LangMart: Mistral: Ministral 3B
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/mistralai/ministral-3b |
| Name | Mistral: Ministral 3B |
| Provider | mistralai |
| Released | 2024-10-17 |
Description
Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.
Description
LangMart: Mistral: Ministral 3B is a language model provided by mistralai. This model offers advanced capabilities for natural language processing tasks.
Provider
mistralai
Specifications
| Spec | Value |
|---|---|
| Context Window | 131,072 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.04 per 1M tokens |
| Output | $0.04 per 1M tokens |
Capabilities
- Frequency penalty
- Max tokens
- Presence penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top p
Detailed Analysis
Ministral 3B (October 2024 release) is Mistral's ultra-lightweight edge model with 3 billion parameters, specifically designed for on-device and edge computing deployment. Despite its compact size, Ministral 3B remarkably outperforms Mistral 7B on most benchmarks through architectural innovations and efficient training. The model excels at knowledge retrieval, commonsense reasoning, and natively supports function calling for API integration. With a 128K context window, it processes extensive documents while maintaining low memory footprint suitable for smartphones, laptops, embedded systems, and IoT devices. Ministral 3B enables privacy-preserving local inference without internet connectivity, critical for sensitive applications in healthcare, finance, and personal assistants. The model achieves impressive quality-to-size ratio, making AI accessible in bandwidth-constrained or offline environments. Ideal use cases include local translation, offline smart assistants, edge analytics, autonomous robotics, and any scenario requiring AI capabilities without cloud dependency or with strict latency requirements under 50ms.