Inference
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output). Output is typically several times pricier: Claude Opus 5.5 lists $4 per million input tokens and $20 per million output tokens, a 5x ratio. Cached input reads bill far below the base input rate, so a task's real cost depends on cache hit rate and how much the model writes, not on the headline rates alone.
Primary source: platform.claude.com
Example
In practice, developers reach for Input vs Output Token Pricing when they need the capability described above as part of an AI feature or workflow.
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
The split pricing model where LLM APIs charge one rate per million tokens you send (input) and a higher rate per million tokens the model generates (output).
Input vs Output Token Pricing sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Developers Digest publishes tutorials and videos that cover Inference topics including Input vs Output Token Pricing. Check the blog and YouTube channel for hands-on walkthroughs.
Related
An inference technique where a small, fast draft model proposes several tokens ahead and the large target model checks them all in one parallel forward pass.
The tokens a reasoning model generates while working through a problem before it writes its final answer.
Caps on how much traffic an organization can send to a model API, measured in requests per minute (RPM) and tokens per minute (TPM).

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