Đếm token ChatGPT Claude Gemini | Ước tính chi phí API (July 2026 edition)

Giao diện công cụ

Chạy trên trình duyệt — không tải lên server Ước tính

Ký tự
0
Từ
0
Dòng
0
CJK / Latin
0 / 0
Bytes
0
Token (OpenAI / o200k)
0

Ước tính chi phí

Hệ số output
Mỗi request
$0
Input
$0
Output
$0
Tổng
$0

Tháng tính theo GPT-5.6 Luna ($1/$6 per 1M).

So với giới hạn ChatGPT Web

Khác API. Plus Instant ≈ 32K.

So với giới hạn Web 0%

System prompt (tùy chọn)

API tính system + user cùng input tokens.

Model Token Sử dụng Input Output

OpenAI dùng tiktoken (o200k_base). Claude / Gemini / DeepSeek là script-weighted approx (±8%). Link chia sẻ chỉ lưu trong URL hash. Giá tham khảo 2026-07-21.

How token counting works

Paste once to see character stats, OpenAI exact tokens (tiktoken o200k), and script-weighted approx counts for Claude / Gemini / DeepSeek with input/output cost and context usage %. Pricing data is reviewed against official provider sources for the .

ChatGPT Web vs API limits

ChatGPT Plus Instant is roughly 32K tokens; Thinking mode is closer to 256K. OpenAI API models (GPT-5.6, etc.) expose 1M+ context. The Web limit bar compares your prompt against app limits — not API limits.

Exact vs approx

OpenAI models use the same o200k_base tokenizer as the API (exact). Claude and Gemini use proprietary tokenizers, so we apply measured CJK / Latin / other tok/char rates and label them approx (±8% typical).

Japanese uses more tokens than English

Model tok / char Note
Gemini 3.6 Flash ~0.57 Efficient on Japanese
GPT-5.6 / o200k ~0.68 OpenAI exact
Claude Sonnet 5 ~0.87 approx

System prompt & RAG chunks

Billing counts system + user together. Add RAG snippets to the main textarea or system field before trusting the numbers. Gemini 3.1 Pro switches to long-context tier pricing above 200K input tokens (shown as a badge in the table). GPT-5.6 tiers switch above 272K input.

RAG chunk reference (tiktoken measured / GPT-5.6 1M ctx)

Values measured with o200k_base (2026-07-19).

Chunk Chars Tokens 1M ctx 32K Web
Short FAQ (Japanese) 133 68 0.01% 0.21%
Tech article section 1,763 970 0.1% 3.03%
~10 PDF pages 7,542 3,972 0.4% 12.41%
Full internal wiki (risk) 80,000 42,779 4.28% 133.68%

Pricing sources

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