LLM Token Counter & Multi-Model API Cost Estimator
Multi-ModelCount Large Language Model (LLM) tokens and estimate API inference costs across OpenAI GPT-4o, Anthropic Claude 3.5, and Meta Llama 3 directly in your browser. Calculate token counts, character-to-token ratios, and cost estimates with zero server uploads.
LLM Token Counter & Multi-Model API Cost Estimator
Calculate BPE tokens, character/word density, and estimated API prompt/completion costs across top LLMs.
Interactive LLM Token Counter & Multi-Model API Cost Estimator Workspace
A client-side LLM token counter and API pricing calculator. Accurately segments text into BPE token chunks, calculates character/word density metrics, and computes exact prompt and completion costs across OpenAI (GPT-4o, o1, o3-mini), Anthropic (Claude 3.5 Sonnet, Haiku, Opus), Google (Gemini 2.0 Flash, 1.5 Pro), and DeepSeek (V3, R1) 100% in-browser.
How This Tool Works (100% Client-Side Engine)
The tool uses an in-memory Byte Pair Encoding (BPE) tokenizer calibrated to standard cl100k_base and o200k_base vocabularies. The text is segmented into Unicode byte sequences and merged into token IDs, calculating exact token counts and cost projections based on current official model pricing tables.
Step-by-Step Guide
Paste Prompt Text or Code
Drop your system prompt, document excerpt, or code snippet into the text canvas.
Select Model Architecture
Choose your target model: OpenAI (GPT-4o, GPT-4o-mini), Anthropic (Claude 3.5 Sonnet), or Meta (Llama 3).
Inspect Token Count & Pricing Breakdown
Review total token count, characters-per-token ratio, and projected per-query cost across multiple providers.
Why Use This Tool: Privacy & Performance
Crafting prompts for AI models (like GPT-4o, Claude 3.5 Sonnet, and Llama 3) requires monitoring token consumption to prevent exceeding model context windows and blowing through API budgets. Because LLMs tokenize text into subword fragments rather than standard words or characters, estimating tokens manually is inaccurate—for example, code snippets, JSON payloads, and non-English languages consume significantly more tokens per word than plain English. Uploading private prompts, proprietary documents, or customer records to cloud token counters creates confidentiality risks. ToolByte LLM Token Counter utilizes client-side BPE (Byte Pair Encoding) tokenizers running directly in your browser memory. You paste your prompt or code, and the tool calculates exact token counts, character ratios, and projects input and output API costs across popular AI providers with complete privacy.
Common Use Cases & Workflows
Sizing Prompts for LLM Context Windows
Verify that large system instructions and few-shot examples fit comfortably within 8k, 32k, or 128k model context limits.
Forecasting Multi-Model API Query Costs
Compare what a 10,000-token prompt costs to run across GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, and DeepSeek V3.
Optimizing Prompt Compression & Code Inlining
Identify token-heavy sections of prompts (such as repetitive JSON keys or unminified whitespace) to reduce API bills.
Testing Multilingual Token Inflation
Measure how non-English languages (Spanish, German, Japanese, Arabic) consume tokens compared to English.
Technical Limitations & Operational Boundaries
Frequently Asked Questions
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