PanGu-Σ uses a usage-based pricing model, where costs are tied to the number of tokens processed both the text you send in (input tokens) and the text the model generates (output tokens). Instead of a fixed subscription, you pay only for what your application consumes. This pay-as-you-go approach makes it easy to scale from early tests to high-volume production deployments while keeping costs aligned with real usage. Teams can forecast spend by estimating typical prompt length, expected response size, and overall request volume.

In common API pricing tiers, input tokens are billed at a lower rate than output tokens because generating responses generally requires more compute work. For example, PanGu-Σ might be priced at around $4 per million input tokens and $16 per million output tokens under standard usage plans. Requests involving longer outputs or extended context naturally increase total spend, so refining prompt design and managing response verbosity can help optimize overall costs. Since output tokens usually make up the larger share of billing, careful planning of expected replies is key to controlling expenses.

To further manage costs, developers often use prompt caching, batching, and context reuse, which reduce redundant processing and lower effective token counts. These optimization strategies are especially useful in high-traffic environments such as conversational agents, automated content workflows, and analytics systems. With transparent usage-based pricing and smart cost-control techniques, PanGu-Σ provides a predictable, scalable pricing structure suitable for a wide range of AI-driven applications.