Mistral Medium 3.1.2508
Mistral AI
At a glance
- Price per 1M tokens (input)
- $0.40
- Price per 1M tokens (output)
- $2.00
- Context
- 131,072 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
The Mistral Medium 3.1.2508 model is designed for businesses and organizations that require advanced image understanding and automation capabilities. This model is well-suited for tasks such as image analysis, object detection, and image classification, making it a valuable tool for industries such as healthcare, finance, and retail. The vendor's approach to artificial intelligence emphasizes the importance of explainability and transparency, which is reflected in the model's ability to provide clear and concise outputs. Tool calling, which refers to the ability of the model to call external functions or tools, is also a key feature of this model, allowing users to integrate it with other systems and workflows. By leveraging this model, businesses can streamline their operations and improve their decision-making processes.
Specifications & pricing
| Input (per 1M tokens) | $0.40 |
|---|---|
| Output (per 1M tokens) | $2.00 |
| Context window | 131,072 tokens |
| Max output | 131,072 tokens |
| Capabilities | images, tool calling |
LiteLLM community dataset (MIT), verified August 15, 2026. Official Mistral AI pricing.
What Mistral Medium 3.1.2508 would cost on your workload — run it through the cost calculator →
Where to try Mistral Medium 3.1.2508 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 is this model good for+
This model is good for tasks that require advanced image understanding, such as image analysis, object detection, and image classification, as well as automation tasks that involve tool calling, which allows the model to call external functions or tools
How do I get started with this model+
To get started with this model, users should familiarize themselves with the model's capabilities and limitations, and then design and implement a workflow that leverages the model's strengths, such as integrating it with other systems and tools
How does this model differ from other models in the line+
This model differs from other models in the line in its emphasis on explainability and transparency, as well as its advanced image understanding capabilities, which make it well-suited for tasks that require a high degree of accuracy and precision
What are the limitations of this model+
The limitations of this model include its potential inability to perform well on tasks that are outside of its training data or that require a high degree of common sense or real-world experience, and users should carefully evaluate the model's performance on their specific use case before deploying it in production
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