Qwen: Qwen3 ASR 1.7B

Qwen3 ASR 1.7B is an automatic speech recognition model from Qwen, listed on api14 under the model id qwen/qwen3-asr-1.7b. It sits in the transcription category, and its name reflects a 1.7B parameter scale. If you are already working with other Qwen models, this one comes from the same provider and follows the same general family conventions, which can simplify evaluation and integration.

The model takes audio as its input and returns transcription as its output. It is multilingual: it supports language identification and transcription across 30 languages and 22 Chinese dialects. This means you do not necessarily need to tell it which language is being spoken before you send audio, since language identification is part of what it does. It supports both streaming and offline inference, so it can be used in real time as audio arrives or against complete recordings. The facts provided do not list a context length, and supported parameters are not specified.

Practical uses follow directly from those capabilities. Because it handles many languages and Chinese dialects, it fits applications that receive mixed or unknown language audio, such as call recordings, customer support archives, or user generated media where the spoken language varies. Language identification output is useful for routing transcripts to the right downstream process or reviewer. Streaming inference suits live captioning, voice interfaces, and any workflow where waiting for a full file to finish is not acceptable. Offline inference suits batch transcription of stored audio, for example building searchable archives or generating subtitles after a recording is complete.

You call the model through api14's OpenAI-compatible API using the model id qwen/qwen3-asr-1.7b. Because the interface is OpenAI-compatible, you can use existing OpenAI client libraries and point them at the api14 base URL, sending audio in the request and reading the transcription from the response. The model page shows a live price table for current rates. The facts do not list any supported request parameters for this model, so check the model page or API reference for what you can pass beyond the model id and the audio input.

There are a few things to keep in mind when deciding. No context length is published for this model, and no supported parameters are listed, so if your application depends on tuning generation settings you may need to confirm what is actually accepted before committing. The output modality is transcription only, which means it will not translate, summarize, or answer questions about the audio. If you need those behaviors, a general purpose language model that accepts audio input would fit better, with this model handling the raw transcription step.