Opus 4.7 Token Efficiency: German Prompts Burn Up to 2x Tokens vs English

Claude's tokenizer has a known language skew, and a recent post on r/ClaudeAI demonstrates the real-world impact of using non-English languages with the Opus 4.7 model.

The Problem

A Pro subscriber ran a stock analysis prompt (forecasting The Trade Desk, Coreweave, Cloudflare) first in English, then in German. Results:

• English (Opus 4.7 Extended): consumed 37% of session tokens
• English (Opus 4.6): 33%
• English (Sonnet): ~28%
• German (Opus 4.7): 100% in seconds

The same prompt in German with the same model exhausted the entire session limit almost instantly.

Why It Happens

Claude tokenizes text. English averages ~1 token per 0.75 words; German averages ~1 token per 0.5 words — sometimes worse. Compound nouns like Aktienmarktanalyse split into more tokens than stock market analysis, and umlauts plus lower training-data coverage inflate counts. For equivalent semantic content, a German prompt + response can consume 1.5× to 2× the tokens of English.

Workarounds

The model itself suggests two mitigations:

• Prompt in German but ask for responses in English — e.g., spreadsheet labels stay English while conversation remains German
• Ask the model to be terser to reduce output token count

Anthropic is aware of the multilingual token-cost issue, but it's a structural property of the tokenizer — not something that can be patched client-side.

Takeaway

If you're using Claude in a language other than English and hitting session limits, this is likely why. For heavy workflows (tool calls, web searches, long outputs), consider switching to English for the output to conserve tokens.