How the Claude token counter works
Use the editor to inspect the text you plan to send: paste a draft, compare wording and look for repeated instructions or oversized examples. The live panel shows boundaries and IDs from the selected reference encoding. Switching that encoding changes the local comparison; it does not turn the panel into Claude's proprietary tokenizer.
For Claude input planning, run the official Messages count_tokens operation with the model ID and message structure you intend to use. Keep the system prompt, conversation history and supported tool definitions in the request. The returned input_tokens value is the provider's preflight estimate, which can differ slightly from actual message usage.
# pip install anthropic
# Set ANTHROPIC_API_KEY in your environment.
from pathlib import Path
import anthropic
prompt = Path("prompt.txt").read_text(encoding="utf-8")
client = anthropic.Anthropic()
count = client.messages.count_tokens(
model="claude-opus-5",
messages=[{"role": "user", "content": prompt}],
)
print("Claude input tokens:", count.input_tokens)Claude token calculator vs word counter
A word counter cannot account for model vocabulary, punctuation, code or message formatting. A short JSON payload can contain few ordinary words but many distinct token pieces. A translation can also change token length while preserving the meaning. Measure the actual prompt instead of applying one word-to-token multiplier to every language.
Use this Claude token calculator as a two-step workflow: inspect the draft locally, then check the structured request with Claude's API. Record the model ID next to that result. After editing the system instructions, adding tools or changing the conversation history, recount the complete input so the number still describes the request you will send.
Claude token count by model
Pass the exact active model ID to the counting endpoint. This site's Claude model guides document the API-based workflow; the browser comparison on those pages is not a substitute for a Claude count. Keep the model selection consistent when comparing two prompt drafts.
Input counting does not predict how much text or reasoning a future response will produce. Reserve an output allowance separately, and inspect the completed response's usage when measuring what actually happened. For requests that the counting endpoint does not support, follow the provider's current guidance and the usage reported by the Messages API.
| Claude guide | Tokenizer access | Counting method |
|---|---|---|
| Claude Sonnet 5 | Provider-managed | Messages count_tokens with the target model ID |
| Claude Fable 5 | Provider-managed | Messages count_tokens with the target model ID |
Tiktokenizer Reference Sources
Tokenizer behavior and model limits change. Verify production decisions with current provider documentation: