Whisper Large v3 Turbo

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Overview

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Specs

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Pricing

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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.

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Pick something else when

• You need to know the mood or background sound, not just the words: use gemini-3-flash-audio.
• Your file is longer than ~30 minutes — split the audio first, or wait for the upcoming Files API path.

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Example

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"

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Python (OpenAI SDK)

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}")

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Aliases that resolve here

• transcribe — auto-tracks the current best ASR model on Kyma. Today that’s this SKU.

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See also

• Audio - full audio family overview
• POST /v1/audio/transcriptions - endpoint reference
• watch-cli - open-source CLI built on this endpoint