Turn this customer email into JSON with intent, urgency and order_id.
customer-email.txtDevstral 2 2512
by MistralReleased 9 December 2025
Devstral 2 is a very large coding model for automated work across a whole code project: it explores files, changes several at once while keeping the overall design in mind, notices when a run fails and retries with a fix. Typical jobs are bug fixing and updating legacy systems. It reads about 262 pages, can use tools and fill in an exact form you give it, and comes under a modified MIT licence.
What it costs
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 | 133,13 DA | 665,67 DA |
| Cached tokens read | 13,31 DA |
per 1M tokens
- When you pay
- After the answer, from your balance. Nothing is held.
- Why it varies
- A longer question or a longer answer costs more.
- After each one
- The exact charge shows on the request.
Where your request runs
One company runs this model. OpenDunes sends every request to it. DA per 1M tokens: ↑ what you send · ↓ what comes back.
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.
See it in action
Real requests, real replies. Try any of them yourself — one click, pre-filled.
Two lines to welcome a shop in Oran.
Welcome to our new shop in Oran — fresh bread every morning, and coffee the way the city likes it. Come in, the first cup is on us.
Try in Chat LabTry your own request — same model, pre-selected.
Open Chat LabCall it in code
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.chat.completions.create(
model="mistralai/devstral-2512",
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.
Parameters you can send
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_penaltyStructured output
Get JSON you can parse instead of prose.
response_formatstructured_outputsTools
Let the model call your functions.
toolstool_choiceQuestions about Devstral 2 2512
How much does Devstral 2 2512 cost in dinars?
About 0,47 DA per message. ↑ 133,13 DA · ↓ 665,67 DA per 1M tokens.
Is Devstral 2 2512 free? Can I try it?
No. You pay per use from your balance, with no subscription. Try it in Chat at the same price.
What is Devstral 2 2512 best for?
Devstral 2 is a very large coding model for automated work across a whole code project: it explores files, changes several at once while keeping the overall design in mind, notices when a run fails and retries with a fix. Typical jobs are bug fixing and updating legacy systems. It reads about 262 pages, can use tools and fill in an exact form you give it, and comes under a modified MIT licence.
How do I pay for Devstral 2 2512 without an international card?
Deposit dinars with your Edahabia or CIB card. Every model, Devstral 2 2512 included, is paid from that balance. No foreign card, no conversion.
Does Devstral 2 2512 work in Arabic, French or Darja?
Mistral names no languages for Devstral 2 2512. Arabic, French and Darja: not stated.
Can it read PDFs, images or a whole book?
It reads text and PDFs. Up to 262K tokens in one request, about 260 pages. It writes up to 157 000 words at once.
Is Devstral 2 2512 open source?
Yes, its weights are public. Open weights on Hugging Face
Who makes Devstral 2 2512, and when was it released?
Mistral. Released 9 December 2025.
Other Mistral models, priced in dinars
See all Mistral modelsMistral'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