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Calculate exact token counts, estimate prompt input & output API costs, and analyze context window limits across top AI models (GPT-4o, Claude 3.5 Sonnet, Claude 3.7 Sonnet, DeepSeek R1/V3, Gemini 1.5/2.0, and Llama 3.3). Powered by BPE tokenizers running 100% client-side in your web browser.
Your prompts, system instructions, and RAG contexts are calculated strictly inside local browser memory using WASM/JS Byte-Pair Encoding.
Model: GPT-4o (OpenAI)
Input user prompts, system instructions, MCP tool schemas, or RAG context payloads into the editor or pick from pre-configured sample prompts.
Choose between OpenAI (GPT-4o, o1, o3-mini), Claude (3.5 / 3.7 Sonnet), DeepSeek (R1, V3), Gemini (1.5 / 2.0), or Meta Llama 3.3 to apply model-specific tokenizers and pricing rates.
Review real-time context window usage fill percentages, estimated USD session API costs, text density ratios, and truncate oversized prompts with one click.
Zero server network requests. Your proprietary prompts, system instructions, RAG context payloads, and API keys stay 100% inside your browser memory.
Supports OpenAI o200k_base (GPT-4o, GPT-4o-mini, o1, o3-mini) and cl100k_base (GPT-4, GPT-3.5) token encoding engines alongside estimation models for Claude, DeepSeek, Gemini, and Llama.
Calculates estimated USD API costs for prompt tokens and output completion limits across top AI models including GPT-4o, Claude 3.5 Sonnet, Claude 3.7 Sonnet, DeepSeek R1, DeepSeek V3, and Gemini 2.0.
Displays visual fill percentage and remaining capacity across standard context limits (8k, 32k, 128k, 200k, 1M, and 2M tokens) to prevent context overflow errors.
Real-time analytics including total character count, word count, line count, average tokens per word, and character-to-token ratio.
Instantly truncate prompts to fit target token limits (e.g., 4096 tokens) and download structured JSON or Markdown reports.
Our LLM Token Counter & Context Estimator is open source under HiMat Technology. Inspect BPE encoding logic, star the repository, or contribute model pricing configurations on GitHub.
Unlike remote token counting APIs that stream your prompt text to external servers, HiMat's LLM Token Counter executes Byte-Pair Encoding (`o200k_base` and `cl100k_base`) directly inside your local web browser memory using WASM and JavaScript. Your confidential system prompts, user queries, RAG context vectors, and API keys never touch a network connection.
A token is the fundamental unit of text processed by Large Language Models (LLMs). Depending on the tokenizer, a token can be a single character, a word fragment, or a whole word. In English, 1,000 tokens equal roughly 750 words.
No. The HiMat LLM Token Counter & Context Estimator operates 100% locally in your web browser. Neither your prompt text, system instructions, RAG context chunks, nor audit reports leave your device.
Different AI models use different Byte-Pair Encoding (BPE) vocabularies. OpenAI GPT-4o uses `o200k_base` (200,000 token vocabulary), while GPT-4 uses `cl100k_base` (100,000 vocabulary). Claude and Llama use their own custom tokenizers with slightly different token densities.
LLMs have strict context window limits (e.g. 128k or 200k tokens). Exceeding limits results in API errors or truncated prompt context, causing hallucination or agent task failure.
Yes, 100% free with no account registration required, no daily rate limits, and zero advertisements.
HiMat Technology designs production-grade AI agent systems, optimized prompt context pipelines, RAG architecture, and 21-day fixed-scope AI pilots with full source code transfer.
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