AI Token Counter
LiveCount GPT-4, Claude, and Gemini tokens with estimated API cost breakdown per model.
Estimates use cl100k_base approximation (±10%). Costs reflect published per-million-token rates.
Understanding Developer Tools
Every API call to a large language model is billed by tokens, not characters or words. A token is roughly 4 characters of English text, but varies significantly by language, punctuation, and code. Underestimating token counts leads to unexpected costs, truncated responses, and exceeded context windows. Knowing the token count before sending a prompt lets you optimize prompts for cost and context limits, batch requests efficiently, and predict API spend accurately.
Estimate the number of tokens in any text using a GPT-4 / cl100k_base compatible approximation. See live token counts, character counts, and word counts. Compare estimated API costs across GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3 Haiku, Gemini 1.5 Pro, and more. All processing is done 100% in your browser.
The Devkitr AI Token Counter estimates token counts for GPT-4, Claude, and Gemini models instantly in your browser. Paste your prompt, system message, or document and see live token counts alongside estimated API costs for the most popular models — all without sending your data anywhere.
In a typical development workflow, AI Token Counter becomes valuable whenever you need to count gpt-4, claude, and gemini tokens with estimated api cost breakdown per model. Whether you are working on a personal side project, maintaining production applications for a company, or collaborating with a distributed team across time zones, having a reliable browser-based inspection tool eliminates the need to install desktop software, write one-off scripts, or send data to third-party services that may log or retain your information. Since AI Token Counter processes everything locally on your device, your data stays private and your workflow stays uninterrupted — open a browser tab, paste your input, get your result.
Key Features
Multi-Model Token Estimation
Estimates tokens using a cl100k_base-compatible approximation (GPT-4 / Claude tokenizer) accurate to within ±10% for English text.
API Cost Calculator
Shows estimated input and output costs for GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3 Haiku, Gemini 1.5 Pro, and Gemini 1.5 Flash based on published per-million-token pricing.
Character & Word Counts
Displays character count, word count, and token count simultaneously so you can quickly compare across metrics.
100% Client-Side
All tokenization logic runs in your browser. Your prompts, API keys, and sensitive text are never sent to any server.
How to Use AI Token Counter
Paste Your Text
Paste any text — system prompt, user message, document chunk, or code — into the input textarea.
Read Token Count
The token estimate updates live as you type. See character count, word count, and token count simultaneously.
Compare Model Costs
The cost table shows estimated API costs for each model at the current token count for both input and output.
Optimize Your Prompt
Trim unnecessary words, remove redundant context, and re-check the token count until it fits your budget and context window.
Use Cases
Context Window Planning
Check whether your prompt + expected response fits within GPT-4o's 128k token limit or Claude's 200k token limit before hitting context length errors at runtime.
Cost Estimation Before Launch
Estimate monthly API spend by multiplying average tokens per request × expected request volume × model price to budget before scaling a product.
Prompt Optimization
Compare token counts before and after prompt trimming to verify you're reducing costs without losing essential instructions.
RAG Chunk Sizing
When implementing retrieval-augmented generation, count tokens per document chunk to ensure each chunk stays within embedding model limits (typically 512–8192 tokens).
Pro Tips
GPT-4 tokenizes common English words as single tokens, but non-English text and code use more tokens per character. Japanese text averages ~1.5 tokens per character.
System prompts count against your input token budget on every request. A 500-token system prompt on 100,000 requests/month costs $125/month on GPT-4o.
The context window includes both input tokens (prompt + system message + history) and output tokens. Budget for output tokens in your context window planning.
Repetitive text, redundant instructions, and verbose descriptions all inflate token counts without adding information value.
Common Pitfalls
Only planning for prompt tokens and ignoring output tokens in cost calculations
Fix: Output tokens are typically 2–4x more expensive than input tokens on most models. Always estimate expected response length and include it in cost projections.
Assuming token counts are consistent across models
Fix: Different models use different tokenizers. GPT-4 (cl100k), Claude (anthropic tokenizer), and Gemini have different token boundaries for the same text. Estimates will vary by ±5–15%.
Using character count as a proxy for token count
Fix: Character count / 4 is only accurate for common English text. Code, JSON, URLs, and non-Latin text all have higher token-to-character ratios.
Frequently Asked Questions
QHow accurate is the token count?
The estimator uses a cl100k_base-compatible approximation and is accurate within ±10% for typical English text. Exact counts require the official tokenizer library.
QWhich models are supported?
GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, Claude 3 Haiku, Gemini 1.5 Pro, and Gemini 1.5 Flash. Pricing reflects published rates and may change over time.
QIs my text sent anywhere?
No. All tokenization happens in your browser using a local approximation. Your text never leaves your device.
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