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 models
Speech-to-text
Cost tier
Open / entry
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.
When not to use this
- Self-hosting outcomes depend on hardware, quantization, and ops maturity—budget time beyond swapping an API hostname.
- May demand more instrumentation than SaaS-managed APIs to duplicate latency, failover, and support guarantees.
- Benchmark prompts and regressions continuously before rewriting entire routing tables around weights.
Technical specs
- Inputs
- audio
- Outputs
- text
- Capabilities
- transcription, translation, timestamps
- License
- MIT
- Model string
whisper-large-v3
Benchmarks
No benchmark data yet.
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