Everything developers and engineering leads need to know about LLM tokenization, cost optimization, and multimodal calculations.
Tokens are the fundamental atomic units of text processed by Large Language Models. Instead of reading character-by-character, models use subword tokenizers (like Byte-Pair Encoding or SentencePiece) to compress frequent sequences of characters into single integer IDs. In English, 1 token is roughly 3.8 to 4.2 characters (or about 0.75 words). For source code and non-English scripts, the character-to-token ratio can drop significantly due to specialized syntax, punctuation, or unicode byte fragments.
Every model family is trained with its own distinct vocabulary dictionary. OpenAI's newer models (GPT-4o, o1) use the o200k_base tokenizer with 200,000 token entries. Older GPT-4 models use cl100k_base (100,000 entries). Anthropic Claude uses an ~65,000 token vocabulary, while Google Gemini uses SentencePiece with a 256,000 vocabulary. A larger vocabulary can represent common words or multilingual phrases in fewer tokens, which is why the exact same document produces different token numbers across providers.
Prompt Caching allows LLM providers to store the key-value (KV) attention states of identical prefix prompts across consecutive API calls. If the beginning of your prompt (e.g. system guidelines, documentation chunks, or conversation history) matches a previous request, the provider reuses the cached activations instead of recalculating them from scratch. Providers like Anthropic, OpenAI, Google, and DeepSeek offer 50% to 90% discounts on cached input tokens, drastically slashing recurring expenses for RAG and agentic workflows.
Batch APIs allow you to submit non-real-time asynchronous requests that are processed within a 24-hour turnaround window. In exchange for not demanding immediate sub-second latency, major providers (including OpenAI, Anthropic, and Google) discount both input and output pricing by 50%. This is ideal for bulk evals, data extraction, synthetic data generation, and offline processing pipelines.
Multimodal models convert image pixels into token patches using vision transformers (ViT). OpenAI downscales the image, fits it within a 2048×2048 grid, scales the shortest side to 768px, and divides the result into 512×512 pixel tiles—charging 170 tokens per tile plus 85 base tokens. Anthropic Claude downscales images to a maximum of 1568px on the longest side and charges roughly (pixels / 750) tokens. Google Gemini charges a flat ~258 tokens per crop.
Never. TokenMath.net is built on a 100% client-side privacy architecture. All tokenization algorithms, BPE encodings, and cost math execute directly inside your browser using pure JavaScript. Your proprietary code, system prompts, and data never leave your local device.
Our model pricing registry (data/models.json) is reviewed weekly against official API documentation from OpenAI, Anthropic, Google Cloud, DeepSeek, Together AI, and Mistral. In addition, developers and administrators can adjust live rates instantly through our Admin Console.
As calculated in our Self-Hosted TCO Solver, self-hosting open-weight models (like Llama 3.3 70B or DeepSeek-V3) on dedicated instances (e.g. RunPod, Lambda, or AWS at $2.49/hr per H100) typically crosses the break-even threshold around 50M to 150M tokens per month, depending on GPU utilization. Below this volume, managed APIs are generally cheaper because you only pay for what you use without paying for idle GPU hours.
Drop our engineering team a note or report an API update.