Overview

Tongyi Qwen, developed by Alibaba Cloud, is a sophisticated series of LLMs. It includes multiple variants, such as Qwen for text processing, Qwen-VL for vision-language tasks, and Qwen-Audio for audio understanding. The models are notable for their impressive scale, with the flagship Qwen-72B model featuring 72 billion parameters and trained on over 3 trillion tokens.

Configure

After installation, you need to get API keys from Alibaba Cloud and setup in Settings -> Model Provider.

Speech-to-text runtime note

Tongyi speech-to-text patches DashScope's async-to-sync websocket bridge when it runs under Dify's gevent-based plugin runtime. This avoids sustained high CPU after STT requests while keeping the normal in-process recognition path.

If you need stronger isolation for STT recognition, set in the plugin daemon environment.
can be used to tune the subprocess timeout.

Request metadata

is optional and disabled by default. Turning it on attaches and to each Tongyi request as custom HTTP headers, so usage can be attributed to a specific Dify app in the DashScope server logs. The opt-in is composed with the existing bury-point header (which carries market analytics) in a single merged headers dict before the call to or . The Dify session lookup is best-effort: if the session context is not initialized, no Dify headers are attached and only the bury-point header is sent.