AEO & AI Search
DeepSeek-R1 Token Counter & Reasoning Cost Estimator
High-power open-weights reasoning model ($0.55 / 1M input tokens).
LLM Context Window & Token Cost Estimator
Active Model:
Estimated TokensCalibrated BPE
42
~3.7 chars / token
Signal-to-NoiseClean DOM
74%
31 text vs 11 markup
Cost / 1,000 Scrapes$0.55/1M
$0.020
On DeepSeek-R1 (Reasoning)
Context WindowDeepSeek
0.07%
Of 64,000 limit
DeepSeek-R1 (Reasoning) Context Utilization42 / 64,000 tokens (0.07%)
Ingestion Cost Comparison (Per 10,000 Scrapes):
Click any card to switch active modelClaude 3.7 Sonnet
$1.26
$3/1M • 200k ctx
Claude 3.5 Sonnet
$1.26
$3/1M • 200k ctx
Claude 3.5 Haiku
$0.34
$0.8/1M • 200k ctx
GPT-4o
$1.05
$2.5/1M • 128k ctx
GPT-4o mini
$0.06
$0.15/1M • 128k ctx
o3-mini (Reasoning)
$0.46
$1.1/1M • 200k ctx
Gemini 2.0 Flash
$0.04
$0.1/1M • 1000k ctx
Gemini 1.5 Pro
$0.52
$1.25/1M • 2000k ctx
DeepSeek-R1 (Reasoning)
$0.23
$0.55/1M • 64k ctx
DeepSeek-V3
$0.06
$0.14/1M • 64k ctx
Llama 3.3 70B
$0.17
$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.