How Should You Compare LLM API Pricing by Token?
LLM API pricing compared by token: input and output rates across major models, and why cost per accepted answer beats cost per million tokens.
Overview
LLM API pricing by token should compare input tokens, output tokens, cached tokens, context length, tool calls, and quality per completed task. The cheapest token price is not always the cheapest production model if it needs longer prompts, more retries, or more human review.
- Compare input and output pricing separately.
- Track average tokens per task, not only price per million tokens.
- Include retries, failed calls, latency, and answer quality in the cost model.
Token pricing is useful because it gives teams a common unit, but real tasks are not equal. Summarization, agents, code generation, support chat, and multimodal workflows may use tokens very differently. A model with a higher listed price may still be cheaper if it solves the task in fewer attempts.
For WaveSpeedAI users, token pricing is one piece of model selection. If LLM routes sit beside media generation routes, teams need one way to understand cost across text and multimodal workloads. The practical approach is to benchmark real tasks, calculate cost per successful outcome, and revisit pricing when models or usage patterns change. Token price tables mislead without your own traffic profile, so replay a week of real prompts through the candidate models and compare cost per accepted answer rather than cost per million tokens.
