feat: support qwen3.6-plus

2026-06-02 00:57:41 +08:00 · 2026-04-02 16:46:58 +08:00
parent 40dfc6860f
commit b5f33e5ecd
22 changed files with 54 additions and 334 deletions
--- a/README.md
+++ b/README.md
@@ -218,7 +218,7 @@ cow install-browser
 <details>
 <summary>2. 其他配置</summary>

-+ `model`: 模型名称，Agent 模式下推荐使用 `MiniMax-M2.7`、`glm-5-turbo`、`kimi-k2.5`、`qwen3.5-plus`、`claude-sonnet-4-6`、`gemini-3.1-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
+ `model`: 模型名称，Agent 模式下推荐使用 `MiniMax-M2.7`、`glm-5-turbo`、`kimi-k2.5`、`qwen3.6-plus`、`claude-sonnet-4-6`、`gemini-3.1-pro-preview`，全部模型名称参考[common/const.py](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py)文件
 + `character_desc`：普通对话模式下的机器人系统提示词。在 Agent 模式下该配置不生效，由工作空间中的文件内容构成。
 + `subscribe_msg`：订阅消息，公众号和企业微信 channel 中请填写，当被订阅时会自动回复， 可使用特殊占位符。目前支持的占位符有{trigger_prefix}，在程序中它会自动替换成 bot 的触发词。
 </details>
@@ -303,7 +303,7 @@ sudo docker logs -f chatgpt-on-wechat

 ## 模型说明

-以下对所有可支持的模型的配置和使用方法进行说明，模型接口实现在项目的 `models/` 目录下。
+推荐通过 Web 控制台在线管理模型配置，无需手动编辑文件，详见 [模型文档](https://docs.cowagent.ai/models)。以下是手动修改 `config.json` 配置模型的说明：

 <details>
 <summary>OpenAI</summary>
@@ -411,18 +411,18 @@ sudo docker logs -f chatgpt-on-wechat

 ```json
 {
-    "model": "qwen3.5-plus",
+    "model": "qwen3.6-plus",
    "dashscope_api_key": "sk-qVxxxxG"
 }
 ```
- - `model`: 可填写 `qwen3.5-plus、qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
- - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建
+ - `model`: 可填写 `qwen3.6-plus、qwen3.5-plus、qwen3-max、qwen-max、qwen-plus、qwen-turbo、qwen-long、qwq-plus` 等
+ - `dashscope_api_key`: 通义千问的 API-KEY，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) ，在 [百炼控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建

 方式二：OpenAI 兼容方式接入，配置如下：
 ```json
 {
  "bot_type": "openai",
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "open_ai_api_key": "sk-qVxxxxG"
 }
@@ -674,7 +674,7 @@ Coding Plan 是各厂商推出的编程包月套餐，所有厂商均可通过 O

 ## 通道说明

-以下对可接入通道的配置方式进行说明，应用通道代码在项目的 `channel/` 目录下。
+推荐通过 Web 控制台在线管理通道配置，无需手动编辑文件，详见 [通道文档](https://docs.cowagent.ai/channels/weixin)。以下为手动修改 `config.json` 配置通道的说明：

 支持同时可接入多个通道，配置时可通过逗号进行分割，例如 `"channel_type": "feishu,dingtalk"`。

--- a/bridge/agent_bridge.py
+++ b/bridge/agent_bridge.py
@@ -67,7 +67,7 @@ class AgentLLMModel(LLMModel):

    _MODEL_BOT_TYPE_MAP = {
        "wenxin": const.BAIDU, "wenxin-4": const.BAIDU,
-        "xunfei": const.XUNFEI, const.QWEN: const.QWEN,
+        "xunfei": const.XUNFEI, const.QWEN: const.QWEN_DASHSCOPE,
        const.MODELSCOPE: const.MODELSCOPE,
    }
    _MODEL_PREFIX_MAP = [
--- a/bridge/bridge.py
+++ b/bridge/bridge.py
@@ -39,11 +39,8 @@ class Bridge(object):
                self.btype["chat"] = const.BAIDU
            if model_type in ["xunfei"]:
                self.btype["chat"] = const.XUNFEI
-            if model_type in [const.QWEN]:
-                self.btype["chat"] = const.QWEN
-            if model_type in [const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
+            if model_type in [const.QWEN, const.QWEN_TURBO, const.QWEN_PLUS, const.QWEN_MAX]:
                self.btype["chat"] = const.QWEN_DASHSCOPE
-            # Support Qwen3 and other DashScope models
            if model_type and (model_type.startswith("qwen") or model_type.startswith("qwq") or model_type.startswith("qvq")):
                self.btype["chat"] = const.QWEN_DASHSCOPE
            if model_type and model_type.startswith("gemini"):
--- a/channel/web/web_channel.py
+++ b/channel/web/web_channel.py
@@ -563,7 +563,7 @@ class ConfigHandler:
    _RECOMMENDED_MODELS = [
        const.MINIMAX_M2_7, const.MINIMAX_M2_5, const.MINIMAX_M2_1, const.MINIMAX_M2_1_LIGHTNING,
        const.GLM_5_TURBO, const.GLM_5, const.GLM_4_7,
-        const.QWEN35_PLUS, const.QWEN3_MAX,
+        const.QWEN36_PLUS, const.QWEN35_PLUS, const.QWEN3_MAX,
        const.KIMI_K2_5, const.KIMI_K2,
        const.DOUBAO_SEED_2_PRO, const.DOUBAO_SEED_2_CODE,
        const.CLAUDE_4_6_SONNET, const.CLAUDE_4_6_OPUS, const.CLAUDE_4_5_SONNET,
@@ -592,7 +592,7 @@ class ConfigHandler:
            "api_key_field": "dashscope_api_key",
            "api_base_key": None,
            "api_base_default": None,
-            "models": [const.QWEN35_PLUS, const.QWEN3_MAX],
+            "models": [const.QWEN36_PLUS, const.QWEN35_PLUS, const.QWEN3_MAX],
        }),
        ("moonshot", {
            "label": "Kimi",
--- a/common/const.py
+++ b/common/const.py
@@ -7,8 +7,8 @@ XUNFEI = "xunfei"
 CHATGPTONAZURE = "chatGPTOnAzure"
 LINKAI = "linkai"
 CLAUDEAPI= "claudeAPI"
-QWEN = "qwen"  # 旧版千问接入
-QWEN_DASHSCOPE = "dashscope"  # 新版千问接入(百炼)
+QWEN = "qwen"  # 千问 (兼容旧配置，实际走 DashscopeBot)
+QWEN_DASHSCOPE = "dashscope"  # 千问 DashScope 接入
 GEMINI = "gemini" 
 ZHIPU_AI = "zhipu"  
 MOONSHOT = "moonshot"
@@ -81,14 +81,14 @@ TTS_1_HD = "tts-1-hd"
 DEEPSEEK_CHAT = "deepseek-chat"  # DeepSeek-V3对话模型
 DEEPSEEK_REASONER = "deepseek-reasoner"  # DeepSeek-R1模型

-# Qwen (通义千问 - 阿里云)
-QWEN = "qwen"
+# Qwen (通义千问 - 阿里云 DashScope)
 QWEN_TURBO = "qwen-turbo"
 QWEN_PLUS = "qwen-plus"
 QWEN_MAX = "qwen-max"
 QWEN_LONG = "qwen-long"
 QWEN3_MAX = "qwen3-max"  # Qwen3 Max - Agent推荐模型
 QWEN35_PLUS = "qwen3.5-plus"  # Qwen3.5 Plus - Omni model (MultiModalConversation)
+QWEN36_PLUS = "qwen3.6-plus"  # Qwen3.6 Plus - Omni model (MultiModalConversation)
 QWQ_PLUS = "qwq-plus"

 # MiniMax
@@ -172,7 +172,7 @@ MODEL_LIST = [
              DEEPSEEK_CHAT, DEEPSEEK_REASONER,
              
              # Qwen
-              QWEN35_PLUS, QWEN3_MAX, QWEN_MAX, QWEN_PLUS, QWEN_TURBO, QWEN_LONG, QWEN,
+              QWEN36_PLUS, QWEN35_PLUS, QWEN3_MAX, QWEN_MAX, QWEN_PLUS, QWEN_TURBO, QWEN_LONG,
              
              # MiniMax
              MiniMax, MINIMAX_M2_7, MINIMAX_M2_5, MINIMAX_M2_1, MINIMAX_M2_1_LIGHTNING, MINIMAX_M2, MINIMAX_ABAB6_5,
--- a/docs/en/README.md
+++ b/docs/en/README.md
@@ -165,7 +165,7 @@ Supports mainstream model providers. Recommended models for Agent mode:
 | GLM | `glm-5-turbo` |
 | Kimi | `kimi-k2.5` |
 | Doubao | `doubao-seed-2-0-code-preview-260215` |
-| Qwen | `qwen3.5-plus` |
+| Qwen | `qwen3.6-plus` |
 | Claude | `claude-sonnet-4-6` |
 | Gemini | `gemini-3.1-pro-preview` |
 | OpenAI | `gpt-5.4` |
--- a/docs/en/models/index.mdx
+++ b/docs/en/models/index.mdx
@@ -6,7 +6,7 @@ description: Supported models and recommended choices for CowAgent
 CowAgent supports mainstream LLMs from domestic and international providers. Model interfaces are implemented in the project's `models/` directory.

 <Note>
-  For Agent mode, the following models are recommended based on quality and cost: MiniMax-M2.7, glm-5-turbo, kimi-k2.5, qwen3.5-plus, claude-sonnet-4-6, gemini-3.1-pro-preview
+  For Agent mode, the following models are recommended based on quality and cost: MiniMax-M2.7, glm-5-turbo, kimi-k2.5, qwen3.6-plus, claude-sonnet-4-6, gemini-3.1-pro-preview
 </Note>

 ## Configuration
@@ -25,7 +25,7 @@ You can also use the [LinkAI](https://link-ai.tech) platform interface to flexib
    glm-5-turbo, glm-5 and other series models
  </Card>
  <Card title="Qwen (Tongyi Qianwen)" href="/en/models/qwen">
-    qwen3.5-plus, qwen3-max and more
+    qwen3.6-plus, qwen3-max and more
  </Card>
  <Card title="Kimi" href="/en/models/kimi">
    kimi-k2.5, kimi-k2 and more
--- a/docs/en/models/qwen.mdx
+++ b/docs/en/models/qwen.mdx
@@ -5,14 +5,14 @@ description: Tongyi Qianwen model configuration

 ```json
 {
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "dashscope_api_key": "YOUR_API_KEY"
 }
 ```

 | Parameter | Description |
 | --- | --- |
-| `model` | Options include `qwen3.5-plus`, `qwen3-max`, `qwen-max`, `qwen-plus`, `qwen-turbo`, `qwq-plus`, etc. |
+| `model` | Options include `qwen3.6-plus`, `qwen3.5-plus`, `qwen3-max`, `qwen-max`, `qwen-plus`, `qwen-turbo`, `qwq-plus`, etc. |
 | `dashscope_api_key` | Create at [Bailian Console](https://bailian.console.aliyun.com/?tab=model#/api-key). See [official docs](https://bailian.console.aliyun.com/?tab=api#/api) |

 OpenAI-compatible configuration is also supported:
@@ -20,7 +20,7 @@ OpenAI-compatible configuration is also supported:
 ```json
 {
  "bot_type": "openai",
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "open_ai_api_key": "YOUR_API_KEY"
 }
--- a/docs/guide/upgrade.mdx
+++ b/docs/guide/upgrade.mdx
@@ -36,7 +36,7 @@ pip3 install -e .
 更新完成后重启服务：

 ```bash
-# 使用 Cow CLI
+# 使用 Cow CLI (推荐)
 cow restart

 # 或使用 run.sh
--- a/docs/ja/README.md
+++ b/docs/ja/README.md
@@ -165,7 +165,7 @@ sudo docker logs -f chatgpt-on-wechat
 | GLM | `glm-5-turbo` |
 | Kimi | `kimi-k2.5` |
 | Doubao | `doubao-seed-2-0-code-preview-260215` |
-| Qwen | `qwen3.5-plus` |
+| Qwen | `qwen3.6-plus` |
 | Claude | `claude-sonnet-4-6` |
 | Gemini | `gemini-3.1-pro-preview` |
 | OpenAI | `gpt-5.4` |
--- a/docs/ja/models/index.mdx
+++ b/docs/ja/models/index.mdx
@@ -6,7 +6,7 @@ description: CowAgentがサポートするモデルとおすすめの選択肢
 CowAgentは国内外の主要なLLMをサポートしています。モデルインターフェースはプロジェクトの`models/`ディレクトリに実装されています。

 <Note>
-  Agent モードでは、品質とコストのバランスから以下のモデルをおすすめします: MiniMax-M2.7、glm-5-turbo、kimi-k2.5、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-pro-preview
+  Agent モードでは、品質とコストのバランスから以下のモデルをおすすめします: MiniMax-M2.7、glm-5-turbo、kimi-k2.5、qwen3.6-plus、claude-sonnet-4-6、gemini-3.1-pro-preview
 </Note>

 ## 設定
@@ -25,7 +25,7 @@ CowAgentは国内外の主要なLLMをサポートしています。モデルイ
    glm-5-turbo、glm-5およびその他のシリーズモデル
  </Card>
  <Card title="Qwen (通义千问)" href="/ja/models/qwen">
-    qwen3.5-plus、qwen3-maxなど
+    qwen3.6-plus、qwen3-maxなど
  </Card>
  <Card title="Kimi" href="/ja/models/kimi">
    kimi-k2.5、kimi-k2など
--- a/docs/ja/models/qwen.mdx
+++ b/docs/ja/models/qwen.mdx
@@ -1,18 +1,18 @@
 ---
-title: Qwen (通义千问)
-description: 通义千问モデルの設定
+title: Qwen (通義千問)
+description: 通義千問モデルの設定
 ---

 ```json
 {
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "dashscope_api_key": "YOUR_API_KEY"
 }
 ```

 | パラメータ | 説明 |
 | --- | --- |
-| `model` | `qwen3.5-plus`、`qwen3-max`、`qwen-max`、`qwen-plus`、`qwen-turbo`、`qwq-plus`などから選択可能 |
+| `model` | `qwen3.6-plus`、`qwen3.5-plus`、`qwen3-max`、`qwen-max`、`qwen-plus`、`qwen-turbo`、`qwq-plus`などから選択可能 |
 | `dashscope_api_key` | [百炼 Console](https://bailian.console.aliyun.com/?tab=model#/api-key)で作成。[公式ドキュメント](https://bailian.console.aliyun.com/?tab=api#/api)を参照 |

 OpenAI互換の設定もサポートしています:
@@ -20,7 +20,7 @@ OpenAI互換の設定もサポートしています:
 ```json
 {
  "bot_type": "openai",
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "open_ai_api_key": "YOUR_API_KEY"
 }
--- a/docs/models/index.mdx
+++ b/docs/models/index.mdx
@@ -6,19 +6,20 @@ description: CowAgent 支持的模型及推荐选择
 CowAgent 支持国内外主流厂商的大语言模型，模型接口实现在项目的 `models/` 目录下。

 <Note>
-  Agent 模式下推荐使用以下模型，可根据效果及成本综合选择：MiniMax-M2.7、glm-5-turbo、kimi-k2.5、qwen3.5-plus、claude-sonnet-4-6、gemini-3.1-pro-preview
+  Agent 模式下推荐使用以下模型，可根据效果及成本综合选择：MiniMax-M2.7、glm-5-turbo、kimi-k2.5、qwen3.6-plus、claude-sonnet-4-6、gemini-3.1-pro-preview
+
+  同时支持使用 [LinkAI](https://link-ai.tech) 平台接口，可灵活切换多种模型，并支持知识库、工作流、插件等 Agent 能力。
 </Note>

 ## 配置方式

-根据所选模型，在 `config.json` 中填写对应的模型名称和 API Key 即可。每个模型也支持 OpenAI 兼容方式接入，将 `bot_type` 设为 `openai`，配置 `open_ai_api_base` 和 `open_ai_api_key`。
-
-同时支持使用 [LinkAI](https://link-ai.tech) 平台接口，可灵活切换多种模型，并支持知识库、工作流、插件等 Agent 能力。
-
-也可以通过 [Web 控制台](/channels/web) 在线管理模型配置，无需手动编辑配置文件：
+**方式一（推荐）：** 通过 [Web 控制台](/channels/web) 在线管理模型配置，无需手动编辑配置文件：

 <img width="850" src="https://cdn.link-ai.tech/doc/20260227173811.png" />

+**方式二：** 手动编辑 `config.json`，根据所选模型填写对应的模型名称和 API Key。每个模型也支持 OpenAI 兼容方式接入，将 `bot_type` 设为 `openai`，配置 `open_ai_api_base` 和 `open_ai_api_key` 即可。
+
+
 ## 支持的模型

 <CardGroup cols={2}>
@@ -29,7 +30,7 @@ CowAgent 支持国内外主流厂商的大语言模型，模型接口实现在
    glm-5-turbo、glm-5 等系列模型
  </Card>
  <Card title="通义千问 Qwen" href="/models/qwen">
-    qwen3.5-plus、qwen3-max 等
+    qwen3.6-plus、qwen3-max 等
  </Card>
  <Card title="Kimi" href="/models/kimi">
    kimi-k2.5、kimi-k2 等
@@ -54,6 +55,7 @@ CowAgent 支持国内外主流厂商的大语言模型，模型接口实现在
  </Card>
 </CardGroup>

+
 <Tip>
  全部模型名称可参考项目 [`common/const.py`](https://github.com/zhayujie/chatgpt-on-wechat/blob/master/common/const.py) 文件。
 </Tip>
--- a/docs/models/qwen.mdx
+++ b/docs/models/qwen.mdx
@@ -5,14 +5,14 @@ description: 通义千问模型配置

 ```json
 {
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "dashscope_api_key": "YOUR_API_KEY"
 }
 ```

 | 参数 | 说明 |
 | --- | --- |
-| `model` | 可填 `qwen3.5-plus`、`qwen3-max`、`qwen-max`、`qwen-plus`、`qwen-turbo`、`qwq-plus` 等 |
+| `model` | 可填 `qwen3.6-plus`、`qwen3.5-plus`、`qwen3-max`、`qwen-max`、`qwen-plus`、`qwen-turbo`、`qwq-plus` 等 |
 | `dashscope_api_key` | 在 [百炼控制台](https://bailian.console.aliyun.com/?tab=model#/api-key) 创建，参考 [官方文档](https://bailian.console.aliyun.com/?tab=api#/api) |

 也支持 OpenAI 兼容方式接入：
@@ -20,7 +20,7 @@ description: 通义千问模型配置
 ```json
 {
  "bot_type": "openai",
-  "model": "qwen3.5-plus",
+  "model": "qwen3.6-plus",
  "open_ai_api_base": "https://dashscope.aliyuncs.com/compatible-mode/v1",
  "open_ai_api_key": "YOUR_API_KEY"
 }
--- a/models/ali/ali_qwen_bot.py
+++ b/models/ali/ali_qwen_bot.py
@@ -1,214 +0,0 @@
-# encoding:utf-8
-
-import json
-import time
-from typing import List, Tuple
-
-import openai
-from models.openai.openai_compat import RateLimitError, Timeout, APIError, APIConnectionError
-import broadscope_bailian
-from broadscope_bailian import ChatQaMessage
-
-from models.bot import Bot
-from models.ali.ali_qwen_session import AliQwenSession
-from models.session_manager import SessionManager
-from bridge.context import ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-from common import const
-from config import conf, load_config
-
-class AliQwenBot(Bot):
-    def __init__(self):
-        super().__init__()
-        self.api_key_expired_time = self.set_api_key()
-        self.sessions = SessionManager(AliQwenSession, model=conf().get("model", const.QWEN))
-
-    def api_key_client(self):
-        return broadscope_bailian.AccessTokenClient(access_key_id=self.access_key_id(), access_key_secret=self.access_key_secret())
-
-    def access_key_id(self):
-        return conf().get("qwen_access_key_id")
-
-    def access_key_secret(self):
-        return conf().get("qwen_access_key_secret")
-
-    def agent_key(self):
-        return conf().get("qwen_agent_key")
-
-    def app_id(self):
-        return conf().get("qwen_app_id")
-
-    def node_id(self):
-        return conf().get("qwen_node_id", "")
-
-    def temperature(self):
-        return conf().get("temperature", 0.2 )
-
-    def top_p(self):
-        return conf().get("top_p", 1)
-
-    def reply(self, query, context=None):
-        # acquire reply content
-        if context.type == ContextType.TEXT:
-            logger.info("[QWEN] query={}".format(query))
-
-            session_id = context["session_id"]
-            reply = None
-            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
-            if query in clear_memory_commands:
-                self.sessions.clear_session(session_id)
-                reply = Reply(ReplyType.INFO, "记忆已清除")
-            elif query == "#清除所有":
-                self.sessions.clear_all_session()
-                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-            elif query == "#更新配置":
-                load_config()
-                reply = Reply(ReplyType.INFO, "配置已更新")
-            if reply:
-                return reply
-            session = self.sessions.session_query(query, session_id)
-            logger.debug("[QWEN] session query={}".format(session.messages))
-
-            reply_content = self.reply_text(session)
-            logger.debug(
-                "[QWEN] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
-                    session.messages,
-                    session_id,
-                    reply_content["content"],
-                    reply_content["completion_tokens"],
-                )
-            )
-            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-            elif reply_content["completion_tokens"] > 0:
-                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
-                reply = Reply(ReplyType.TEXT, reply_content["content"])
-            else:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[QWEN] reply {} used 0 tokens.".format(reply_content))
-            return reply
-
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def reply_text(self, session: AliQwenSession, retry_count=0) -> dict:
-        """
-        call bailian's ChatCompletion to get the answer
-        :param session: a conversation session
-        :param retry_count: retry count
-        :return: {}
-        """
-        try:
-            prompt, history = self.convert_messages_format(session.messages)
-            self.update_api_key_if_expired()
-            # NOTE 阿里百炼的call()函数未提供temperature参数，考虑到temperature和top_p参数作用相同，取两者较小的值作为top_p参数传入，详情见文档 https://help.aliyun.com/document_detail/2587502.htm
-            response = broadscope_bailian.Completions().call(app_id=self.app_id(), prompt=prompt, history=history, top_p=min(self.temperature(), self.top_p()))
-            completion_content = self.get_completion_content(response, self.node_id())
-            completion_tokens, total_tokens = self.calc_tokens(session.messages, completion_content)
-            return {
-                "total_tokens": total_tokens,
-                "completion_tokens": completion_tokens,
-                "content": completion_content,
-            }
-        except Exception as e:
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if isinstance(e, RateLimitError):
-                logger.warn("[QWEN] RateLimitError: {}".format(e))
-                result["content"] = "提问太快啦，请休息一下再问我吧"
-                if need_retry:
-                    time.sleep(20)
-            elif isinstance(e, Timeout):
-                logger.warn("[QWEN] Timeout: {}".format(e))
-                result["content"] = "我没有收到你的消息"
-                if need_retry:
-                    time.sleep(5)
-            elif isinstance(e, APIError):
-                logger.warn("[QWEN] Bad Gateway: {}".format(e))
-                result["content"] = "请再问我一次"
-                if need_retry:
-                    time.sleep(10)
-            elif isinstance(e, APIConnectionError):
-                logger.warn("[QWEN] APIConnectionError: {}".format(e))
-                need_retry = False
-                result["content"] = "我连接不到你的网络"
-            else:
-                logger.exception("[QWEN] Exception: {}".format(e))
-                need_retry = False
-                self.sessions.clear_session(session.session_id)
-
-            if need_retry:
-                logger.warn("[QWEN] 第{}次重试".format(retry_count + 1))
-                return self.reply_text(session, retry_count + 1)
-            else:
-                return result
-
-    def set_api_key(self):
-        api_key, expired_time = self.api_key_client().create_token(agent_key=self.agent_key())
-        broadscope_bailian.api_key = api_key
-        return expired_time
-
-    def update_api_key_if_expired(self):
-        if time.time() > self.api_key_expired_time:
-            self.api_key_expired_time = self.set_api_key()
-
-    def convert_messages_format(self, messages) -> Tuple[str, List[ChatQaMessage]]:
-        history = []
-        user_content = ''
-        assistant_content = ''
-        system_content = ''
-        for message in messages:
-            role = message.get('role')
-            if role == 'user':
-                user_content += message.get('content')
-            elif role == 'assistant':
-                assistant_content = message.get('content')
-                history.append(ChatQaMessage(user_content, assistant_content))
-                user_content = ''
-                assistant_content = ''
-            elif role =='system':
-                system_content += message.get('content')
-        if user_content == '':
-            raise Exception('no user message')
-        if system_content != '':
-            # NOTE 模拟系统消息，测试发现人格描述以"你需要扮演ChatGPT"开头能够起作用，而以"你是ChatGPT"开头模型会直接否认
-            system_qa = ChatQaMessage(system_content, '好的，我会严格按照你的设定回答问题')
-            history.insert(0, system_qa)
-        logger.debug("[QWEN] converted qa messages: {}".format([item.to_dict() for item in history]))
-        logger.debug("[QWEN] user content as prompt: {}".format(user_content))
-        return user_content, history
-
-    def get_completion_content(self, response, node_id):
-        if not response['Success']:
-            return f"[ERROR]\n{response['Code']}:{response['Message']}"
-        text = response['Data']['Text']
-        if node_id == '':
-            return text
-        # TODO: 当使用流程编排创建大模型应用时，响应结构如下，最终结果在['finalResult'][node_id]['response']['text']中，暂时先这么写
-        # {
-        #     'Success': True,
-        #     'Code': None,
-        #     'Message': None,
-        #     'Data': {
-        #         'ResponseId': '9822f38dbacf4c9b8daf5ca03a2daf15',
-        #         'SessionId': 'session_id',
-        #         'Text': '{"finalResult":{"LLM_T7islK":{"params":{"modelId":"qwen-plus-v1","prompt":"${systemVars.query}${bizVars.Text}"},"response":{"text":"作为一个AI语言模型，我没有年龄，因为我没有生日。\n我只是一个程序，没有生命和身体。"}}}}',
-        #         'Thoughts': [],
-        #         'Debug': {},
-        #         'DocReferences': []
-        #     },
-        #     'RequestId': '8e11d31551ce4c3f83f49e6e0dd998b0',
-        #     'Failed': None
-        # }
-        text_dict = json.loads(text)
-        completion_content =  text_dict['finalResult'][node_id]['response']['text']
-        return completion_content
-
-    def calc_tokens(self, messages, completion_content):
-        completion_tokens = len(completion_content)
-        prompt_tokens = 0
-        for message in messages:
-            prompt_tokens += len(message["content"])
-        return completion_tokens, prompt_tokens + completion_tokens
--- a/models/ali/ali_qwen_session.py
+++ b/models/ali/ali_qwen_session.py
@@ -1,62 +0,0 @@
-from models.session_manager import Session
-from common.log import logger
-
-"""
-    e.g.
-    [
-        {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Who won the world series in 2020?"},
-        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
-        {"role": "user", "content": "Where was it played?"}
-    ]
-"""
-
-class AliQwenSession(Session):
-    def __init__(self, session_id, system_prompt=None, model="qianwen"):
-        super().__init__(session_id, system_prompt)
-        self.model = model
-        self.reset()
-
-    def discard_exceeding(self, max_tokens, cur_tokens=None):
-        precise = True
-        try:
-            cur_tokens = self.calc_tokens()
-        except Exception as e:
-            precise = False
-            if cur_tokens is None:
-                raise e
-            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
-        while cur_tokens > max_tokens:
-            if len(self.messages) > 2:
-                self.messages.pop(1)
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
-                self.messages.pop(1)
-                if precise:
-                    cur_tokens = self.calc_tokens()
-                else:
-                    cur_tokens = cur_tokens - max_tokens
-                break
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
-                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
-                break
-            else:
-                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens, len(self.messages)))
-                break
-            if precise:
-                cur_tokens = self.calc_tokens()
-            else:
-                cur_tokens = cur_tokens - max_tokens
-        return cur_tokens
-
-    def calc_tokens(self):
-        return num_tokens_from_messages(self.messages, self.model)
-
-def num_tokens_from_messages(messages, model):
-    """Returns the number of tokens used by a list of messages."""
-    # 官方token计算规则："对于中文文本来说，1个token通常对应一个汉字；对于英文文本来说，1个token通常对应3至4个字母或1个单词"
-    # 详情请产看文档：https://help.aliyun.com/document_detail/2586397.html
-    # 目前根据字符串长度粗略估计token数，不影响正常使用
-    tokens = 0
-    for msg in messages:
-        tokens += len(msg["content"])
-    return tokens
--- a/models/bot_factory.py
+++ b/models/bot_factory.py
@@ -46,10 +46,7 @@ def create_bot(bot_type):
    elif bot_type == const.CLAUDEAPI:
        from models.claudeapi.claude_api_bot import ClaudeAPIBot
        return ClaudeAPIBot()
-    elif bot_type == const.QWEN:
-        from models.ali.ali_qwen_bot import AliQwenBot
-        return AliQwenBot()
-    elif bot_type == const.QWEN_DASHSCOPE:
+    elif bot_type in (const.QWEN, const.QWEN_DASHSCOPE):
        from models.dashscope.dashscope_bot import DashscopeBot
        return DashscopeBot()
    elif bot_type == const.GEMINI:
--- a/models/dashscope/dashscope_bot.py
+++ b/models/dashscope/dashscope_bot.py
@@ -26,15 +26,15 @@ dashscope_models = {

 # Model name prefixes that require MultiModalConversation API instead of Generation API.
 # Qwen3.5+ series are omni models that only support MultiModalConversation.
-MULTIMODAL_MODEL_PREFIXES = ("qwen3.5-",)
+MULTIMODAL_MODEL_PREFIXES = ("qwen3.5-", "qwen3.6-")


 # Qwen对话模型API
 class DashscopeBot(Bot):
    def __init__(self):
        super().__init__()
-        self.sessions = SessionManager(DashscopeSession, model=conf().get("model") or "qwen-plus")
-        self.model_name = conf().get("model") or "qwen-plus"
+        self.sessions = SessionManager(DashscopeSession, model=conf().get("model") or "qwen3.6-plus")
+        self.model_name = conf().get("model") or "qwen3.6-plus"
        self.client = dashscope.Generation
        api_key = conf().get("dashscope_api_key")
        if api_key:
--- a/plugins/cow_cli/cow_cli.py
+++ b/plugins/cow_cli/cow_cli.py
@@ -407,7 +407,7 @@ class CowCliPlugin(Plugin):
        from common import const
        _EXACT = {
            "wenxin": const.BAIDU, "wenxin-4": const.BAIDU,
-            "xunfei": const.XUNFEI, const.QWEN: const.QWEN,
+            "xunfei": const.XUNFEI, const.QWEN: const.QWEN_DASHSCOPE,
            const.MODELSCOPE: const.MODELSCOPE,
            const.MOONSHOT: const.MOONSHOT,
            "moonshot-v1-8k": const.MOONSHOT, "moonshot-v1-32k": const.MOONSHOT,
--- a/plugins/godcmd/godcmd.py
+++ b/plugins/godcmd/godcmd.py
@@ -315,7 +315,7 @@ class Godcmd(Plugin):
                    except Exception as e:
                        ok, result = False, "你没有设置私有GPT模型"
                elif cmd == "reset":
-                    if bottype in [const.OPEN_AI, const.OPENAI, const.CHATGPT, const.CHATGPTONAZURE, const.LINKAI, const.BAIDU, const.XUNFEI, const.QWEN, const.GEMINI, const.ZHIPU_AI, const.CLAUDEAPI]:
+                    if bottype in [const.OPEN_AI, const.OPENAI, const.CHATGPT, const.CHATGPTONAZURE, const.LINKAI, const.BAIDU, const.XUNFEI, const.QWEN, const.QWEN_DASHSCOPE, const.GEMINI, const.ZHIPU_AI, const.CLAUDEAPI]:
                        bot.sessions.clear_session(session_id)
                        if Bridge().chat_bots.get(bottype):
                            Bridge().chat_bots.get(bottype).sessions.clear_session(session_id)
@@ -341,7 +341,7 @@ class Godcmd(Plugin):
                            ok, result = True, "配置已重载"
                        elif cmd == "resetall":
                            if bottype in [const.OPEN_AI, const.OPENAI, const.CHATGPT, const.CHATGPTONAZURE, const.LINKAI,
-                                           const.BAIDU, const.XUNFEI, const.QWEN, const.GEMINI, const.ZHIPU_AI, const.MOONSHOT,
+                                           const.BAIDU, const.XUNFEI, const.QWEN, const.QWEN_DASHSCOPE, const.GEMINI, const.ZHIPU_AI, const.MOONSHOT,
                                           const.MODELSCOPE]:
                                channel.cancel_all_session()
                                bot.sessions.clear_all_session()
--- a/run.sh
+++ b/run.sh
@@ -271,7 +271,7 @@ select_model() {
    echo -e "${YELLOW}2) Zhipu AI (glm-5-turbo, glm-5, etc.)${NC}"
    echo -e "${YELLOW}3) Kimi (kimi-k2.5, kimi-k2, etc.)${NC}"
    echo -e "${YELLOW}4) Doubao (doubao-seed-2-0-code-preview-260215, etc.)${NC}"
-    echo -e "${YELLOW}5) Qwen (qwen3.5-plus, qwen3-max, qwq-plus, etc.)${NC}"
+    echo -e "${YELLOW}5) Qwen (qwen3.6-plus, qwen3.5-plus, qwen3-max, qwq-plus, etc.)${NC}"
    echo -e "${YELLOW}6) Claude (claude-sonnet-4-6, claude-opus-4-6, etc.)${NC}"
    echo -e "${YELLOW}7) Gemini (gemini-3.1-flash-lite-preview, gemini-3.1-pro-preview, etc.)${NC}"
    echo -e "${YELLOW}8) OpenAI GPT (gpt-5.4, gpt-5.2, gpt-4.1, etc.)${NC}"
@@ -318,7 +318,7 @@ configure_model() {
        2) read_model_config "Zhipu AI" "glm-5-turbo" "ZHIPU_KEY" ;;
        3) read_model_config "Kimi (Moonshot)" "kimi-k2.5" "MOONSHOT_KEY" ;;
        4) read_model_config "Doubao (Volcengine Ark)" "doubao-seed-2-0-code-preview-260215" "ARK_KEY" ;;
-        5) read_model_config "Qwen (DashScope)" "qwen3.5-plus" "DASHSCOPE_KEY" ;;
+        5) read_model_config "Qwen (DashScope)" "qwen3.6-plus" "DASHSCOPE_KEY" ;;
        6)
            read_model_config "Claude" "claude-sonnet-4-6" "CLAUDE_KEY"
            read_api_base "CLAUDE_BASE" "https://api.anthropic.com/v1"
--- a/scripts/run.ps1
+++ b/scripts/run.ps1
@@ -154,7 +154,7 @@ $ModelChoices = @{
    "2" = @{ Provider = "Zhipu AI";                 Default = "glm-5-turbo";                            Key = "ZHIPU_KEY" }
    "3" = @{ Provider = "Kimi (Moonshot)";          Default = "kimi-k2.5";                              Key = "MOONSHOT_KEY" }
    "4" = @{ Provider = "Doubao (Volcengine Ark)";  Default = "doubao-seed-2-0-code-preview-260215";    Key = "ARK_KEY" }
-    "5" = @{ Provider = "Qwen (DashScope)";         Default = "qwen3.5-plus";                           Key = "DASHSCOPE_KEY" }
+    "5" = @{ Provider = "Qwen (DashScope)";         Default = "qwen3.6-plus";                           Key = "DASHSCOPE_KEY" }
    "6" = @{ Provider = "Claude";                   Default = "claude-sonnet-4-6";                      Key = "CLAUDE_KEY";  Base = "https://api.anthropic.com/v1" }
    "7" = @{ Provider = "Gemini";                   Default = "gemini-3.1-pro-preview";                 Key = "GEMINI_KEY";  Base = "https://generativelanguage.googleapis.com" }
    "8" = @{ Provider = "OpenAI GPT";               Default = "gpt-5.4";                                Key = "OPENAI_KEY";  Base = "https://api.openai.com/v1" }
@@ -169,7 +169,7 @@ function Select-Model {
    Write-Host "2) Zhipu AI (glm-5-turbo, glm-5, etc.)"
    Write-Host "3) Kimi (kimi-k2.5, kimi-k2, etc.)"
    Write-Host "4) Doubao (doubao-seed-2-0-code-preview-260215, etc.)"
-    Write-Host "5) Qwen (qwen3.5-plus, qwen3-max, qwq-plus, etc.)"
+    Write-Host "5) Qwen (qwen3.6-plus, qwen3.5-plus, qwen3-max, qwq-plus, etc.)"
    Write-Host "6) Claude (claude-sonnet-4-6, claude-opus-4-6, etc.)"
    Write-Host "7) Gemini (gemini-3.1-flash-lite-preview, gemini-3.1-pro-preview, etc.)"
    Write-Host "8) OpenAI GPT (gpt-5.4, gpt-5.2, gpt-4.1, etc.)"