How It Works Under the Hood
This tool estimates token counts locally using fast, offline tokenization heuristics without loading multi-megabyte vocabulary files. It maps estimated token counts against reference API pricing tables and calculates approximate VRAM footprints at common quantization levels.
Step-by-Step Instructions
Paste your raw text, JSON payload, or code snippet into the input editor.
View the live estimated token count, character breakdown, and token-to-word ratio.
Select your target models (OpenAI, Anthropic, Google Gemini, DeepSeek).
Inspect estimated cost per call, cost per 1M requests, and estimated VRAM footprint.
Key Features & Performance
- Real-time reactive estimation with zero network latency
- Multi-model cost comparison side-by-side (reference pricing)
- Local GPU VRAM footprint estimator (Weights + KV Cache)
- Code-, whitespace-, and punctuation-aware token approximation
Local Browser Processing & Privacy
Traditional web converters upload your documents, snippets, or media files to remote servers. LLM Tokenizer & Cost Estimator runs inside your browser tab:
Your input text and files are processed locally without uploading payloads to a processing server.
Microdeck does not log or store the files or inputs you paste into tool workspaces (normal static hosting/CDN requests still load page assets).
Working outputs stay in your browser tab and are released when you reset the tool or close the tab.
Frequently Asked Questions
How accurate is the token count compared to official APIs?
Counts are fast offline BPE heuristics typically within a few percent for English prose and standard code, though exact counts vary by provider tokenizer (such as o200k_base or Claude/Gemini tokenizers). Check provider APIs before hard billing limits.
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