Token Verbosity Is the New Pricing War

DeepInfra argues that per-token prices alone are an incomplete measure of AI inference cost because models can differ substantially in output volume, reasoning overhead, retry rates, and task success rates. It cites benchmark examples in which Kimi K3 and DeepSeek V4 Pro generated far more output tokens than comparable open-weight models, potentially making low-priced tokens expensive in total, while explaining that reasoning depth, tokenization methods, workload complexity, context length, and sequential output generation affect both costs and latency. The company recommends evaluating cost per successfully completed task rather than price per million tokens and presents its platform’s controls for managing inference economics, including prompt caching for repeated context, API-level token and cost reporting, adjustable reasoning effort, access to multiple models and private GPU deployments, and live metrics for throughput, latency, traffic, and errors.

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