by MistralReleased 31 October 2025
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
You pay for what each request uses. No subscription, no minimum.
One page, the ordinary use
Every rate
| Meter | ↑ you send | ↓ comes back |
|---|---|---|
| Text tokens | 33,28 DA |
per 1M tokens
One company runs this model. OpenDunes sends every request to it. DA per 1M tokens: ↑ what you send.
| Provider | ↑ text | Latency | Uptime |
|---|---|---|---|
| Mistral · Zdr | 33,28 DA | … | … |
| MistralEurope | 36,61 DA | … | … |
You pay the price of the provider that served your request. Prices read 15:19 · 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"],
)
resp = client.embeddings.create(
model="mistralai/mistral-embed-2312",
input="Delivery to Oran takes two days.",
)
print(resp.data[0].embedding[:5])Runs this request once on your balance.
About 0,033 DA per page. ↑ 33,28 DA per 1M tokens.
No. You pay per use from your balance, with no subscription. Run it once from Call it in code, at the same price.
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.
Deposit dinars with your Edahabia or CIB card. Every model, Mistral Embed 2312 included, is paid from that balance. No foreign card, no conversion.
Mistral names no languages for Mistral Embed 2312. Arabic, French and Darja: not stated.
Up to 8 192 tokens. Split anything longer.
1 024
Mistral. Released 31 October 2025.
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
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
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.
Open weights No.
Read on docs.mistral.ai.
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