Grok 4.5 Token Counter

How to Count Grok 4.5 Tokens Exactly

xAI exposes a Tokenize service in its SDK and gRPC API. Pass the exact text and model grok-4.5 to TokenizeText, then count the returned token records. Each record includes a token ID and its string representation, which makes the endpoint useful for both totals and boundary inspection.

This is more reliable than applying a tiktoken ratio because the endpoint runs the tokenizer selected for Grok 4.5. It requires an xAI API key and sends the supplied text to xAI, so use it according to your organization's data-handling policy.

import os
import xai_sdk

client = xai_sdk.Client(api_key=os.getenv("XAI_API_KEY"))
tokens = client.tokenize.tokenize_text(
    text="Count this Grok 4.5 prompt.",
    model="grok-4.5",
)
print(len(tokens))
for token in tokens:
    print(token.token_id, token.string_token)

Grok 4.5 Token Facts

Tokenizer Count vs Grok API Usage

TokenizeText counts the text you submit to the tokenization endpoint. A real Grok request can also contain system messages, images, function definitions, search results, cached input, reasoning, and generated output. Review the usage object on the actual response when estimating cost.

Grok 4.5 applies higher long-context pricing once a prompt crosses the provider's documented threshold. That makes an exact preflight count especially useful for long agent traces, but current pricing should always be read from xAI rather than copied into a permanent estimate.

Compare Grok 4.5 Prompt Variants with TokenizeText

Grok 4.5 token IDs must come from xAI's model-aware tokenizer. The local explorer's o200k_base reference output is useful for cross-encoding inspection, but those IDs cannot be relabeled as Grok IDs. Pass model="grok-4.5" to TokenizeText for each prompt variant so both counts use the same provider tokenizer.

Set XAI_API_KEY in your environment before running this example. It sends each text to xAI and prints the returned count and IDs without generating a completion. Keep complete chat messages and tool definitions in your final request accounting; this comparison measures only the strings passed to TokenizeText.

# pip install xai-sdk
import os
from xai_sdk import Client

client = Client(api_key=os.environ["XAI_API_KEY"])
prompts = ["Summarize this document.", "请总结这份文档。"]
for prompt in prompts:
    tokens = client.tokenize.tokenize_text(
        text=prompt, model="grok-4.5"
    )
    token_ids = [token.token_id for token in tokens]
    print(repr(prompt), len(token_ids), token_ids)

Tiktokenizer Reference Sources

Tokenizer behavior and model limits change. Verify production decisions with current provider documentation:

• xAI: Grok 4.5 model page
• xAI: Tokenize API reference
• xAI: Grok 4.5 announcement
• xAI: Current pricing