LLM Token Counter & Cost Simulator Technical Architecture Guide
State-of-the-art foundation models including GPT-6.1 Sol, Claude Opus 5.5, and Gemini 4 Argon process text in discrete 'Tokens' rather than raw words or characters. Non-English and multilingual inputs consume up to 2-3x more tokens due to Byte-Pair Encoding (BPE). SafeAI Token Counter calculates live token counts and estimates exact API invocation costs with 100% client-side privacy.
1. Subword Tokenization & Real-Time Price Simulation
SafeAI implements cl100k_base and modern multi-model heuristics inside your browser engine. As you paste or type text, it dynamically tokenizes content, calculates characters, words, and lines, and cross-references live token rates: GPT-6.1 Sol ($2.00/1M), GPT-6 Luna ($0.10/1M), Claude Opus 5.5 ($4.00/1M), and Gemini 4 Argon ($2.00/1M). All metrics calculate locally with zero cloud transmission.
2. Enterprise Foundation Model Pricing & Context Limits
| Model Name | Context Window | Price per 1M Input Tokens |
|---|---|---|
| OpenAI GPT-6.1 Sol | 1,050k Tokens | $2.00 / 1M Input |
| OpenAI GPT-6 Luna | 1,050k Tokens | $0.10 / 1M Input |
| Claude Opus 5.5 | 1,000k Tokens | $4.00 / 1M Input |
| Gemini 4 Argon | 1,000k Tokens | $2.00 / 1M Input |
3. Token Reduction & Cost Engineering Best Practices
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System Prompt Deduplication: Centralize repetitive instructions into concise schemas to save up to 40% of baseline token expenditures.
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Table Format Conversion: Converting wide markdown tables to delimited CSV reduces structural delimiter tokens by over 50%.
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Whitespace Normalization: Stripping trailing spaces and excessive blank lines eliminates redundant whitespace token consumption.