Why higher costs with more tokens?

Higher costs occur with more tokens because token processing directly consumes computational resources. Every additional token increases the workload for the AI model during both input processing and output generation.

These costs stem primarily from the significant computational power required by large language models (LLMs). As token count grows, the processing complexity increases, particularly within the attention mechanisms of Transformer architectures, often scaling quadratically. Generating output tokens involves intensive sequential computations constrained by real-time demands. Storing and managing the context of numerous tokens also requires substantial memory (RAM/VRAM). Provider pricing typically reflects this resource usage, charging per token to cover infrastructure and operational expenses.

The cost-per-token model ensures users pay for actual resource consumption. It scales with usage, making solutions commercially viable for providers while enabling complex, long-context applications for users. Businesses can control expenses by optimizing inputs and outputs. This pricing structure directly supports the development and operation of advanced AI capabilities that require significant computational investment.