Developer & Digital Tools · 🌏 Global

AI Token Counter

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Compare token counts and 2026 pricing across GPT, Claude, Gemini, and Llama on one page. Includes Korean efficiency chart.

About this tool

AI Token Counter computes token counts and cost ($/KRW) across 30 LLM API models (ChatGPT, Claude, Gemini, Llama, Mistral, HyperCLOVA X) from your raw input text β€” for free. The same sentence yields different token counts per tokenizer (tiktoken / Anthropic / SentencePiece), and Korean is typically 2~3x more tokens than English. The tool runs OpenAI tiktoken WASM, Anthropic claude-tokenizer, and SentencePiece WASM directly in your browser, then produces input/output cost and a per-character Korean efficiency chart (GPT-4o 0.7 / Claude 1.3 / HyperCLOVA X 0.5). It also simulates Prompt Caching savings (10~25% of base input). LLM app developers and PMs use it for model selection.

Use cases

Scenario 1

LLM app cost estimate

For 50k chatbot responses/mo (avg 1,500 in / 800 out tokens), instantly estimate the monthly cost gap between GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro.

Scenario 2

Korean vs English token efficiency

Paste the same meaning in Korean and English to visualize per-model token ratios, and test whether HyperCLOVA X / Solar Mini are cheaper for Korean-only workloads.

Scenario 3

Prompt-Caching ROI

Simulate ROI when caching a 4,000-token system prompt drops to ~10% of base input cost β€” the basis for the caching decision.

Scenario 4

Long-doc model pick

Compare context length Γ— cost trade-offs for summarizing a 40k-token PDF in one shot β€” Gemini 2.5 Pro vs Claude Opus 4.6.

Scenario 5

Korean startup LLM PoC

A Korean-only startup compares GPT-4o mini ($0.15/1M), HyperCLOVA X, and Solar Mini in PoC and picks the cheapest within a week.

Features

  • 30-model coverage (OpenAI, Anthropic, Google, Mistral, Meta, xAI, NCSoft, Upstage)
  • Accurate in-browser counts via tiktoken WASM / claude-tokenizer / SentencePiece
  • Split input vs output cost (output is usually 3-5x input)
  • Per-character Korean token efficiency chart
  • Prompt Caching cost-saving simulation
  • Auto USD ↔ KRW conversion
  • Input text stays in your browser (WASM-only)

Frequently asked

Q. Why does token count vary across models for the same Korean text?
A. Each model uses its own tokenizer (BPE / SentencePiece / Tiktoken) trained on different Korean data and vocab. GPT-4o is the most Korean-efficient; Llama 3 uses ~3x more tokens for Korean vs English.
Q. On average, how many tokens is one Korean character?
A. GPT-4o ~0.7, Claude Sonnet 4.5 ~1.3, Gemini 2.5 ~1.0, Llama 3 ~1.8, HyperCLOVA X ~0.5. English is ~0.25-0.3 token/char.
Q. Why do input and output cost differ?
A. Output is usually 3-5x more expensive due to heavier GPU inference cost. Example: Claude Sonnet 4.5 β€” $3 input / $15 output per 1M tokens.
Q. How much does Prompt Caching save?
A. Claude / OpenAI / Gemini all support prompt caching, billing 10~25% of base input on cache hits. If a stable system prompt is >1,024 tokens for >24h, caching almost always wins.
Q. How accurate are the token counts?
A. GPT family uses OpenAI tiktoken WASM (100% accurate), Claude uses Anthropic public tokenizer (Β±1%), Gemini/Llama uses SentencePiece WASM (Β±2%). Short text (<100 tokens) is exact across all models.

Sources / references

Related tools

How we run it / disclaimer

This tool is advisory and does not constitute legal, tax, medical, or financial advice. All calculations and document generation run in your browser; inputs are never sent to a server. Ads follow Google AdSense policy and are kept separate from tool accuracy.