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MODEL Listed

Mistral Large 2

Mistral Large 2 is Mistral AI's premier proprietary model, engineered specifically for high-reasoning tasks and complex multilingual workflows. With 123B parameters, it occupies a strategic middle ground: it delivers performance comparable to top-tier closed models while maintaining significantly higher efficiency for enterprise-scale deployment. For developers, the standout feature is its 128k context window, which allows for deep document analysis and extensive codebase reasoning without immediate memory degradation. Unlike many general-purpose models that struggle with nuanced linguistic shifts, Mistral Large 2 excels in multilingual code generation and logical reasoning across diverse languages. It is designed for seamless integration into RAG pipelines and agentic workflows where precision and instruction-following are non-negotiable. If your use case requires a robust engine for sophisticated reasoning or high-throughput multilingual processing, this model provides a highly optimized alternative to the most bloated frontier models.

Mistral AItext generation
01 / MODEL CARD

Model card

Mistral Large 2 is Mistral AI's premier proprietary model, engineered specifically for high-reasoning tasks and complex multilingual workflows. With 123B parameters, it occupies a strategic middle ground: it delivers performance comparable to top-tier closed models while maintaining significantly higher efficiency for enterprise-scale deployment. For developers, the standout feature is its 128k context window, which allows for deep document analysis and extensive codebase reasoning without immediate memory degradation. Unlike many general-purpose models that struggle with nuanced linguistic shifts, Mistral Large 2 excels in multilingual code generation and logical reasoning across diverse languages. It is designed for seamless integration into RAG pipelines and agentic workflows where precision and instruction-following are non-negotiable. If your use case requires a robust engine for sophisticated reasoning or high-throughput multilingual processing, this model provides a highly optimized alternative to the most bloated frontier models.

Model typetext generation
ProviderMistral AI
LicenseProprietary
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/mistralai/Mistral-Large-2
View model source
Version informationUse the source repository for the latest version
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03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: mistralai/Mistral-Large-2
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model mistralai/Mistral-Large-2
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model mistralai/Mistral-Large-2 README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('mistralai/Mistral-Large-2')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/mistralai/Mistral-Large-2.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/mistralai/Mistral-Large-2.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

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