Words & Characters
≈ 7,500 words
≈ 40,000 characters
(rule of thumb → 1 token ≈ ¾ word ≈ 4 chars)
Printed Pages
15 pages, single‑spaced
30 pages, double‑spaced
Think: one dense book chapter
Conversation Transcript
≈ 45–50 min of two‑way chat
(ideal fuel for an Agent summarizer)
Code Footprint
~2,300 lines of well‑commented Apex
or a full Lightning Web Component library
JSON / Data
~350 KB raw JSON
≈ 4,000 trimmed Case records
Perfect for vector‑chunk ingestion
Images (Vision)
1,024 × 1,024 photo →
• detail:"low" ≈ 85 tokens
• detail:"high" ≈ 765 tokens
Crop or caption+URL to stay lean
Docs / Slides
15‑slide PPT (75 words/slide)
≈ 1,500 tokens of text
OCR scans → chunk → embed for RAG
Customer Case History
150 multi‑note Service Cloud cases
(≈ 10k tokens total)
Ready for root‑cause clustering & Agent‑Or actions
Understanding Token Usage
What is a token?
Tokens are the basic units that AI models process. They're not exactly words—they're pieces of words, sometimes characters, sometimes larger chunks. Different languages tokenize differently. English typically averages about 0.75 words per token, but this varies widely.
Why tokens matter
Understanding token usage helps you optimize your AI interactions: stay within context limits, reduce costs, and improve performance. For large-scale applications, efficient token use can significantly impact your budget and system responsiveness.
Optimizing token usage
To reduce token usage: be concise, use structured formats when possible, choose lower detail levels for images when appropriate, and consider chunking large documents strategically. For API interactions, monitor and analyze your token usage patterns to identify optimization opportunities.