Magistral Medium 1.1
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Mistral AI
- Organisation type
- Industry
- Country
- France
- Published
- 10 June 2025
- Authors
- Abhinav Rastogi, Albert Q. Jiang, Andy Lo, Gabrielle Berrada, Guillaume Lample, Jason Rute, Joep Barmentlo, Karmesh Yadav, Kartik Khandelwal, Khyathi Raghavi Chandu, Léonard Blier, Lucile Saulnier, Matthieu Dinot, Maxime Darrin, Neha Gupta, Roman Soletskyi, Sagar Vaze, Teven LeScao, Yihan Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation
- Base model
- Mistral Medium 3
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
You can try out a preview version of Magistral Medium in Le Chat or via API on La Plateforme. Magistral Medium is also available on Amazon SageMaker, and soon on IBM WatsonX, Azure AI and Google Cloud Marketplace.
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
Sources
Where this record came from and when it was last checked.
- Reference
- Announcing Magistral — the first reasoning model by Mistral AI — excelling in domain-specific, transparent, and multilingual reasoning.
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Magistral Medium 1.1 was published by Mistral AI, in France, in June 2025. It comes out of industry.
It works in Language, and is recorded as doing language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation.
It builds on Mistral Medium 3, which is why it shares that model's general shape and size.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Magistral Medium 1.1 — common questions
How many parameters does Magistral Medium 1.1 have?
No parameter count has been published for Magistral Medium 1.1, which is why no memory or speed figure appears on this page.
Who created Magistral Medium 1.1?
Magistral Medium 1.1 was published by Mistral AI, based in France, categorised as industry.
When was Magistral Medium 1.1 released?
Magistral Medium 1.1 was published in June 2025.
What is Magistral Medium 1.1 used for?
Magistral Medium 1.1 works in Language, and is recorded as handling language modeling/generation, Question answering, Quantitative reasoning, Code generation, Translation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Magistral Medium 1.1?
None. Magistral Medium 1.1 is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Magistral Medium 1.1 open source?
No. Magistral Medium 1.1 has not had its weights published, so it exists only as a service controlled by its owner.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.