File size: 8,539 Bytes
a3021af |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
library_name: vllm
language:
- en
- fr
- es
- de
- it
- pt
- nl
- zh
- ja
- ko
- ar
license: apache-2.0
inference: false
base_model:
- mistralai/Ministral-3-14B-Base-2512
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
tags:
- mistral-common
---
# Ministral 3 14B Instruct 2512
The largest model in the Ministral 3 family, **Ministral 3 14B** offers frontier capabilities and performance comparable to its larger [Mistral Small 3.2 24B](https://huggingface.co/mistralai/Mistral-Small-3.2-Instruct-2506) counterpart. A powerful and efficient 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 14B can even be deployed locally, capable of fitting in 32GB of VRAM in BF16, and less than 24GB of RAM/VRAM when quantized.
We provide a no-loss FP8 version [here](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-FP8), you can find other formats and quantizations in the [Ministral 3 - Quants](https://huggingface.co/collections/mistralai/ministral-3-quants) collection.
## Key Features
Ministral 3 14B consists of two main architectural components:
- **13.5B Language Model**
- **0.4B Vision Encoder**
The Ministral 3 14B 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
Private AI deployments where advanced capabilities meet practical hardware constraints:
- Private/custom chat and AI assistant deployments in constrained environments
- Advanced local agentic use cases
- Fine-tuning and specialization
- And more...
Bringing advanced AI capabilities to most environments.
## 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-quants).
## Benchmark Results
We compare Ministral 3 to similar sized models.
### Reasoning
| Model | AIME25 | AIME24 | GPQA Diamond | LiveCodeBench |
|---------------------------|-------------|-------------|--------------|---------------|
| **Ministral 3 14B** | <u>0.850</u>| <u>0.898</u>| <u>0.712</u> | <u>0.646</u> |
| Qwen3-14B (Thinking) | 0.737 | 0.837 | 0.663 | 0.593 |
| | | | | |
| **Ministral 3 8B** | 0.787 | <u>0.860</u>| 0.668 | <u>0.616</u> |
| Qwen3-VL-8B-Thinking | <u>0.798</u>| <u>0.860</u>| <u>0.671</u> | 0.580 |
| | | | | |
| **Ministral 3 3B** | <u>0.721</u>| <u>0.775</u>| 0.534 | <u>0.548</u> |
| Qwen3-VL-4B-Thinking | 0.697 | 0.729 | <u>0.601</u> | 0.513 |
### Instruct
| Model | Arena Hard | WildBench | MATH Maj@1 | MM MTBench |
|---------------------------|-------------|------------|-------------|------------------|
| **Ministral 3 14B** | <u>0.551</u>| <u>68.5</u>| <u>0.904</u>| <u>8.49</u> |
| 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 | <u>66.8</u>| 0.876 | <u>8.08</u> |
| Qwen3-VL-8B-Instruct | <u>0.528</u>| 66.3 | <u>0.946</u>| 8.00 |
| | | | | |
| **Ministral 3 3B** | 0.305 | <u>56.8</u>| 0.830 | 7.83 |
| Qwen3-VL-4B-Instruct | <u>0.438</u>| <u>56.8</u>| <u>0.900</u>| <u>8.01</u> |
| 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 | <u>0.676</u> | 0.648 | 0.820 | 0.794 | 0.749 |
| Qwen3 14B Base | <u>0.754</u> | 0.620 | <u>0.661</u> | <u>0.837</u> | <u>0.804</u>| 0.703 |
| Gemma 3 12B Base | 0.690 | 0.487 | 0.587 | 0.766 | 0.745 | <u>0.788</u> |
| | | | | | | |
| **Ministral 3 8B** | <u>0.706</u> | <u>0.626</u> | 0.591 | 0.793 | <u>0.761</u>| <u>0.681</u> |
| Qwen 3 8B Base | 0.700 | 0.576 | <u>0.596</u> | <u>0.794</u> | 0.760 | 0.639 |
| | | | | | | |
| **Ministral 3 3B** | 0.652 | <u>0.601</u> | 0.511 | 0.735 | 0.707 | 0.592 |
| Qwen 3 4B Base | <u>0.677</u> | 0.405 | <u>0.570</u> | <u>0.759</u> | <u>0.713</u>| 0.530 |
| Gemma 3 4B Base | 0.516 | 0.294 | 0.430 | 0.626 | 0.589 | <u>0.640</u> |
## 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.* |