by MistralReleased 20 October 2026
Mistral's flagship speech-to-text model that turns audio into transcripts with word timestamps and speaker labels, with context biasing for domain-specific vocabulary
You pay for what each request uses. No subscription, no minimum.
One minute of audio, the ordinary use
Every rate
| Meter | ↑ you send | ↓ comes back |
|---|---|---|
| Second of audio | 0,024 DA |
per second of audio sent
One company runs this model. OpenDunes sends every request to it.
| Provider | Per second of audio | Latency | Uptime |
|---|---|---|---|
| Mistral · Zdr | 0,022 DA | … | … |
| MistralUnited States | 0,024 DA | … | … |
| MistralEurope | 0,024 DA | … | … |
You pay the price of the provider that served your request. Prices read 15:20 · speed over the last 30 minutes · uptime over 3 days.
Swap the base URL and the key. Your OpenAI code runs unchanged.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://opendunes.com/v1",
api_key=os.environ["OPENDUNES_API_KEY"],
)
with open("meeting.mp3", "rb") as f:
resp = client.audio.transcriptions.create(model="mistralai/voxtral-small-transcribe-3-0", file=f)
print(resp.text)Runs this request once on your balance.
About 1,46 DA per minute of audio. ↑ 0,024 DA per second of audio.
No. You pay per use from your balance, with no subscription. Try it in Chat at the same price.
Mistral's flagship speech-to-text model that turns audio into transcripts with word timestamps and speaker labels, with context biasing for domain-specific vocabulary.
Voxtral Small 24B 2507 STT costs 0,46 DA less for the same minute of audio.
Deposit dinars with your Edahabia or CIB card. Every model, Voxtral Small Transcribe 3 included, is paid from that balance. No foreign card, no conversion.
Mistral names no languages for Voxtral Small Transcribe 3. Arabic, French and Darja: not stated.
Speaker labels: yes. Word timings: yes.
Up to 25 MB as an upload. Longer audio goes in the request body.
Mistral. Released 20 October 2026.
The Voxtral Mini derivative that only transcribes, taking audio from meetings, voice notes and podcasts and returning the text through a standard transcription request
Mistral's compact speech-to-text model that turns audio into transcripts with word timestamps and speaker labels, and can be steered with context biasing to catch domain terms
Mistral AI's text embedding model from late 2023, whose 1024-dimension vectors capture how passages relate, for semantic search and RAG; Codestral Embed is the version for code
Turns written text into natural-sounding speech through a library of preset voices, taking emotion and pace tags so narration and spoken content carry the right tone and rhythm
From what its maker and two benchmark sites publish, and where it stands on OpenDunes. Read 7 Nov 2026.
Who said what
Labels each speaker in the transcript. Read in the maker's description.
Word timestamps
Gives the time each word was spoken. Read in the maker's description.
Transcribe audio into text with word-level timestamps and speaker diarization
Who said what · Word timestamps
Bias transcription toward domain-specific vocabulary supplied as context
Word timestamps