data-token-counting-reference

name	Data: Token counting reference
description	Reference documentation for counting Claude model tokens with the Messages count_tokens endpoint and Anthropic SDK or CLI examples, including warnings against OpenAI tokenizers
ccVersion	2.1.163
allowed-tools	Read Write Edit Bash
license	BSD-3-Clause license
metadata	{"skill-author":"Lord1Egypt"}

Token Counting

Use the count_tokens endpoint (POST /v1/messages/count_tokens) for accurate token counts against Claude models. Token counts are model-specific — pass the same model ID you'll use for inference.

Do not use tiktoken. It's OpenAI's tokenizer. It undercounts Claude tokens by ~15–20% on typical text, and by much more on code or non-English input. Any estimate from tiktoken, gpt-tokenizer, or similar is wrong for Claude.

Count a file or string

from anthropic import Anthropic

client = Anthropic()
resp = client.messages.count_tokens(
    model="{{OPUS_ID}}",
    messages=[{"role": "user", "content": open("CLAUDE.md").read()}],
)
print(resp.input_tokens)

TypeScript: await client.messages.countTokens({model, messages}) → .input_tokens. See {lang}/claude-api/README.md for other SDKs.

CLI

ant messages count-tokens --model {{OPUS_ID}} \
  --message '{role: user, content: "@./CLAUDE.md"}' \
  --transform input_tokens -r

Diffing a file across two versions

The endpoint is stateless — count each version separately and subtract:

from anthropic import Anthropic
import subprocess

client = Anthropic()
def count(text: str) -> int:
    return client.messages.count_tokens(
        model="{{OPUS_ID}}",
        messages=[{"role": "user", "content": text}],
    ).input_tokens

before = subprocess.check_output(["git", "show", "HEAD:CLAUDE.md"], text=True)
after = open("CLAUDE.md").read()
print(count(after) - count(before))

Full docs: see the Token Counting entry in shared/live-sources.md.