When to use: Paste text to get an approximate token count (real tokenizers vary — this is an estimate, not exact), see what fraction of a given context window it uses, and estimate per-call and monthly API cost using your own model's published pricing.
This tool estimates how many tokens a piece of text will consume, tracks that against a model's context window, and projects API cost per call and per month — using pricing and context-window figures you supply, since these vary by provider and model and change over time. It's meant as a quick planning estimate, not an exact tokenizer count or a substitute for your provider's own usage dashboard.
Real LLM tokenizers (like OpenAI's tiktoken or Anthropic's tokenizer) split text into subword units based on a trained vocabulary, and the exact count depends on the specific tokenizer, the language, and even formatting like whitespace and punctuation — there is no universal, provider-agnostic formula that reproduces an exact count for every model. Two commonly-used rough heuristics are roughly 4 characters per token for English prose, and roughly 1.3 tokens per word — this tool shows both estimates side by side (and their average) because they tend to bracket the real count for typical English text, but code, non-English text, and text with lots of punctuation or rare tokens can deviate meaningfully from either heuristic. For an exact count before a production run, use your specific provider's tokenizer library or API response (most chat completion APIs return exact token usage in the response).
A model's context window is the total token budget shared across the system prompt, conversation history, any retrieved documents, the current user input, and the space reserved for the model's own output — it is not just "how long can my prompt be." Reserving too little space for output can cause a response to be cut off mid-generation; reserving too much wastes usable context that could otherwise hold more conversation history or retrieved context. This is especially important for RAG (retrieval-augmented generation) pipelines and long-running agent conversations, where prompt size grows over the course of a session and can silently approach the context limit well before anyone notices — tracking usage percentage, not just raw token count, makes that growth visible.
Most LLM API providers charge a different rate for input (prompt) tokens versus output (completion) tokens — output tokens are typically priced several times higher than input tokens, because generating each output token requires a full forward pass through the model, while input tokens can often be processed more efficiently in a batch and are sometimes eligible for caching discounts on repeated content. This tool asks for both prices separately for exactly that reason. Pricing also changes over time as providers release new model versions and adjust rates — always pull current pricing directly from your provider's pricing page before budgeting, rather than reusing a number from memory or an older reference.
Paste representative text — an actual prompt template, a typical document chunk, or sample conversation history — to get a token estimate. Enter your model's context window size and how many tokens you want reserved for output, to see usage percentage and remaining headroom. Enter your provider's current published input and output price per million tokens (check their pricing page for the exact current numbers, since these are not hardcoded here), your expected output length, and your expected call volume, to get a per-call and projected monthly cost — useful for budgeting before scaling up a feature, or for comparing the cost impact of switching models or trimming prompt size.
The character-based estimate (chars/4) and word-based estimate (words × 1.3) are two independent rough heuristics that tend to agree reasonably well for typical English prose but can diverge for text with unusual structure — very long words, lots of punctuation or numbers, code, or non-English text. Showing both (and their average as the working estimate) makes it visible when your specific text is an unusual case where the heuristics disagree more than usual, which is a signal to get an exact count from your provider's real tokenizer rather than trusting either rough estimate.
Yes — reserving zero or too little output budget risks the model's response being truncated mid-generation if it runs longer than expected, which is a common and often confusing failure mode (the response just stops, sometimes mid-sentence, with no error). A reasonable default is to reserve at least your typical expected response length plus some margin, and to actually check your API response's finish/stop reason in production — a "length" or "max_tokens" stop reason means the response was cut off by the output budget, not because the model was finished.
Generating output tokens requires the model to run a full forward pass for every single new token, one at a time, sequentially — this is inherently more expensive per token than processing input tokens, which can be computed in parallel across the whole prompt in a single pass. Additionally, some providers offer prompt caching, which can substantially discount input tokens that are repeated across calls (like a stable system prompt or reused document context), widening the effective gap between input and output cost further — check whether your provider offers caching and whether your usage pattern (large stable context, small varying user input) is a good fit for it.
Because this tool multiplies cost per call by call volume, even a modest per-call token reduction can add up meaningfully at production scale — trimming 500 tokens off a system prompt that's sent on every one of 10,000 daily calls removes 5 million input tokens per day, which at typical input pricing is a real, recurring monthly cost difference, not a rounding error. This is exactly why prompt-size optimization (removing redundant instructions, using prompt caching for stable content, retrieving only the most relevant context rather than everything available) is a standard cost-control lever in production LLM systems, alongside choosing a cheaper model tier where quality requirements allow it.
No — filling a large context window with excessive or low-relevance content (in RAG, sometimes called "context stuffing") tends to increase both cost and latency, and can sometimes degrade response quality if it dilutes the model's attention with irrelevant information ("needle in a haystack" retrieval problems are a known failure mode even for models advertising very large context windows). A large context window is best treated as headroom for legitimately needed content — conversation history, genuinely relevant retrieved documents — not a budget to spend fully on every call regardless of relevance.
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