AEO & AI Search

LLM Context Window & Token Cost Estimator

Calculate how many tokens your landing page consumes when scraped by autonomous AI agents, web crawlers, or LLMs (GPT-4o, Claude 3.5, Gemini 1.5).

Benchmarks:Claude 3.7 Sonnet OpenAI GPT-4o DeepSeek-R1 Google Gemini 2.0 Flash

LLM Context Window & Token Cost Estimator

Active Model:

Paste HTML or Page Copy377 characters • ~109 tokens

Estimated TokensCalibrated BPE

109

~3.5 chars / token

Signal-to-NoiseClean DOM

40%

44 text vs 65 markup

Cost / 1,000 Scrapes$3/1M

$0.330

On Claude 3.7 Sonnet

Context WindowAnthropic

0.05%

Of 200,000 limit

Claude 3.7 Sonnet Context Utilization109 / 200,000 tokens (0.05%)

Ingestion Cost Comparison (Per 10,000 Scrapes):

Click any card to switch active model

Claude 3.7 Sonnet

$3.27

$3/1M • 200k ctx

Claude 3.5 Sonnet

$3.27

$3/1M • 200k ctx

Claude 3.5 Haiku

$0.87

$0.8/1M • 200k ctx

GPT-4o

$2.73

$2.5/1M • 128k ctx

GPT-4o mini

$0.16

$0.15/1M • 128k ctx

o3-mini (Reasoning)

$1.20

$1.1/1M • 200k ctx

Gemini 2.0 Flash

$0.11

$0.1/1M • 1000k ctx

Gemini 1.5 Pro

$1.36

$1.25/1M • 2000k ctx

DeepSeek-R1 (Reasoning)

$0.60

$0.55/1M • 64k ctx

DeepSeek-V3

$0.15

$0.14/1M • 64k ctx

Llama 3.3 70B

$0.44

$0.4/1M • 128k ctx

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Methodology & Grounded Benchmarks

What is the Signal-to-Noise Ratio (SNR) in web scraping?

Signal-to-Noise Ratio measures the proportion of readable content tokens versus raw HTML boilerplate, div wrappers, and script tags. Standard web pages often exhibit an SNR below 25%, meaning 75% of your token budget is wasted on markup overhead.

Why does token count matter for Agent Engine Optimization (AEO)?

Autonomous AI agents (such as Claude Computer Use, OpenAI Operator, and Perplexity) prioritize sites that provide clean, semantic HTML or JSON-LD. Pages with high token bloat take longer to parse and risk truncation in restricted agent sub-contexts.