Ministral 3 3B Base 2512

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

This model is the base pre-trained version, not fine-tuned for instruction or reasoning tasks, making it ideal for custom post-training processes.
For instruction and chat based use cases, we recommend using Ministral 3 3B Instruct 2512.

The Ministral 3 family is designed for edge deployment, capable of running on a wide range of hardware. Ministral 3 3B can even be deployed locally, fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized.

Learn more in our blog post and paper.

Key Features

Ministral 3 3B consists of two main architectural components:

• 3.4B Language Model
• 0.4B Vision Encoder

The Ministral 3 3B Base model offers the following capabilities:

• Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
• Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
• Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
• Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
• Large Context Window: Supports a 256k context window.

Use Cases

Ideal for lightweight, real-time applications on edge or low-resource devices, such as:

• Image captioning
• Text classification
• Real-time efficient translation
• Data extraction
• Short content generation
• Fine-tuning and specialization
• And more...

Bringing advanced AI capabilities to edge and distributed environments for embedded systems.

Ministral 3 Family

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

Instruct

Base

Usage

The model can be used with the following frameworks;

• vllm: See here
• transformers: See here

vLLM

We recommend using this model with vLLM.

Installation

Make sure to install vllm >= 1.12.0:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.8.6.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve

Due to their size and the BF16 format of their weights Ministral-3-3B-Base-2512 and Ministral-3-8B-Base-2512 can run on a single 1xH200 GPU.

A simple launch command is:

vllm serve mistralai/Ministral-3-3B-Base-2512 \
 --tokenizer_mode mistral --config_format mistral --load_format mistral

Additional flags:

• You can set --max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
• You can set --max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.

Usage of the model

Here we assume that the model mistralai/Ministral-3-3B-Base-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.

Transformers

You can also use Ministral 3 3B Base 2512 with Transformers ! Make sure to install Transformers from its first v5 release candidate or from "main":

pip install transformers==5.0.0rc0

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

License

This model is licensed under the Apache 2.0 License.

You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.

A collection of edge models, with Base, Instruct and Reasoning variants, in 3 different sizes: 3B, 8B and 14B. All with vision capabilities. • 9 items • Updated Dec 2, 2025 • 170