mistralai/Ministral-3-3B-Base-2512

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

| Model Name | Type | Precision | Link | |--------------------------------|--------------------|-----------|------------------------------------------------------------------------------------------| | Ministral 3 3B Base 2512 | Base pre-trained | BF16 | Hugging Face | | Ministral 3 3B Instruct 2512 | Instruct post-trained | FP8 | Hugging Face | | Ministral 3 3B Reasoning 2512 | Reasoning capable | BF16 | Hugging Face | | Ministral 3 8B Base 2512 | Base pre-trained | BF16 | Hugging Face | | Ministral 3 8B Instruct 2512 | Instruct post-trained | FP8 | Hugging Face | | Ministral 3 8B Reasoning 2512 | Reasoning capable | BF16 | Hugging Face | | Ministral 3 14B Base 2512 | Base pre-trained | BF16 | Hugging Face | | Ministral 3 14B Instruct 2512 | Instruct post-trained | FP8 | Hugging Face | | Ministral 3 14B Reasoning 2512 | Reasoning capable | BF16 | Hugging Face |

Other formats available here.

Benchmark Results

We compare Ministral 3 to similar sized models.

Reasoning

| Model | AIME25 | AIME24 | GPQA Diamond | LiveCodeBench | |---------------------------|-------------|-------------|--------------|---------------| | Ministral 3 14B | 0.850| 0.898| 0.712 | 0.646 | | Qwen3-14B (Thinking) | 0.737 | 0.837 | 0.663 | 0.593 | | | | | | | | Ministral 3 8B | 0.787 | 0.860| 0.668 | 0.616 | | Qwen3-VL-8B-Thinking | 0.798| 0.860| 0.671 | 0.580 | | | | | | | | Ministral 3 3B | 0.721| 0.775| 0.534 | 0.548 | | Qwen3-VL-4B-Thinking | 0.697 | 0.729 | 0.601 | 0.513 |

Instruct

| Model | Arena Hard | WildBench | MATH Maj@1 | MM MTBench | |---------------------------|-------------|------------|-------------|------------------| | Ministral 3 14B | 0.551| 68.5| 0.904| 8.49 | | Qwen3 14B (Non-Thinking) | 0.427 | 65.1 | 0.870 | NOT MULTIMODAL | | Gemma3-12B-Instruct | 0.436 | 63.2 | 0.854 | 6.70 | | | | | | | | Ministral 3 8B | 0.509 | 66.8| 0.876 | 8.08 | | Qwen3-VL-8B-Instruct | 0.528| 66.3 | 0.946| 8.00 | | | | | | | | Ministral 3 3B | 0.305 | 56.8| 0.830 | 7.83 | | Qwen3-VL-4B-Instruct | 0.438| 56.8| 0.900| 8.01 | | Qwen3-VL-2B-Instruct | 0.163 | 42.2 | 0.786 | 6.36 | | Gemma3-4B-Instruct | 0.318 | 49.1 | 0.759 | 5.23 |

Base

| Model | Multilingual MMLU | MATH CoT 2-Shot | AGIEval 5-shot | MMLU Redux 5-shot | MMLU 5-shot | TriviaQA 5-shot | |---------------------|-------------------|-----------------|----------------|-------------------|-------------|-----------------| | Ministral 3 14B | 0.742 | 0.676 | 0.648 | 0.820 | 0.794 | 0.749 | | Qwen3 14B Base | 0.754 | 0.620 | 0.661 | 0.837 | 0.804| 0.703 | | Gemma 3 12B Base | 0.690 | 0.487 | 0.587 | 0.766 | 0.745 | 0.788 | | | | | | | | | | Ministral 3 8B | 0.706 | 0.626 | 0.591 | 0.793 | 0.761| 0.681 | | Qwen 3 8B Base | 0.700 | 0.576 | 0.596 | 0.794 | 0.760 | 0.639 | | | | | | | | | | Ministral 3 3B | 0.652 | 0.601 | 0.511 | 0.735 | 0.707 | 0.592 | | Qwen 3 4B Base | 0.677 | 0.405 | 0.570 | 0.759 | 0.713| 0.530 | | Gemma 3 4B Base | 0.516 | 0.294 | 0.430 | 0.626 | 0.589 | 0.640 |

Usage

The model can be used with the following frameworks;

• `vllm`: See [here](#vllm)
• `transformers`: See [here](#transformers)

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...