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
Technical Specifications
Attention Structure
Multi-Head Attention
Hidden Dimension Size
5120
Number of Layers
40
Attention Heads
32
Key-Value Heads
8
Activation Function
SwigLU
Normalization
RMS Normalization
Position Embedding
Absolute Position Embedding
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.
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.
Other Ministral 3 Models
Evaluation Benchmarks
No evaluation benchmarks for Ministral 3 14B available.
Rankings
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Coding Rank
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Context Size: 1,024 tokens