Inference
Token Counting
Measuring how many tokens a prompt will use before you send it, usually through a provider endpoint that runs the model's own tokenizer.
In depth
Measuring how many tokens a prompt will use before you send it, usually through a provider endpoint that runs the model's own tokenizer. Anthropic's /v1/messages/count_tokens accepts the same system prompt, tools, images, and PDFs as a real request, is free to call, and returns an estimate that can differ slightly from billed usage. Counts are tokenizer-specific: Anthropic notes Claude Opus 4.7 and later produce roughly 30 percent more tokens for the same text than earlier models, so recount when you switch.
Primary source: platform.claude.com
Example
In practice, developers reach for Token Counting when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Token Counting?
Measuring how many tokens a prompt will use before you send it, usually through a provider endpoint that runs the model's own tokenizer.
Why does Token Counting matter for AI developers?
Token Counting sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Token Counting?
Developers Digest publishes tutorials and videos that cover Inference topics including Token Counting. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Thinking Tokens
The tokens a reasoning model generates while working through a problem before it writes its final answer.
Inference
Tokenizer
The component that converts raw text into tokens (and back) for a language model.
Inference
Tokens per Second
A throughput measure of how many output tokens a model or serving system produces each second.
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