QwenQwen 3 ASR 1.7B
Qwen develops the Qwen 3 ASR 1.7B model, an open-source audio model exceling at speech-to-text tasks, particularly in complex acoustic environments and challenging text patterns. It is notable for its ability to support language identification and speech recognition for 52 languages and dialects, leveraging large-scale speech training data and a strong foundation model.
Input
Output
Context
4K
Max Output
-
Parameters
2.3B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Qwen 3 ASR 1.7B
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Qwen 3 ASR 1.7B
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Qwen 3 ASR 1.7B advertises a context window of 4,096 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Qwen 3 ASR 1.7B is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Qwen 3 ASR 1.7B accepts Audio and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Qwen 3 ASR 1.7B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Yes. Qwen 3 ASR 1.7B is served on the managed pool through the OpenAI-compatible endpoint as qwen3-asr-1-7b, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 2.8 GB at INT8 for the weights and a default context, from the catalog's 2.3B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 6, 2026
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