Developer & Digital Tools Β· π Global
AI Token Counter
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 β 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
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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.