by MistralReleased 30 April 2026
Mistral Medium 3.5 is a large model taking text, pictures and files and answering in text. It is the Medium line's pick for automated work: it uses several tools in a row reliably, stays on long tasks, and lets you set thinking effort per request. Its picture reading copes with any size or shape of image, and you can download it to run yourself.
You pay for what each request uses. No subscription, no minimum.
One message, the ordinary use
Every rate
| Meter | ↑ you send | ↓ comes back |
|---|---|---|
| Text tokens | 499,25 DA | 2 496,27 DA |
per 1M tokens
One company runs this model. OpenDunes sends every request to it. DA per 1M tokens: ↑ what you send · ↓ what comes back.
| Provider | ↑ Sent | ↓ Back | Latency | Throughput | Uptime |
|---|---|---|---|---|---|
| Mistral · Zdr | 499,25 DA | 2 496,27 DA | … | … | … |
| MistralEurope | 549,18 DA | 2 745,89 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.
Real requests, real replies. Try any of them yourself — one click, pre-filled.
What's the total on this invoice, and when is it due?
invoice-scan.jpg · photoThe total is 42 300,00 DA, due 5 September 2026. Worth a double-check: the handwritten day could read as 8 instead of 3.
Try in Chat LabTurn this customer email into JSON with intent, urgency and order_id.
customer-email.txtTry your own request — same model, pre-selected.
Open Chat LabSwap 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.chat.completions.create(
model="mistralai/mistral-medium-3-5",
messages=[{"role": "user", "content": "Two lines to welcome a shop in Oran."}],
)
print(resp.choices[0].message.content)Runs this request once on your balance.
Standard OpenAI fields, grouped by what they control. Anything not listed is accepted and ignored.
Shaping the reply
How long, how varied, where to stop.
max_tokenstemperaturetop_pstopseedfrequency_penaltypresence_penaltyReasoning
Let it think before answering — and see the thinking.
reasoningreasoning_effortinclude_reasoningStructured output
Get JSON you can parse instead of prose.
response_formatstructured_outputsTools
Let the model call your functions.
toolstool_choiceAbout 1,75 DA per message. ↑ 499,25 DA · ↓ 2 496,27 DA per 1M tokens.
No. You pay per use from your balance, with no subscription. Try it in Chat at the same price.
Mistral Medium 3.5 is a large model taking text, pictures and files and answering in text. It is the Medium line's pick for automated work: it uses several tools in a row reliably, stays on long tasks, and lets you set thinking effort per request. Its picture reading copes with any size or shape of image, and you can download it to run yourself.
Mistral Medium 3.1 costs 1,28 DA less for the same message.
Deposit dinars with your Edahabia or CIB card. Every model, Mistral Medium 3.5 included, is paid from that balance. No foreign card, no conversion.
Mistral says it supports dozens of languages, naming French and Arabic among them. Darja is not named.
It reads text, images and PDFs. Up to 262K tokens in one request, about 260 pages. It writes up to 157 000 words at once.
Yes, its weights are public.
Mistral. Released 30 April 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
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
From what its maker and two benchmark sites publish, and where it stands on OpenDunes. Read 7 Nov 2026.
Thinks first
Works through the problem before it answers.
Uses your tools
Can call functions your code provides.
Fills your JSON
Answers in the JSON shape you give it.
Searches the web No.
Can look things up online while it answers.
Cheaper on repeats No.
A prompt it saw a moment ago costs less the second time.
Open weights
Read on docs.mistral.ai.
Run a coding agent that edits several files and runs tests
Uses your tools
Coordinate an agent that calls five or more of your tools
Uses your tools · Thinks first
Read a long contract bundle and answer in French
Files