--- title: Ministral-3-3B-Instruct-2512-BF16 canonical_url: "https://www.modelscope.cn/models/mistralai/Ministral-3-3B-Instruct-2512-BF16" md_url: "https://www.modelscope.cn/models/mistralai/Ministral-3-3B-Instruct-2512-BF16.md" repository: mistralai/Ministral-3-3B-Instruct-2512-BF16 last_updated: 2026-07-22 license: apache-2.0 model_type: - mistral3 architectures: - Mistral3ForConditionalGeneration base_model: - mistralai/Ministral-3-3B-Base-2512 base_model_relation: finetune parameters: 3.8B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch language: - en - fr - es - de - it - pt - nl - zh - ja - ko - ar downloads: 338 stars: 0 tags: - mistral-common --- # Ministral-3-3B-Instruct-2512-BF16 > Ministral-3-3B-Instruct-2512-BF16 - mistralai 在 ModelScope 开源的模型。Ministral 3 3B Instruct 2512 BF16 mistralai/Ministral-3-3B-Instruct-2512-BF16 是 ModelScope 魔搭社区上的 3.8B 参数机器学习模型，采用 apache-2.0 许可，基于 mistralai/Ministral-3-3B-Base-2512 构建。 - **Repository**: mistralai/Ministral-3-3B-Instruct-2512-BF16 - **License**: apache-2.0 - **Parameters**: 3.8B - **Base model**: mistralai/Ministral-3-3B-Base-2512 - **Tags**: mistral-common - **Downloads**: 338 - **Stars**: 0 - **Last updated**: 2026-07-22 Source: https://www.modelscope.cn/models/mistralai/Ministral-3-3B-Instruct-2512-BF16 --- # Ministral 3 3B Instruct 2512 BF16 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 instruct post-trained version, fine-tuned for instruction tasks, making it ideal for chat and instruction based use cases. 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, capable of fitting in 16GB of VRAM in BF16, and less than 8GB of RAM/VRAM when quantized. We provide a no-loss FP8 version [here](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512), you can find other formats and quantizations in the [Ministral 3 - Additional Checkpoints](https://huggingface.co/collections/mistralai/ministral-3-additional-checkpoints) collection. Learn more in our [blog post](https://mistral.ai/news/mistral-3) and [paper](https://arxiv.org/abs/2601.08584). ## Key Features Ministral 3 3B consists of two main architectural components: - **3.4B Language Model** - **0.4B Vision Encoder** The Ministral 3 3B Instruct 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. - **System Prompt**: Maintains strong adherence and support for system prompts. - **Agentic**: Offers best-in-class agentic capabilities with native function calling and JSON outputting. - **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](https://huggingface.co/mistralai/Ministral-3-3B-Base-2512) | | **Ministral 3 3B Instruct 2512** | **Instruct post-trained** | **BF16** | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512) | | Ministral 3 3B Reasoning 2512 | Reasoning capable | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512) | | Ministral 3 8B Base 2512 | Base pre-trained | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Base-2512) | | Ministral 3 8B Instruct 2512 | Instruct post-trained | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Instruct-2512) | | Ministral 3 8B Reasoning 2512 | Reasoning capable | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512) | | Ministral 3 14B Base 2512 | Base pre-trained | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Base-2512) | | Ministral 3 14B Instruct 2512 | Instruct post-trained | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512) | | Ministral 3 14B Reasoning 2512 | Reasoning capable | BF16 | [Hugging Face](https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512) | Other formats available [here](https://huggingface.co/collections/mistralai/ministral-3-additional-checkpoints). ## 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 | ## License This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt). *You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.*