LangMart: Mistral Tiny
Model Overview
| Property | Value |
|---|---|
| Model ID | openrouter/mistralai/mistral-tiny |
| Name | Mistral Tiny |
| Provider | mistralai |
| Released | 2024-01-10 |
Description
Note: This model is being deprecated. Recommended replacement is the newer Ministral 8B
This model is currently powered by Mistral-7B-v0.2, and incorporates a "better" fine-tuning than Mistral 7B, inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.
Description
LangMart: Mistral Tiny is a language model provided by mistralai. This model offers advanced capabilities for natural language processing tasks.
Provider
mistralai
Specifications
| Spec | Value |
|---|---|
| Context Window | 32,768 tokens |
| Modalities | text->text |
| Input Modalities | text |
| Output Modalities | text |
Pricing
| Type | Price |
|---|---|
| Input | $0.25 per 1M tokens |
| Output | $0.25 per 1M tokens |
Capabilities
- Frequency penalty
- Max tokens
- Presence penalty
- Response format
- Seed
- Stop
- Structured outputs
- Temperature
- Tool choice
- Tools
- Top p
Detailed Analysis
Mistral Tiny represents the smallest tier in Mistral's model lineup, optimized for ultra-fast inference where speed matters more than maximum intelligence. This model targets use cases requiring sub-50ms latency, minimal computational resources, and high throughput at scale. Tiny excels at simple classification (sentiment, intent, category), basic extraction (keywords, entities, dates), simple completions (autocomplete suggestions, template filling), rapid content moderation, and high-volume batch processing where individual query quality is less critical than aggregate throughput. The model trades reasoning depth and nuanced understanding for exceptional speed and efficiency, enabling applications to process thousands of requests per second on modest hardware. Mistral Tiny is ideal for preprocessing pipelines feeding larger models, real-time user interfaces requiring instant feedback, cost-optimization for simple tasks that don't justify larger models, and edge deployments with severe resource constraints. Think of Tiny as the "API glue" model - fast, efficient, and good enough for straightforward tasks in larger AI systems.