Magistral Small 2506
Mistral AI
At a glance
- Price per 1M tokens (input)
- $0.50
- Price per 1M tokens (output)
- $1.50
- Context
- 40,000 tokens
- Free access
- yes (see below)
Refreshed daily; data verified August 15, 2026. Published August 12, 2026.
Magistral Small 2506 is a compact language model from Mistral AI designed for developers and businesses that need reliable automation without the overhead of a large-scale system. It is particularly well-suited for tasks that require structured interaction with external software, such as database queries, API orchestration, or workflow triggers, thanks to its tool calling capability—the ability to invoke predefined functions as part of a response. The model also performs step-by-step reasoning, breaking down complex problems into intermediate logical steps, which improves accuracy in multi-stage tasks like data extraction or decision support. Mistral AI’s approach emphasizes efficiency and control, offering a model that balances performance with deployability across various environments.
Specifications & pricing
| Input (per 1M tokens) | $0.50 |
|---|---|
| Output (per 1M tokens) | $1.50 |
| Context window | 40,000 tokens |
| Max output | 40,000 tokens |
| Capabilities | tool calling, reasoning |
LiteLLM community dataset (MIT), verified August 15, 2026. Official Mistral AI pricing.
What Magistral Small 2506 would cost on your workload — run it through the cost calculator →
Where to try Magistral Small 2506 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 best used for in a business context?+
It is ideal for automating backend processes that require both understanding natural language and taking action, such as customer support ticket routing, inventory lookups, or generating structured reports. The tool calling feature lets the model interact with your existing APIs and databases, while step-by-step reasoning helps ensure that complex instructions are executed in a logical order.
How do I start integrating it into my existing systems?+
You can access it through Mistral AI’s platform or via their API, and then define the functions you want the model to call, such as 'get_user_data' or 'send_email'. Provide clear descriptions of each function, and the model will decide when to invoke them based on the user’s request. Most teams begin with a simple proof-of-concept using a few test functions before scaling up.
How does it differ from other models in the Magistral line?+
Within the Magistral family, this variant is optimized for smaller-scale deployments where speed and resource efficiency matter more than maximum raw capability. Compared to larger sibling models, it may offer less broad world knowledge but compensates with more predictable tool use and a lighter footprint, making it easier to run on standard infrastructure.
Are there any limitations I should be aware of?+
Like all language models, it can sometimes misinterpret ambiguous instructions, especially when multiple functions could apply. Its step-by-step reasoning helps mitigate this, but for critical workflows, you should validate its outputs with deterministic checks. Additionally, it may not perform as well on open-ended creative writing or deep domain expertise without fine-tuning on your specific data.
Compare with others
Org chart: how to move your company onto AI
A practical map: which company roles and processes AI agents can take over, where to start, and in what order to roll it out.