ToolsInsightsToken Cheatsheet

Interactive Tools

Insights

What does 10,000 tokens look like?

A visual guide to understanding token usage in AI models

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 code

Full React component library

Or 50+ Python functions with docs

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

• 4K image: ≈ 1,105 tokens (high detail)

Crop, resize, or use URLs to optimize

Docs / Slides

15‑slide PPT (75 words/slide)

≈ 1,500 tokens of text

OCR scans → chunk → embed for RAG

Thinking Tokens

GPT-5 Thinking: Uses ~2-5x base tokens

Gemini 2.5 Pro: Adjustable thinking budgets

Internal reasoning + final response tokens

Audio & Video

1 hour audio ≈ 18,000 tokens

1 min video ≈ 1,500 tokens

Transcription + visual analysis combined

Batch Processing

50% cost savings vs real-time

24-hour processing window

Perfect for large-scale analysis

Customer Case History

150 multi‑note Service Cloud cases

(≈ 10k tokens total)

Ready for root‑cause clustering & Agent‑Or actions

2025 AI Model Comparison

Compare context windows, capabilities, and optimal use cases for the latest AI models. Note: Claude tokenizer produces ~16-30% more tokens than GPT/Gemini for identical content.

Google Gemini

Model	Context Window	Output Limit	Reasoning	Strengths	Best Use Case
Gemini 2.5 Pro	1M tokens (2M coming)	65K tokens	Yes	Complex reasoning, large context	Research, complex analysis, large documents
Gemini 2.5 Flash	1M tokens	65K tokens	Yes	Best price-performance	General purpose, balanced tasks
Gemini 2.5 Flash-Lite	1M tokens	65K tokens	Yes	Most cost-efficient	High-volume, simple tasks

OpenAI GPT

Model	Context Window	Output Limit	Reasoning	Strengths	Best Use Case
GPT-5	400K tokens (API)	128K tokens	Unified reasoning	94.6% AIME math, unified model	General purpose, coding, reasoning
GPT-5 Mini	400K tokens	128K tokens	Yes	Lower cost, good performance	Cost-conscious applications
GPT-5 Thinking	196K tokens	128K tokens	Advanced reasoning	Deep reasoning, complex problems	Research, complex problem solving

Anthropic Claude

Model	Context Window	Output Limit	Reasoning	Strengths	Best Use Case
Claude 4 Sonnet	200K tokens	64K tokens	No	72.7% SWE-bench coding	Coding, consistent performance
Claude 4 Opus	200K tokens	64K tokens	No	Premium performance	High-quality text, analysis

Token Optimization Tips

Practical techniques to reduce token usage and improve efficiency with real before/after examples.

Language Efficiency

❌ BEFORE (12 tokens)

"Could you please help me understand this?"

✅ AFTER (6 tokens)

"Explain this concept"

Saved: 6 tokens (50% reduction)

Context Management

• Reserve 10-20% of context window for response

• For 200K context: use max 160K for input

• Chunk large documents strategically

GPT-5: 400K context → use 320K max input
Gemini 2.5: 1M context → use 800K max input

Structured Formats

❌ VERBOSE (45 tokens)

"I need you to analyze this data and tell me what the trends are and what insights you can find..."

✅ STRUCTURED (25 tokens)

"Analyze data for:
1. Trends
2. Key insights
3. Patterns"

Smart Model Selection

✓ Simple tasks: Flash-Lite, GPT-5 Mini

⚡ Balanced: Flash, GPT-5

🧠 Complex: Pro, Thinking models

Cost Example:
Gemini 2.5 Flash-Lite: $0.10/1M tokens
vs. Claude 4 Sonnet: $3.00/1M tokens
30x cheaper for simple tasks!

Interactive Token Calculator

Count tokens in real-time and see how different models tokenize your text.

Enter your text:

Model:

Tokens

Characters

Words (approx)

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.

Salesforce AgentForce