Whisper large-v3
Whisper large-v3 is OpenAI’s ASR model for transcription and translation across many languages, with strong robustness to accents and noise.
Provider
OpenAI
Model family
OpenAI Whisper
Speech-to-text
Cost tier
Large
Status
Current
Why teams choose it
Broad capability envelope
Useful when the same stack must cover chat, multimodal inputs, tooling, or structured-output shapes without juggling many SKUs.
Long-context analysis
Helps teams summarize, compare, and extract insights from long documents without losing important nuance.
Coding and tools
Works well for code assistance, tool calling, and agent workflows where instructions must stay consistent across steps.
Cost-efficient routing
Useful as part of a routing stack where cheap models handle drafts and confirmations and this tier handles genuinely hard passages.
Tradeoffs to know
- Hallucinations still occur on silence or music—add VAD and confidence thresholds.
- Throughput scales with hardware—plan GPU pools for peak hours.
Technical specs
- Inputs
- audio
- Outputs
- text
- Capabilities
- transcription, translation, timestamps
- License
- MIT
Benchmarks
No independent or vendor benchmark scores in this catalog entry.
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Whisper large-v3 FAQ
What is Whisper large-v3?
Whisper large-v3 is OpenAI’s ASR model for transcription and translation across many languages, with strong robustness to accents and noise. It is commonly self-hosted or used via API partners; latency depends heavily on hardware and chunking strategy.
When does Whisper large-v3 fit best?
Call-center transcription with human review
What should teams watch out for with Whisper large-v3?
Hallucinations still occur on silence or music—add VAD and confidence thresholds.
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