Whisper Large V3
OpenAI's Whisper Large V 3 is a state-of-the-art audio model for automatic speech recognition and speech translation, trained on over 5 million hours of labeled data. It is genuinely best at generalizing to many datasets and domains in a zero-shot setting, demonstrating improved performance over a wide variety of languages with a 10% to 20% reduction of errors compared to its predecessor.
Input
Output
Context
0K
Max Output
2K
Parameters
1.5B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Resources & Links
Estimated GPU requirements for Whisper Large V3
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 Whisper Large V3
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Whisper Large V3 advertises a context window of 1 tokens, with a maximum output of 1,500 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Whisper Large V3 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.
Whisper Large V3 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 Whisper Large V3 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.
Not on the managed pool today; Whisper Large V3 is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 2.1 GB at INT8 for the weights and a default context, from the catalog's 1.5B 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, host APIs and benchmark publishers, each tagged with its source.
Last updated: Sep 13, 2026
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