OpenAI Whisper large-v3-turbo served on Groq's LPU hardware — very fast, very low cost batch transcription with word-level timestamps and automatic language detection.
12 languages · benchmarked
Whisper Large V3 Turbo is a speech-to-text model from Groq. In our standardized benchmarks it reaches 14.31% word error rate, ranking #10 of 34 models tested. It supports 12 languages and runs at $0.001/min — the cheapest in the catalog.
Its nearest benchmarked alternative is Soniox STT (Async). Best suited for high-volume, cost-sensitive pipelines.
Overall Score
81.3
#10 of 34
Word Error Rate
14.31%
Character Error Rate
10.63%
Match Error Rate
13.44%
Word Info Lost
19.01%
Avg Latency
3.4s
Benchmarks Run
33
last 30 runs
Word error rate
14.31%
↓ 22.2% · 30d
3 runs ago
latest
Avg latency
3.4s
↑ 32.3% · 30d
3 runs ago
latest
overall WER · 34 models
Whisper Large V3 Turbo
field · 33 models
lower-left is better
8 categories
WER
lower is better
Medical
1.8%
Legal
3.8%
Finance
7.1%
Technical
8.3%
General
9.2%
Conversational
13.7%
Noisy Environment
36.7%
Code-Switching
49.9%
| Category | WER | CER | MER | WIL | Latency | Benchmarks |
|---|---|---|---|---|---|---|
Code-Switching | 49.85% | 31.17% | 47.07% | 63.66% | 5.1s | 2 |
Conversational | 13.65% | 8.79% | 13.27% | 15.77% | 3.7s | 2 |
Finance | 7.11% | 3.35% | 7.03% | 10.92% | 4.3s | 2 |
General | 9.21% | 7.29% | 9.03% | 14.31% | 3.3s | 17 |
Legal | 3.83% | 2.33% | 3.78% | 4.78% | 3.8s | 2 |
Medical | 1.78% | 0.86% | 1.78% | 2.58% | 3.1s | 2 |
Noisy Environment | 36.67% | 30.58% | 31.97% | 42.24% | 2.0s | 4 |
Technical | 8.27% | 5.83% | 8.24% | 9.85% | 3.8s | 2 |
5 accents
| Accent | WER | Benchmarks |
|---|---|---|
African | 24.62% | 1 |
Australian | 16.92% | 1 |
Indian | 26.98% | 1 |
British | 5.97% | 1 |
American | 9.43% | 1 |
Rate
$0.0007
/min
Per-second billing. Bring your own provider key and pay your provider directly — a 5% routing fee applies (first 100 min/mo free).
Set up BYOKCost estimator
1 hour
$0.04
10 hours
$0.40
100 hours
$3.96
1,000 hours
$39.60
Billed per second of audio processed.
Model ID
groq/whisper-large-v3-turbo
Authenticate every request with your secret API key as a Bearer token. Issue a key from your dashboard.
Use it with your agent
Paste this into Claude Code, Cursor, Codex or any agent with a shell. It installs the CLI and the OpenTranscription skill, then transcribes with this model.
Quickstart
Upload your audio, create a job with this model, then poll the job or set a webhook_url to be notified when it completes.
Key parameters
file_path
string
required
Storage path returned by the upload step.
model
string
required
The model to run this job on.
models
string[]
Ordered fallback chain: primary first, then backups tried in order if a provider fails.
language
string
ISO 639-1 language code. Omit to auto-detect.
word_timestamps
boolean
Per-word start, end and confidence. Defaults to true; send false to leave them out.
webhook_url
string
HTTPS URL notified with the result when the job completes. Delivered events are signed (X-OT-Signature).
title
string
Display name for this job in the dashboard. Falls back to the file name.
Response
A completed job returns the transcript in the OpenTranscription Unified Schema (OTUS).
Webhooks — skip polling
We POST a signed event to your webhook_url when a job completes or fails; verify the X-OT-Signature (HMAC-SHA256, reject if older than 300 s) and dedupe on the event id, then fetch the full transcript via GET /api/v1/transcriptions/{id}.
Rate-limited per tier — see the X-RateLimit-* response headers.
Full API referencefrom the same field
same benchmark, every match
Supported Languages
Supported Formats
Features
Limits
Max file size
100 MB
Max duration
No documented limit