Ministral 14b
Mistral AI
At a glance
- Price per 1M tokens (input)
- $0.20
- Price per 1M tokens (output)
- $0.20
- Context
- 262,144 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 29, 2026. Published August 29, 2026.
Ministral 14b is a compact multimodal model from Mistral AI designed for developers and enterprises that need efficient image understanding and structured decision-making. It excels at tasks like visual document analysis, image captioning, and integrating vision with automated workflows. The model's key differentiator is its native support for tool calling, which lets it invoke external functions or APIs directly from a conversation, enabling agents to act on what they see. Mistral AI emphasizes efficiency and control, offering a model that balances capability with operational simplicity for production deployments.
Specifications & pricing
| Input (per 1M tokens) | $0.20 |
|---|---|
| Output (per 1M tokens) | $0.20 |
| Context window | 262,144 tokens |
| Max output | 262,144 tokens |
| Capabilities | images, tool calling |
LiteLLM community dataset (MIT), verified August 29, 2026. Official Mistral AI pricing.
What Ministral 14b would cost on your workload — run it through the cost calculator →
Where to try Ministral 14b for free
- Mistral AI offers a free chat — Le Chat (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What business tasks is Ministral 14b best suited for?+
It is ideal for automating workflows that require both visual input and follow-up actions. Common use cases include extracting data from invoices or forms, moderating image content, and building visual assistants that can trigger backend processes via tool calling. Because it is lightweight, it fits well into real-time applications where low latency matters.
How do I get started with Ministral 14b?+
You can access it through Mistral AI's platform or via major cloud providers that host their models. The model supports standard API calls for image and text input, and tool calling is configured by providing function schemas in the request. Mistral's documentation includes examples for both Python and REST, and you can test it in their interactive playground before integrating.
How does Ministral 14b differ from other models in the Mistral line?+
Unlike the larger flagship models that focus on broad language reasoning, Ministral 14b is tuned for efficiency and multimodal understanding. It is smaller, which means faster inference and lower infrastructure demands, but it still supports tool calling—a feature not always available in similarly sized models. Compared to Mistral's text-only models, it adds vision capability, making it a versatile choice for mixed-modal tasks.
What are the main limitations of Ministral 14b?+
As a smaller model, it may have lower accuracy on complex reasoning or niche visual domains compared to larger frontier models. Its image understanding is strongest with clear, high-resolution inputs; highly cluttered or low-quality images can reduce performance. Tool calling requires that you define functions precisely, and the model may occasionally misinterpret ambiguous instructions, so you should validate outputs in production.
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