Know what your AI workload will cost.
Exact tokenization where supported. Calibrated estimates elsewhere. Compare models, evaluate prompt caching ROI, forecast monthly API expenses, and find the most cost-efficient architecture for your workload. All calculations run strictly in your browser—zero prompts transmitted.
Universal AI Token Counter
Zero-latency, client-side BPE tokenization with real-time multi-model benchmarks.
What is TokenMath?
TokenMath is an independent developer intelligence platform designed to eliminate financial ambiguity from production AI engineering. As LLM architectures evolve rapidly across OpenAI, Anthropic, Google Cloud, DeepSeek, Meta, and Mistral, developers face unprecedented complexity: disparate token vocabularies, compounding prompt caching formulas, hidden write surcharges, and tiered long-context billing.
TokenMath provides byte-accurate, zero-latency token counting and cost modeling that runs entirely within your browser's local memory. No prompts, proprietary code, or customer communications are ever transmitted over external networks.
Why AI Token Pricing Intelligence Matters in 2026
Headline model pricing ($X per 1M input / $Y per 1M output) rarely reflects the actual invoice an organization receives at scale. Real-world AI unit economics are governed by nuanced micro-mechanisms:
While cache hits offer discounts up to 90%, providers like Anthropic and OpenAI charge a 1.25x write surcharge on initial prefix ingestion. Google offers free cache writes with hourly retention fees, requiring rigorous break-even modeling.
A 1,000-word prompt does not produce the same token count across models. OpenAI's 200,000-entry o200k_base vocabulary encodes multilingual and code text up to 25% more compactly than legacy 65,000-entry tokenizers.
With full-history replay, cumulative input-token usage can grow roughly quadratically with agent steps, turning a $0.05 single call into a $1.80 multi-turn execution.
How AI Token Counting Works Under the Hood
Modern Large Language Models do not process strings as human words. They rely on subword algorithms like Byte-Pair Encoding (BPE) or SentencePiece. BPE iteratively identifies the most frequent byte pairs in a training corpus and merges them into dictionary entries.
- English Prose: Approximately 1.3 to 1.4 tokens per word (75 words ≈ 100 tokens).
- Source Code (Python/TypeScript): Approximately 1.8 to 2.2 tokens per word due to special symbols, indentation whitespace, and camelCase casing.
- JSON & Structured Data: Braces, quotes, and keys frequently fragment unless formatted with compact delimiters.
- Multilingual Text: Non-Latin alphabets (such as Greek, Cyrillic, or CJK) expand into multiple byte tokens per character in smaller vocabularies.
How We Verify AI Pricing Data
Read Full MethodologyTokenMath enforces a fail-closed data integrity pipeline. We cross-reference daily pricing feeds from OpenRouter and LiteLLM against official first-party documentation from OpenAI, Anthropic, Google Cloud, DeepSeek, Meta/Together, and Mistral AI.
Daily automated scripts ingest 3,500+ model configurations and detect price drops, context window expansions, or new snapshot releases.
Rate variances are quarantined for human confirmation against official provider docs before entering the production registry and changelog ledger.
Who TokenMath is Built For
Size prompts accurately, avoid context limit truncation errors, and evaluate trade-offs between flagship and high-throughput mini models.
Forecast monthly SaaS API expenses, simulate customer unit margins, and determine break-even points for prompt caching or self-hosted GPU clusters.
Model chunking costs for vector embeddings, estimate top-k retrieval overhead, and optimize multi-turn agent tool call budgets.
31 tracked models • 11 interactive calculators • Local in-browser privacy