Qwen3-ASR vs OpenAI Whisper (API)

On the heaviest attribute, batch price per audio minute, Qwen3-ASR at 0.002 per minute is one-third the cost of OpenAI Whisper at 0.006 per minute. At high call-center volumes this gap is decisive. On speaker diarizationdiarization - Splitting a transcript by speaker, so you can see who said what. and PII redaction, both tools are equal: neither supports either feature, so no advantage shifts the outcome. On concurrency, OpenAI Whisper has a documented tiered structure reaching 10,000 RPM, while Qwen3-ASR publishes no equivalent figure, which slightly favors Whisper. On sentiment analysis, neither tool offers it. The cost advantage for Qwen3-ASR on the top-weighted attribute outweighs Whisper's concurrency documentation edge, but the diarization and PII parity on both sides keeps the margin narrow.

On batch pricing, Qwen3-ASR charges 0.002 dollars per audio minute versus 0.006 for OpenAI Whisper, making it 3x cheaper, a decisive advantage at the heaviest weight. On websocket streaming, Qwen3-ASR supports it while Whisper does not, which is a meaningful advantage for real-time product integrations. Both tools tie on word-level timestampsword-level timestamps - The API reports exactly when each word is spoken in the audio, for subtitles and highlighting.. Whisper leads on SDK breadth, covering Python, JS/TS,.NET, Ruby, Java, and Go versus Qwen3-ASR's Python and DashScope API, and Whisper also lists nine supported audio formats explicitly while Qwen3-ASR does not publish a comparable format list. Balancing all five attributes, Qwen3-ASR's strong cost advantage and streaming support outweigh Whisper's SDK and format breadth.

For dictation on your own computer, the most important attributes are platform support, local versus cloud processing, AI formatting, one-time versus subscription pricing, and app integrations. Neither tool has published information covering platform compatibility, AI formatting or editing features, pricing model, or app integrations. On local processing, Qwen3-ASR supports self-hostingself-host - Run the software on your own servers instead of the vendor’s cloud. on an NVIDIA GPU while OpenAI Whisper API does not, which favors Qwen3-ASR on that attribute. However, the remaining four attributes collectively carry more weight, and no supporting information exists for either tool on those points. A winner cannot be named on one attribute alone when the more decisive attributes remain unresolved.

For clinical transcription under US healthcare privacy law, a HIPAA BAAHIPAA BAA - A signed Business Associate Agreement that lets you handle protected health information under US healthcare privacy law. is the absolute requirement. OpenAI Whisper API offers a verified HIPAA BAA, while no such fact exists for Qwen3-ASR. On the next most important attributes, both tools lack PII redaction, but OpenAI Whisper API supports custom vocabulary and keyterm boosting, which is critical for medical terminology accuracy, and Qwen3-ASR has no verified equivalent. OpenAI Whisper API also holds SOC 2 Type II certification. Qwen3-ASR wins on self-hosting, but that lower-weighted attribute cannot overcome the decisive gaps on HIPAA BAA and custom vocabulary.

Neither tool supports speaker diarizationdiarization - Splitting a transcript by speaker, so you can see who said what., which carries the heaviest weight for this use case. Neither offers a summarization endpoint either, so both tools are equal on the two most decisive attributes. On languages supported, OpenAI Whisper covers 57 versus Qwen3-ASR's 52, a minor advantage. On file size handling, Qwen3-ASR supports long audio via toolkit chunking while OpenAI Whisper caps uploads at 25 MB per request, giving Qwen3-ASR a small edge. Both provide word-level timestamps. These minor differences point in opposite directions, leaving the tools genuinely balanced on what matters most for meeting transcription.

For self-hostedself-host - Run the software on your own servers instead of the vendor’s cloud. deployment, Qwen3-ASR leads on every attribute that matters. It explicitly supports self-hosting on-prem, while OpenAI Whisper API offers no self-host option at all. Qwen3-ASR carries an Apache-2.0 license, a permissive open-weights license suitable for commercial self-hosting, versus the Whisper API which provides no on-prem weights access. Hardware requirements are concrete: an NVIDIA GPU via vLLM or Transformers. The project is actively maintained, released in early 2026. Model size of 0.6B to 1.7B parameters means modest hardware demands. OpenAI Whisper API fails the self-host test entirely, making this a decisive outcome.

For voice agents, a native WebSocket streaming API is essential for real-time transcription. Qwen3-ASR offers a WebSocket streaming API while OpenAI Whisper API does not support WebSocket streaming at all. On streaming price, Qwen3-ASR charges 0.005 dollars per audio minute versus no comparable streaming tier for Whisper, which lacks the capability entirely. Whisper does hold advantages on third-party WERWER - Word error rate - the share of words a transcription gets wrong; lower is more accurate. (4.06% vs 5.8%) and concurrency scaling up to 10,000 RPM at higher tiers, but these benefits are secondary when the fundamental streaming mechanism required for a voice bot is absent. The inability to stream via WebSocket is a disqualifying gap for this use case.