Ministral 14b 2512
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 30, 2026. Published August 30, 2026.
Ministral 14b 2512 is aimed at enterprises that need to process visual data alongside text and automate workflows through external functions. It handles image classification, document extraction, and multimodal question answering, while its tool calling capability lets the model invoke APIs or custom scripts to fetch real‑time information. Mistral AI builds the model on a sparse‑mixture architecture that balances efficiency with depth, allowing it to run on modest hardware without sacrificing accuracy. The approach emphasizes open‑source alignment practices and transparent evaluation, distinguishing it from more proprietary offerings.
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 30, 2026. Official Mistral AI pricing.
What Ministral 14b 2512 would cost on your workload — run it through the cost calculator →
Where to try Ministral 14b 2512 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 kinds of business problems is this model best suited for?+
It excels in scenarios where visual content must be interpreted together with textual instructions, such as product image tagging, invoice data extraction, and interactive support bots that can call external services.
How do I integrate the model into my current system?+
Start by retrieving the model from Mistral’s model repository, load it with your preferred inference framework, and use the provided SDK to configure tool calling endpoints that match your internal APIs.
In what ways does this model differ from other models in the Ministral family?+
Unlike sibling models that focus solely on text, this version adds native image understanding and a built‑in tool calling layer, giving it the ability to both see and act without separate components.
What limitations should I keep in mind when deploying this model?+
The model may struggle with extremely high‑resolution images or highly specialized domains that were not represented in its training data, and tool calling requires careful security review of the functions it can invoke.
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