSTS["XuanYuan-70B-Chat-4bit"],
+    "qianfan_bloomz_7b_compressed": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-BLOOMZ-7B-compressed"],
+    "qianfan_chinese_llama_2_7b": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-7B"],
+    "qianfan_chinese_llama_2_13b": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-13B"],
+    "chatlaw": QIANFAN_MODEL_TOKEN_COSTS["ChatLaw"],
+    "yi_34b_chat": QIANFAN_MODEL_TOKEN_COSTS["Yi-34B-Chat"],
 }
 
 """
 DashScope Token price https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-thousand-questions-metering-and-billing
 Different model has different detail page. Attention, some model are free for a limited time.
 """
-DashScope_TOKEN_COSTS = {
+DASHSCOPE_TOKEN_COSTS = {
     "qwen-turbo": {"prompt": 0.0011, "completion": 0.0011},
     "qwen-plus": {"prompt": 0.0028, "completion": 0.0028},
     "qwen-max": {"prompt": 0.0, "completion": 0.0},