Understanding Token Usage and Costs

Token usage directly affects how much you'll spend on AI-assisted content updates. This guide explains what tokens are, what influences token consumption, and how to understand the costs involved.

• What Are Tokens?
• Input Tokens vs Output Tokens
• What Affects Token Usage?
• Understanding Model Pricing
• Real-World Cost Examples
• Tips for Managing Token Costs
• Monitoring Your Token Usage

What Are Tokens?

Tokens are the basic units that AI models use to process text. Understanding tokens is essential for managing your AI costs.

Tokens are chunks of text that AI models read and write. Think of them as pieces of words or characters AI models charge per token processed.

Token to Text Conversion:

• 1 token ≈ 4 characters
• 1 token ≈ 0.75 words (on average)
• 100 tokens ≈ 75 words
• 1,000 tokens ≈ 750 words
• 1,500 tokens ≈ 1,125 words (typical blog post intro)

Real-World Examples:

• "Hello" = 1 token
• "artificial intelligence" = 2 tokens
• "WordPress" = 2 tokens
• This paragraph (50 words) ≈ 65-70 tokens

How AI Models Count Tokens

Tokenization Process:

1. AI breaks text into tokens
2. Common words = 1 token each
3. Uncommon/long words = multiple tokens
4. Punctuation and spaces = separate tokens

Examples:

`"I love WordPress" = 4 tokens "I" (1) + "love" (1) + "Word" (1) + "Press" (1)

"The cat sat" = 4 tokens "The" (1) + "cat" (1) + "sat" (1) + [space] (1)

"Antidisestablishmentarianism" = 6-7 tokens (Long, uncommon word splits into multiple tokens)`

Input Tokens vs Output Tokens

AI models charge separately for input (what you send) and output (what AI returns). Understanding this distinction is crucial for managing costs.

Input Tokens (Reading)

What they are:

• Text you send TO the AI
• Your blog post content
• Analysis instructions
• Context and prompts

What counts as input in FreshRank:

• Your post title
• Your post content (body text)
• Post excerpt
• Analysis instructions from FreshRank
• Custom instructions (if enabled)
• Previous analysis results (for draft creation)
• SEO meta data (title, description)

Example Input:

`A 1,500-word blog post = ~2,000 input tokens

• FreshRank analysis instructions = ~500 tokens
• Custom instructions = ~100 tokens Total input: ~2,600 tokens`

Output Tokens (Writing)

What they are:

• Text the AI sends BACK to you
• Analysis results
• Updated content drafts

What counts as output in FreshRank:

• Complete analysis report (issues, recommendations, summaries)
• Updated post content
• Revised title and excerpt
• Meta description updates
• Explanations of changes made

Example Output:

• Analysis results for 1,500-word post = ~3,000-5,000 output tokens
• Draft update for same post = ~2,000-2,500 output tokens

Pricing Comparison (GPT-5 example):

• Input tokens: $1.25 per 1 million tokens
• Output tokens: $10.00 per 1 million tokens
• Output is 8x more expensive than input

Real-World Impact: 1,000 input tokens = $0.00125 1,000 output tokens = $0.01000 Same quantity, 8x price difference  

What Affects Token Usage?

Several factors influence how many tokens you'll use per operation. Understanding these helps you predict and control costs.

1. Content Length

Content length is the biggest factor affecting token usage.

Input tokens scale with content length:

• 500-word post: ~700-800 input tokens
• 1,500-word post: ~2,000-2,500 input tokens
• 3,000-word post: ~4,000-5,000 input tokens
• 5,000-word post: ~6,500-7,500 input tokens

Output tokens also scale (but less predictably):

• Short post analysis: ~2,000-3,000 output tokens
• Long post analysis: ~4,000-8,000 output tokens
• Depends on number of issues found

Cost Impact Example (GPT-5)

2. Web Search (Biggest Multiplier)

Enabling web search dramatically increases token usage.

Why web search uses more tokens:

• AI sends search queries (output tokens)
• Receives search results (input tokens)
• Processes results (more input tokens)
• Generates responses using that data (output tokens)
• Multiple searches per operation

Token Increase:

• Without web search: 5,000-10,000 tokens per analysis
• With web search: 15,000-40,000 tokens per analysis
• Multiplier: 2-4x more tokens

Cost Impact Example (GPT-5, 1,500-word post)

When the extra cost is worth it:

• ✅ Content with statistics and data
• ✅ Product reviews with pricing
• ✅ Technical content referencing versions
• ✅ Posts older than 1 year
• ❌ Opinion pieces
• ❌ Creative writing
• ❌ Recently published content

3. Custom Instructions

Long custom instructions add tokens to every request.

Token Addition:

• Short instructions (50 words): ~65 tokens per operation
• Medium instructions (200 words): ~260 tokens per operation
• Long instructions (500 words): ~650 tokens per operation

Cumulative Impact:

200-word custom instructions = ~260 tokens

Over 100 operations:

• Additional tokens: 26,000
• Additional cost (GPT-5): ~$0.26-0.33`

Recommendation:

• Keep custom instructions under 200 words
• Focus on 2-3 key priorities
• Be concise and specific
• Remove unnecessary explanations

4. Content Update Filters

More filters enabled = more comprehensive updates = more output tokens.

Token Impact by Filter Configuration:

Minimal (Factual + High only):

• Fixes: 5-10 issues
• Output tokens: ~2,000-2,500
• Cost: Lower

Moderate (3 categories + High/Medium):

• Fixes: 15-25 issues
• Output tokens: ~3,500-5,000
• Cost: Medium

Maximum (All categories + All severities):

• Fixes: 30-50+ issues
• Output tokens: ~6,000-10,000
• Cost: Higher

Cost Example (GPT-5, draft creation)

Strategic Approach:

• Start with fewer filters
• Add more only if needed
• Prioritize high-impact categories
• Skip low-severity issues initially

5. Model Selection

Different models have different efficiencies and costs.

Token Efficiency:

• Some models are more concise (fewer output tokens)
• Some models are more verbose (more output tokens)
• Efficiency varies by task type

General Patterns:

• GPT-5 Nano: Most concise, minimal explanations
• GPT-5 Mini: Balanced brevity
• GPT-5: Moderate detail
• GPT-5 Pro: More detailed, longer outputs
• O3 models: Reasoning-focused, can be verbose

Price vs Efficiency Trade-off:

Sometimes a more expensive model uses fewer tokens

6. Number of Issues Found

More problems = longer analysis = more output tokens.

Analysis Output Scales with Issues:

• 5 issues found: ~2,000 output tokens
• 15 issues found: ~4,000 output tokens
• 30 issues found: ~6,000 output tokens
• 50+ issues found: ~8,000+ output tokens

What Affects Issue Count:

• Content age (older = more issues)
• Content quality (lower quality = more issues)
• Last update date (longer ago = more issues)
• Content complexity
• Analysis categories enabled

Cost Impact

Understanding Model Pricing

Different models have vastly different pricing. So you'll need to find a balance between your budget and desired performance.

You can check current pricing for all OpenAI models on their API pricing page. FreshRank tracks your OpenAI costs automatically so you'll always be able to see your spending in real-time directly on your dashboard.

OpenRouter provides access to 450+ models with varying prices. FreshRank cannot estimate OpenRouter costs, so you must check openrouter.ai/activity for actual spending.

Real-World Cost Examples

Let's look at realistic monthly costs for different usage patterns.

Small Blog (20 posts/month)

Usage Pattern:

• 20 analyses per month (avg 1,500 words)
• 10 drafts per month
• No web search
• Using GPT-5

Token Usage:

• Analyses: 20 × 6,500 = 130,000 tokens
• Drafts: 10 × 6,300 = 63,000 tokens
• Total: 193,000 tokens

Approximate cost: ~$1.30/month  

Medium Blog (100 posts/month)

Usage Pattern:

• 100 analyses per month (avg 1,500 words)
• 50 drafts per month
• Web search on 20 analyses
• Using GPT-5

Token Usage:

• Standard analyses: 80 × 6,500 = 520,000 tokens
• Web search analyses: 20 × 16,000 = 320,000 tokens
• Drafts: 50 × 6,300 = 315,000 tokens
• Total: 1,155,000 tokens  

Approximate cost: ~$8-10/month  

Large Content Site (500 posts/month)

Usage Pattern:

• 500 analyses per month (avg 2,000 words)
• 250 drafts per month
• Web search on 100 analyses
• Using GPT-5 for most, GPT-5 Mini for some

Token Usage:

• Standard analyses: 400 × 8,000 = 3,200,000 tokens
• Web search analyses: 100 × 20,000 = 2,000,000 tokens
• Drafts: 250 × 7,500 = 1,875,000 tokens
• Total: 7,075,000 tokens  

Approximate cost: ~$50-70/month  

Tips for Managing Token Costs

Strategy 1: Choose the Right Model

Match model to content importance:

• GPT-5 Mini: Routine updates, simple fixes
• GPT-5: Most content (best balance)
• GPT-5 Pro/O3: High-value, critical content only

Potential savings: 50-80% on routine content

Strategy 2: Use Web Search Selectively

Enable web search only for:

• Content with statistics/data
• Posts older than 1 year
• Product reviews with pricing
• Technical content referencing versions

Disable for:

• Opinion pieces
• Creative content
• Recently updated posts
• Content without time-sensitive info

Potential savings: 60-75% on non-factual content

Strategy 3: Optimize Content Update Filters

Start minimal:

• Factual Updates + High Severity only
• Fix critical issues first

Expand gradually:

• Add Medium severity for polish
• Add more categories as needed
• Skip Low severity unless perfecting

Potential savings: 30-50% per draft

Strategy 4: Keep Custom Instructions Brief

Target length: Under 200 words (260 tokens)

Be concise:

• Focus on 2-3 key priorities
• Remove unnecessary explanations
• Use clear, direct language

Potential savings: Minimal per operation, adds up over hundreds of operations

Strategy 5: Batch Similar Content

Group by content type:

• Analyze all news posts together
• Analyze all tutorials together
• Use consistent settings per batch

Benefits:

• Predictable costs
• Easier to budget
• Optimize settings per content type

Strategy 6: Test Before Bulk Operations

Always test first:

1. Analyze 1-3 sample posts
2. Check token usage in Analysis Details
3. Calculate cost × total posts
4. Adjust settings if needed
5. Proceed with bulk operation

Prevents:

• Unexpected high costs
• Wasted tokens on wrong settings
• Budget overruns

Monitoring Your Token Usage

Where to check:

1. FreshRank System Status - Cumulative totals and estimates
2. Analysis Results Page - Per-operation details
3. AI Provider Dashboard - Actual billing (most accurate)