TOKEN_AUDIT // SYSTEM.v4.8

ENGINE: BPE_REGEX_UNICODE
ACTIVE_SWARM: 4 AGENTS
MEM_GC: 0.00 MB
EST_BURN_RATE: 0 TOK/S

MULTI-MODEL TOKEN WATERFALL & COST DRIFT AUDITOR

Zero-dependency, client-side cryptographic and lexical token audit terminal. Deconstruct complex multi-agent prompts, identify reasoning and chain-of-thought blocks, benchmark Anthropic prompt caching discounts against OpenAI and Google APIs, and simulate subagent swarm spend velocity in real time.

⚡ 100% Client-Side ES6 🛡️ Zero API Leakage / Pure Offline 🧠 Claude 3.5 / GPT-4o / Gemini 1.5 Pro 📈 60 FPS Dynamic Velocity Canvas

[01] LIVE BPE TOKEN SLICER & REASONING DETECTOR

TOTAL TOKENS 0
PROMPT TOK 0
REASONING TOK 0
CHARACTERS 0
BYTE SIZE 0 B
TOKEN STREAM FRAGMENTS (HOVER FOR METADATA): 0 chunks rendered

[02] MULTI-MODEL PRICING MATRIX & PROMPT CACHE SAVINGS

Model Input / 1M Output / 1M Cache Read Est. Run Cost
Claude 3.5 Sonnet $3.00 $15.00 $0.30 (-90%) $0.000000
GPT-4o (Omni) $2.50 $10.00 $1.25 (-50%) $0.000000
Gemini 1.5 Pro $3.50 $10.50 $0.875 (-75%) $0.000000
DeepSeek V3 $0.14 $0.28 $0.014 (-90%) $0.000000
Prompt Cache Hit Rate: 75%
Active Subagent Swarm Size: 4 Agents
Expected Output Multiplier: 1.25x
ESTIMATED PROMPT CACHE SAVINGS (CLAUDE): $0.000000
TELEMETRY 3.2

Real-time Token Stream & TTFT Benchmark Terminal

> Ready. Click [START TOKEN STREAM] to simulate LLM inference generation...
THROUGHPUT: 84.2 tok/s TTFT: 142 ms
ACCUMULATED BATCH COST: $0.00142
Active Model: Claude 3.7 Sonnet (Hybrid Reasoning Cluster)

[03] REAL-TIME SUBAGENT SPEND VELOCITY & INGESTION WATERFALL (60 FPS)

Claude 3.5
GPT-4o
Gemini 1.5
Swarm Ingestion Velocity

⚙️ BPE LEXICAL PARSING ENGINE

Implements Unicode-aware regular expression token segmentation matching standard GPT/Claude tokenizer boundaries (byte pairs, contractions, numerical clustering, and whitespace grouping).

/(?:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?[\p{L}]+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+/gu

🧠 REASONING BLOCK ISOLATION

Automatically identifies and isolates <thinking>, <thought>, and scratchpad XML tags to calculate reasoning token overhead separately from prompt input payloads.

prompt_tokens = total - thinking_tokens; cost = (prompt * rate_in) + (reasoning * rate_reason);

🔒 ZERO TELEMETRY LEAKAGE

Every calculation and token render runs 100% locally in browser memory. No prompts, codebases, or API tokens ever leave the client workstation.

Privacy Attestation: Zero External Requests (0 KB Egress)
CLI & AUTOMATION // QUICK INTEGRATION
# Scan repository token burn rates with Token Audit CLI
git clone https://github.com/marko1olo/token-audit.git
cd token-audit
python tokenaudit_scan.py --path ./src --model claude-3-5-sonnet --cache-ratio 0.75 --live-waterfall

🧮 Real-Time LLM Token Cost & Inference Waterfall Simulator

Calculate exact pipeline cost across frontier models for large context windows, multi-turn agent loops, and batch workloads.

25,000 tokens
4,000 tokens
10 runs / day
MODEL ID INPUT RATE OUTPUT RATE PER RUN TOTAL BATCH COST
ENGINEERING SYNDICATE

Адольф Петушков & Жирняк (Jirnyak)

@marko1olo (Адольф) @Jirnyak (Жирняк)
AP

Адольф Петушков

LEAD ARCHITECT & SYSTEMS

Game engine internals, 0B GC hot paths, autonomous AI orchestrators, enterprise clinical CRM & real-time audio DSP engines.

Ж

Жирняк (Jirnyak)

DEEP TECH & PHYSICS SPECIALIST

N-Body gravitational simulation, micromagnetic Landau-Lifshitz solvers, quantum Liouville dynamics & low-level macOS HID automation.

🌐 Unified Syndicate Portfolio & Sister Applications

12 PRODUCTION HUBS
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ADOLF PETUSHKOV ECOSYSTEM |
60 FPS | PWA READY | |