> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Whisper Large v3 Turbo

> OpenAI Whisper Large v3 Turbo on Kyma. Speech-to-text at 228x realtime, $0.0009/min, OpenAI Whisper API compatible.

## Overview

`whisper-v3-turbo` is OpenAI's Whisper Large v3 Turbo — the fast variant of Whisper Large v3, distilled to skip half the decoder layers without losing meaningful accuracy. On Kyma it serves both the `transcribe` alias and the `whisper-v3-turbo` SKU at 228x realtime inference speed.

Right pick for transcripts, voice-agent input, podcast captions, and any pipeline where speed and cost matter more than the last 1% of WER.

## Specs

| Field             | Value                                              |
| ----------------- | -------------------------------------------------- |
| Model ID          | `whisper-v3-turbo`                                 |
| Creator           | OpenAI                                             |
| License           | MIT (model weights)                                |
| Best for          | Transcription, captions, voice agents              |
| Max file size     | 25 MB                                              |
| Max duration      | \~30 min mono 16kHz mp3                            |
| Input modalities  | Audio (`mp3`, `wav`, `m4a`, `ogg`, `webm`, `flac`) |
| Output modalities | Text                                               |
| Pricing mode      | Per minute                                         |
| Min billable      | 1 minute (rounded up)                              |

## Pricing

|               |              Cost             |
| ------------- | :---------------------------: |
| Per minute    |            \$0.0009           |
| 1-hour file   |            \$0.054            |
| 5-second clip | \$0.0009 (rounds up to 1 min) |

## Use this when

* You need accurate transcripts at \~50x cheaper than full multimodal LLM analysis.
* You're feeding a voice agent or building real-time captions where end-to-end latency matters.
* You want the OpenAI Whisper API shape with no code changes — just swap the `base_url`.

## Pick something else when

* You need to know the *mood* or *background sound*, not just the words: use [`gemini-3-flash-audio`](/models/gemini-3-flash-audio).
* Your file is longer than \~30 minutes — split the audio first, or wait for the upcoming Files API path.

## Example

```bash theme={null}
curl -X POST https://kymaapi.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $KYMA_API_KEY" \
  -F "file=@meeting.mp3" \
  -F "model=whisper-v3-turbo" \
  -F "response_format=verbose_json"
```

Response includes the full transcript, per-segment timestamps, and detected language. See [endpoint reference](/api-reference/audio-transcriptions) for all parameters.

### Python (OpenAI SDK)

```python theme={null}
from openai import OpenAI

client = OpenAI(
    base_url="https://kymaapi.com/v1",
    api_key="kyma-...",
)

with open("meeting.mp3", "rb") as f:
    result = client.audio.transcriptions.create(
        model="whisper-v3-turbo",
        file=f,
        response_format="verbose_json",
    )

print(result.text)
for segment in result.segments:
    print(f"[{segment.start:.2f}-{segment.end:.2f}] {segment.text}")
```

## Aliases that resolve here

* `transcribe` — auto-tracks the current best ASR model on Kyma. Today that's this SKU.

If you want stable behavior across alias changes, pin `whisper-v3-turbo` directly. If you want to ride future upgrades automatically, use `transcribe`.

## See also

* [Audio](/models/audio) - full audio family overview
* [`POST /v1/audio/transcriptions`](/api-reference/audio-transcriptions) - endpoint reference
* [`watch-cli`](https://github.com/sonpiaz/watch-cli) - open-source CLI built on this endpoint