OpenAI: Whisper Large V3 Turbo

Whisper Large V3 Turbo is a speech recognition model from OpenAI, and it sits in the Whisper family as an optimized version of Whisper Large V3. The model is built specifically for transcription, and its stated design goals are speed and cost efficiency relative to the larger V3 model it derives from. If you already know the Whisper line, this variant keeps the same recognition target while trimming the compute needed to get there.

On the capability side, the model accepts audio as its input and produces a transcription as its output. It handles speech recognition across 99 or more languages, which makes it a general purpose transcriber rather than something tuned for a single locale. The provider description does not list a context window for this model, and no supported request parameters are documented, so there is no token budget or parameter surface to reason about here. What you get is audio in, text out, with broad language coverage.

That shape points to a fairly clear set of uses. Any workflow that turns recorded speech into written text is a candidate: meeting and call transcripts, subtitle or caption generation for video, notes dictated on a phone, interview recordings that need to be searchable, and voice memos that should end up as text in a pipeline. Because the model covers 99+ languages, it also fits products that serve multilingual users and cannot assume English input. The speed and efficiency focus matters most when you are transcribing at volume or want results back quickly rather than running a batch job overnight.

You call it through api14's OpenAI-compatible API using the model id "openai/whisper-large-v3-turbo". Because the interface follows the OpenAI convention, you can point an existing OpenAI client at api14 and send your audio to the transcription endpoint with that id in place of an OpenAI model name. The facts list no supported parameters for this model, so plan on sending the audio and the model id and not relying on tuning options such as temperature or prompt style. Check the live price table on the page for current rates.

As for limits, the available facts do not describe a context window, output token cap, or any parameter controls, so there is little to say about where this model stops. The one clear boundary is scope: it is a transcription model, so if your task is text generation, reasoning, translation as a text to text operation, or anything that is not speech to text, a different model will fit better. Within transcription itself, the tradeoff to weigh is the one OpenAI states, speed and cost efficiency against the larger Whisper Large V3, so if you need the full V3 behavior rather than the optimized variant, compare the two before committing.