Magistral Medium 1.1 uses a usage‑based pricing model, where costs are tied to the number of tokens processed both the text you send (input tokens) and the text the model generates (output tokens). Rather than paying a flat subscription, you pay only for the compute you actually consume, making this structure flexible and scalable from early experimentation to full‑scale production. Teams can estimate budgets based on expected prompt lengths, typical response size, and overall usage volume, helping avoid paying for unused capacity.

In common API pricing tiers, input tokens are billed at a lower rate than output tokens because generating responses generally requires more compute effort. For example, Magistral Medium 1.1 might be priced at around $2.50 per million input tokens and $10 per million output tokens under standard usage plans. Larger contexts or extended outputs naturally increase total spend, so refining prompt structure and managing response verbosity can help optimize costs. Because output tokens typically make up the largest share of usage billing, designing efficient interactions is key to cost control.

To further manage expenses, developers often use prompt caching, batching, and context reuse, which help reduce redundant processing and lower effective token counts. These strategies are particularly valuable in high‑volume environments like conversational agents, automated content workflows, and data analysis systems. With transparent usage‑based pricing and practical cost‑management techniques, Magistral Medium 1.1 provides a predictable, scalable pricing structure suitable for a wide range of AI applications.