Whisper API

deepgram/whisper-large
Whisper: Multilingual speech recognition model, robust, versatile, open-source.
Output
$0.000104 / sec
Released
Sep 21, 2022

Overview

What is Whisper Large API?

Whisper: Multilingual speech recognition model, robust, versatile, open-source.

Technical Specifications
- Supported inputs: Audio
- Output: Text

Performance Benchmarks
[embed]

Whisper Large API Pricing
- Pricing on request

Where to Use Whisper Large

Transcription
Convert calls, meetings, and media into accurate, searchable text.

Captioning and indexing
Generate subtitles and make audio archives searchable.

Uptime and performance

  • End-to-end latency (24h median): 310.5s

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

  • Transcription of meetings, interviews, and conference calls
  • Voice search and voice command systems
  • Multilingual speech recognition and translation apps
  • Customer support call transcription and analysis
  • Legal and medical transcription services
  • Podcast and audio content indexing
  • Market research from customer call analysis
  • Accessible voice interfaces and subtitles generation

‍

How to use Whisper API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to deepgram/whisper-large.
import requests, time

headers = {"Authorization": "Bearer " + AIMLAPI_KEY}
job = requests.post(
    "https://api.aimlapi.com/v1/stt/create",
    headers=headers,
    json={
      "model": "deepgram/whisper-large",
      "url": "https://example.com/audio.mp3"
    },
).json()
gid = job["generation_id"]

while True:
    res = requests.get(f"https://api.aimlapi.com/v1/stt/{gid}", headers=headers).json()
    if res.get("status") in ("completed", "error", "failed"):
        break
    time.sleep(3)
print(res)
const headers = {
  Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
  "Content-Type": "application/json",
};
const job = await (await fetch("https://api.aimlapi.com/v1/stt/create", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "model": "deepgram/whisper-large",
    "url": "https://example.com/audio.mp3"
  }),
})).json();

let res;
do {
  await new Promise((r) => setTimeout(r, 3000));
  res = await (await fetch(`https://api.aimlapi.com/v1/stt/${job.generation_id}`, { headers })).json();
} while (!["completed", "error", "failed"].includes(res.status));
console.log(res);
# submit the job — the response contains "generation_id"
curl -X POST https://api.aimlapi.com/v1/stt/create \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepgram/whisper-large","url":"https://example.com/audio.mp3"}'

# then poll for the result until it is ready
curl "https://api.aimlapi.com/v1/stt/{generation_id}" -H "Authorization: Bearer $AIMLAPI_KEY"

OpenAI-compatible — swap the base URL and it works with your existing SDK.

Whisper API Pricing

TypePrice
Output
$0.000104 / sec

Whisper vs other models

ModelInputOutputContextBest for
Whisper
This page
$0.000104 / sec
Transcription
$130 / 1M characters
$130 / 1M
Speech synthesis
$130 / 1M characters
$0.13 / 1M tokens
Speech synthesis
$78 / 1M characters
$0.078 / 1M tokens
Speech synthesis
$13 / 1M characters
$0.013 / 1M tokens
Speech synthesis

Frequently asked questions

Whisper takes audio as input and returns text.

Whisper became available on September 21, 2022.

Whisper is priced at output $0.000104 / sec.

Whisper was built by OpenAI.

Send a request to v1/stt with deepgram/whisper-large as the model id.

Yes. Whisper is served through AI/ML API, so the same key and endpoint format used for other models applies.

Start building with Whisper

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