Tokenize endpoint and tools

Count tokens before sending

Three questions arise before every serious API call: Does my prompt fit within the context window? How much will it cost? Where do I cut if the document is too long? Answering these after sending—when you see an error or receive a bill—always costs more than answering them beforehand. The Grok API provides exactly the tool for this: a tokenization endpoint, free and decoupled from completions.

The /v1/tokenize-text endpoint

POST /v1/tokenize-text takes raw text and returns the corresponding token count. The key feature comes down to one sentence: unlike a completion call, this endpoint consumes no billed tokens. It counts; it doesn’t generate.

curl -X POST "https://api.x.ai/v1/tokenize-text" \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Your text to tokenize here."
  }'

The response gives you the total token count and, depending on the version, the breakdown of individual tokens. Three uses follow directly from this. First, validation: you verify that the sum of the system prompt, history, and user message stays within the target model’s window—1 million tokens on grok-4.3 and 500,000 on grok-4.5. Next, cost estimation: the resulting number, multiplied by the model’s rate, gives you the request price before you commit. Finally, truncation: for an oversized document, tokenization shows you where to cut cleanly rather than letting the API reject the whole thing.

The console visual tool

For prototyping, xAI offers a visual tokenizer in its web console where you paste text and immediately see its breakdown. It’s an excellent teaching tool—use it at least once before designing your prompt templates.

You’ll make three useful observations there. French produces significantly more tokens than English for semantically identical content, which directly impacts the cost of a French-language application. Source code is split according to a different logic than natural text. And punctuation or special characters often occupy a token all by themselves, which explains why verbose JSON costs surprisingly much to transmit.

Where to place counting in a pipeline

Beware the reflex to tokenize systematically before every call: you double the number of network requests and add latency to every interaction, for information you often already knew. Reserve exact counting for cases where uncertainty is real.

For everything else, an estimate suffices. Count roughly 1.5 tokens per French word, about 0.3 tokens per code character, and 256 to 1,792 tokens per image depending on its size. These orders of magnitude, applied client-side, let you locally reject a prompt manifestly out of bounds without consuming a call.

Save the endpoint for three situations then: prompts whose size varies widely, typically user-provided documents or excerpts retrieved by a RAG system; initial calibration of your templates, when you establish once and for all the token budget for each building block; and debugging, when a context overflow error appears and you need to identify which element caused the bloat.

Built-in tools, a separate line item

Tokens aren’t the only source of cost. Built-in API tools are billed per call, independently of the text volume exchanged.

Run the numbers on your own traffic before generalizing a tool. An application that triggers a web search on every request pays $5 per thousand interactions for the tool alone, which frequently exceeds the cost of tokens consumed over the same period. Web search isn’t superfluous, but it should be triggered on condition rather than by default.

Key points to remember

• The POST /v1/tokenize-text endpoint lets you count tokens without billing
• The web tokenization console is useful for prototyping and understanding the tokenizer
• Don’t call the tokenization endpoint on every request in production: use estimates
• Built-in tools (Web Search, Code Execution, etc.) have additional costs per call
• French consumes about 1.5 tokens per word on average