LLM Token Counter & Cost Estimator

Estimate tokens and API cost for GPT-4o, Claude, Gemini, and Llama

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注意: 下記のツールインターフェースは現時点で英語のみに対応しています。UI の翻訳は順次進めています。 このツールを英語で見る →
Characters
0
Words
0
Tokens (est.)
0
Context used
0.00%
Estimated cost per call
Input (0 × $2.5/1M)$0.000000
Output (500 × $10/1M)$0.005000
Total per call$0.005000
× 1,000 calls$5.00
× 1,000,000 calls$5000.00

Token counts are estimates based on average characters-per-token (~4 for GPT-4o). Actual counts from the model's tokenizer may differ by 5–15%. Prices reflect published rates and may change - check the provider's pricing page for authoritative numbers.

使い方

  1. 1
    Paste your prompt

    Drop the full prompt - system message, user message, or a long RAG context.

  2. 2
    Pick a model

    Choose GPT-4o, Claude, Gemini, or Llama. Each model has its own price and tokenizer ratio.

  3. 3
    Set expected output tokens

    Add the response length you expect. The tool shows per-call cost and 1K / 1M-call totals.

LLM Token Counter & Cost Estimator について

Free online LLM token counter and cost calculator. Paste any prompt to estimate the token count for GPT-4o, GPT-4o mini, Claude Opus/Sonnet/Haiku, Gemini 2.0, and Llama 3, plus the per-call and per-1M-call API cost. Runs entirely in your browser. 712 Tools の LLM Token Counter & Cost Estimator はモダンな JavaScript API を使って完全にブラウザ内で動作します - サーバーがあなたのデータを見ることは一切ありません。つまり、即時の結果、完全なプライバシー、アップロード制限なしです。

デバッグ、簡単な確認、あるいは本番のインシデント対応まで - このツールは何度でも無料で使えます。ウォーターマークなし、サインアップなし、ツール内広告なし。

よくある質問

How accurate are the token counts?

They're estimates based on average characters-per-token (~4 for GPT, ~3.6 for Claude). Actual counts from the model's real tokenizer usually differ by 5–15%.

Where do the prices come from?

Published rates from OpenAI, Anthropic, Google, and hosted-Llama providers. Rates change - always verify with the provider before building financial projections.

Does it upload my prompt?

No. Counting and cost math run entirely in your browser.

Why does context-used % matter?

If a prompt approaches the model's context window, the model may truncate or refuse. The gauge helps you stay well under the limit.

続きを読む

7 分で読了

Which LLM is actually cheapest? An honest 2026 comparison

GPT-4o vs Claude Sonnet 4.5 vs Gemini 2.5 vs Llama 3.3 vs DeepSeek V3 - the per-call and monthly cost math for real workloads, and where prompt caching changes the answer.

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How to write better AI prompts that actually work (ChatGPT, Claude, Gemini in 2026)

Prompt engineering isn't magic - it's a small set of structural moves that produce dramatically better output from any modern LLM. Here's the shape, the anti-patterns, and the template.

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System prompts that ship: Custom GPTs, Claude Projects, and Cursor rules in 2026

The system prompt is where you install your agent's personality, guardrails, and output style. Here's the structure that works across every major LLM platform.

6 分で読了

LLM token counting and API cost estimation: a 2026 developer's guide

Tokens aren't words, GPT-4o and Claude count them differently, and a 1M-context prompt can cost $30. Here's how tokens actually work, and how to estimate cost before you ship.

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