CLASSIC
Containers
Mistral-Small-3.2-24B-Instruct-2506

Mistral-Small-3.2-24B-Instruct-2506

Associated Products
Features
Description
Mistral Small 3.2 24B adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance.
Publisher
Mistral AI
Latest Tag
1
Modified
August 7, 2025
Compressed Size
11.86 GB
Multinode Support
No
Multi-Arch Support
No
1 (Latest) Security Scan Results

Linux / amd64

Sorry, your browser does not support inline SVG.

Mistral Small 3.2 24B Container Overview

Description:

The Mistral-Small-3.2-24B NIM houses the Mistral-Small-3.2-24B-Instruct-2506 model and delivers a multimodal VLM designed for efficient image understanding and reasoning tasks. Mistral-Small-3.2-24B-Instruct-2506 is a minor update of Mistral-Small-3.1-24B-Instruct-2503.

Small-3.2 improves in the following categories:

  • Instruction following: Small-3.2 is better at following precise instructions
  • Repetition errors: Small-3.2 produces less infinite generations or repetitive answers
  • Function calling: Small-3.2's function calling template is more robust (see here)

In all other categories Small-3.2 should match or slightly improve compared to Mistral-Small-3.1-24B-Instruct-2503.

This model is ready for commercial/non-commercial use.

License/Terms of Use:

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of this model is governed by the NVIDIA Community Model License. Additional Information: Apache 2.0 License.

Deployment Geography:

Global

Release Date:

Build.Nvidia.com 05/2025 via https://build.nvidia.com/mistralai/mistral-small-24b-instruct

Mistral Small 3.2 24B:

The Mistral Small 3.2 24B 3 Container references the following model:

Model Name & Link Use Case
Mistral Small 3.2 24B Mistral Small 3.2 24B adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance

Deployment Details:

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

For information on how to deploy this NIM, please visit - Get started

Container Version(s):

nvcr.io/nim/mistralai/mistral-small-3.2-24b-instruct-2506:latest

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

Please report security vulnerabilities or NVIDIA AI Concerns here.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.