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About TokenMath.net

Built for Engineers Building with LLMs

TokenMath.net was created to solve a persistent frustration among AI developers: existing token counters and cost calculators are either outdated, locked into a single provider, slow, or rely on server endpoints that harvest prompt text.

100% Client-Side

Every tokenization and cost computation runs strictly within your browser runtime. Your proprietary system prompts, customer data, and code never touch any external server.

Exact Multi-Model BPE

We run native byte-pair encoding (BPE) for OpenAI o200k and cl100k vocabularies, paired with empirically benchmarked subword tokenizers for Claude, Gemini, DeepSeek, and Llama 3.

Weekly Verified Rates

Pricing is updated weekly and directly cross-referenced against official documentation from OpenAI, Anthropic, Google Cloud, DeepSeek, and Together AI, complete with prompt caching rates.

Tokenization Confidence Methodology

Large Language Models use Byte-Pair Encoding (BPE) or SentencePiece tokenizers to break raw UTF-8 strings into discrete token IDs. Because different model families employ distinct vocabulary sizes (from 65k to 256k), 1,000 words in GPT-4o does not translate into the same token count in Claude 3.5 or Gemini 2.0.

Current Precision Standards
o200k_base (GPT-4o, o1, o1-mini): 100% exact byte-for-byte BPE calculation.
cl100k_base (GPT-4 Turbo, GPT-3.5): 100% exact BPE encoding.
Claude 3.5 Sonnet / Haiku: Calibrated subword tokenizer model derived from extensive bilingual and code benchmarks (±3% margin).
Google Gemini 2.0 / 1.5: Calibrated SentencePiece 256k vocabulary benchmark (±4% margin).

Independent & Developer-Supported

TokenMath.net is an independent engineering project. We do not accept paid ranking placements for models. All model rankings in the Price Matrix and Budget Planner are purely determined by mathematical formulas and verified pricing rates.

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