per second audio
openai/whisper-large-v3-turbo
deepinfra/openai/whisper-large-v3-turbo
Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper "Robust Speech Recognition via Large-Scale Weak Supervision" by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.
Price
Providers
Each row is one seller. Prices are the published rate from that source. A dash means that source did not publish the amount. Sorted by a 3:1 blend of input and output.
No provider prices yet.
Monthly bill
Token models use the token counts. Image, video, and audio models use units times the flat rate.
$0.00 at list price for this workload
Every rate
| Unit | Input | Output | Flat | Confidence |
|---|---|---|---|---|
| second_audio | Not published— | Not published— | $0.000003/second audio | official |
Price history
input or flat output
Daily snapshots
| UTC date | Unit | Input | Output | Flat |
|---|---|---|---|---|
| 2026-10-08 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-09 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-10 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-11 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-12 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-13 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-14 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-15 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-16 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-17 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-18 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-19 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-20 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-21 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-22 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-23 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-24 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-25 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-26 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-27 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-28 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-29 | second_audio | Not published— | Not published— | $0.000003 |
| 2026-10-30 | second_audio | Not published— | Not published— | $0.000003 |
Alternatives
- nvidia/Nemotron-3.5-ASR-Streaming-Multilingual-0.6b$0.000003/second audio · Compare
- Qwen/Qwen3-ASR-0.6B$0.000003/second audio · Compare
- Fish Audio: S2.1 Pro Free (free)$0/1M · Compare
- HeyGen: HeyGen Voice Turbo (free)$0/1M · Compare
- NVIDIA: Nemotron 3 Nano Omni (free)$0/1M · Compare
- Thinking Machines: Inkling (free)$0/1M · Compare
Parameters (1)
stt
Call this model
OpenRouter id deepinfra/openai/whisper-large-v3-turbo. InferenceList does not send the request.
curl
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"deepinfra/openai/whisper-large-v3-turbo","messages":[{"role":"user","content":"Hello"}]}'
JavaScript
await fetch("https://openrouter.ai/api/v1/chat/completions", {
method: "POST",
headers: {
Authorization: "Bearer " + process.env.OPENROUTER_API_KEY,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "deepinfra/openai/whisper-large-v3-turbo",
messages: [{ role: "user", content: "Hello" }],
}),
});
Python
import os, json, urllib.request
req = urllib.request.Request(
"https://openrouter.ai/api/v1/chat/completions",
data=json.dumps({"model": "deepinfra/openai/whisper-large-v3-turbo", "messages": [{"role": "user", "content": "Hello"}]}).encode(),
headers={"Authorization": "Bearer " + os.environ["OPENROUTER_API_KEY"], "Content-Type": "application/json"},
)
print(urllib.request.urlopen(req).read().decode())