Ministral 3 14B
Parameters
14B
Context Length
256K
Modality
Multimodal
Architecture
Dense
License
Apache 2.0
Release Date
2 Dec 2025
Knowledge Cutoff
Jun 2025
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
2x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
256,000 tokens
Consumer
4x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#94
| Benchmark | Score | Rank |
|---|---|---|
General Knowledge | 0.794 | 24 |
Rankings
Overall Rank
#94
Coding Rank
-
About Ministral 3 14B
Ministral 3 14B is a high-density, multimodal transformer model engineered by Mistral AI to bridge the gap between edge-efficient computing and frontier-class intelligence. As the largest member of the Ministral 3 family, it employs a sophisticated Cascade Distillation strategy, where knowledge is progressively transferred from larger parent models, such as Mistral Small 3.1, into a more compact 14-billion-parameter footprint. This architecture integrates a 13.5-billion-parameter decoder-only language core with a frozen 410-million-parameter Vision Transformer (ViT) encoder, enabling the model to process interleaved image and text inputs with high precision.
The technical foundation of the model features 40 transformer layers and a hidden dimension of 5120, utilizing Grouped Query Attention (GQA) with 32 query heads and 8 key-value heads to optimize memory throughput during inference. It incorporates modern architectural best practices, including RMSNorm for stable normalization, SwiGLU activation functions for enhanced non-linear processing, and Rotary Positional Embeddings (RoPE) enhanced by YaRN scaling. These components collectively support an expansive context window of 256,000 tokens, allowing for the ingestion of massive document sets or complex multi-turn agentic workflows without performance degradation.
Designed for sophisticated automation and private AI deployments, Ministral 3 14B excels in agentic tasks through native support for function calling and structured JSON outputs. Its training emphasizes efficiency and versatility, providing robust multilingual capabilities across more than 40 languages and high-tier performance in reasoning-heavy domains like mathematics and coding. By balancing a dense architectural structure with advanced quantization compatibility, the model is optimized for deployment on local workstations and enterprise edge hardware, offering a high-performance alternative to much larger cloud-based systems.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
1,000,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
5,120
Number of Layers
40
FFN Intermediate Size (Dense)
16,384
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
131,072
About Ministral 3
Ministral 3 is a family of efficient edge models with vision capabilities, available in 3B, 8B, and 14B parameter sizes. Designed for edge deployment with multimodal and multilingual support, offering best-in-class performance for resource-constrained environments.