The German Tax: What a Page of Text Really Costs on GPT-6, Claude, Gemini and Qwen

What this is about

If you use AI models through an API, you pay per token. Tokens are the chunks a model breaks language into, often pieces of words. That makes price lists quoted in "dollars per million tokens" look comparable, but they aren't. Every provider uses its own tokenizer, its own method for splitting text. The same German paragraph comes out as 160 tokens with one provider and more than 300 with the next.

Studies on the "tokenizer tax" are making the rounds right now. In late October, Rest of World reported on research by the Coalition for Language Equity in AI Research (CLEAR). The group compared tokenizers using FLORES-200, a set of professionally translated sentences in around 200 languages. According to Rest of World, among the 20 most widely used languages online, the most expensive one needed 1.5 to 5 times as many tokens as the cheapest. Across all 200 languages, one model needed up to 17 times as many. GIGAZINE puts the range for the top 20 at 1.5 to 3.1. Neither report gives a separate figure for German, and we couldn't access CLEAR's original page.

So we collected published measurements for German, converted them into euros per standard page using current list prices (a German Normseite: 1,800 characters, roughly 250 words by the usual rule of thumb), and built our own ranking. One important caveat: we didn't run any tokenizers ourselves. All token figures come from the sources named. What's new is the conversion and the side-by-side comparison.

What the measurements say about German

The most telling metric is fertility, meaning tokens per word. The lower the number, the cheaper. Two sources matter most. The first is a study by Volodymyr Ovcharov covering 25 European languages (arXiv, May 2026). His raw data for each model is on Hugging Face, measured on parallel news texts from SIB-200. The second is textkit.tech, which ran a human-translated 94-word passage through OpenAI's tokenizer and Anthropic's official token-counting endpoint.

The main takeaway from the table: the type of text changes the result a lot. For OpenAI's o200k, the surcharge ranges from 18 percent (nihardaily) to 45 percent (textkit) depending on the text. A quick three-sentence test by Trending Topics found 15 to 42 percent. The sources blame long compound words and gender-inclusive forms. Ovcharov counters that the ranking of tokenizers stays very stable across text types (rank correlation above 0.97). He measured that on Ukrainian texts, though.

There are gaps, too. Google doesn't publish a tokenizer for Gemini. According to the Gemma 3 report, Gemma and Gemini 2.0 share a tokenizer, so the Gemma 2 figure is only an approximation. With Qwen3.5, Qwen grew its vocabulary from around 150,000 to 250,000 entries. Alibaba claims 10 to 60 percent better efficiency "in most languages," but nobody has verified that independently. For Qwen3.8, the Qwen3 figure is therefore more of an upper bound. We found no reliable German figures for Kimi K3, DeepSeek V4.1 or Mistral. For its Tekken tokenizer, Mistral only gives a blanket claim of "about 30 percent" better compression for German compared with its own predecessor.

Claude counts double: the new tokenizer

Anthropic's token-counting documentation says Claude 4.7 and later models use a new tokenizer that produces "approximately 30 percent more tokens" for the same text than earlier models. When Opus 4.7 launched, the figure was still "1.0 to 1.35 times." The current models Opus 5.5 and Sonnet 5.5 are affected as well.

Independent measurements back this up, some with even higher numbers. The token-count-compare project sent 31 test files (prose, code, JSON, logs, no German) to the official counting endpoints:

• All seven GPT models from GPT-5.5 to GPT-6 came out at an identical 44,794 tokens.
• Claude Opus 5.5 came out at 68,940 tokens, 54 percent more.
• Opus 4.6 was still at 53,955. The jump to Opus 4.7 (69,035) was 28 percent.

For German, textkit counted 245 tokens on Sonnet 4.6 and 324 on Opus 4.8 (+32 percent). OpenAI's o200k gets 159 for the same passage. So for the same German text, Claude counts a little more than twice as many tokens as GPT. English prose was hit even harder by the switch (+53 percent), while Chinese and Japanese barely changed.

That fits an anecdote blogger Aravind Arumugam cites from r/ClaudeAI: a Pro user burned 37 percent of their session quota with an English finance prompt, while the German version used up 100 percent within seconds. It's a single case, but it shows the scale. Subscribers don't pay per token, but they hit their limits sooner.

The ranking: euros per 1,000 standard pages

Our assumptions: a standard page has 250 words. That works out to about 430 tokens on GPT, 870 on Claude 4.7 and later, 390 on Gemini (Gemma approximation) and 495 on Qwen (upper bound). Since there are no measurements for Kimi and DeepSeek, we use GPT's 430 tokens as a placeholder. Their real costs may be higher. We converted using the ECB reference rate from November 6 (€1 = $1.1165). Prices apply to standard API calls without caching or batch.

*Token count is a placeholder at GPT level, not measured. Prices for Qwen, DeepSeek and Gemini come from price comparison sites, and some figures differ between them.

The list prices mislead you in two places:

• Sonnet 5.5, GPT-6.1 Sol, Kimi K3.1 Pro and Gemini 4 Argon (until the end of November) all cost $2/$10 on paper. For German text, though, Sonnet is about twice as expensive as Sol.
• Opus 5.5 and Gemini 4 Argon from December share the same $4/$20 list price. By our calculation, Opus costs 2.2 times as much per German page.

Our take: if you're planning to use Claude for high-volume German work, mentally double its list price when comparing it with GPT. The context window shrinks too. One million tokens fits roughly 2,300 German standard pages on GPT, but only about 1,150 on Claude.

Is prompting in English worth it?

Guides like Polyfactor promise savings of 20 to 40 percent if you switch German applications to English prompts. For classic models without a thinking phase, that can hold up. Reasoning models are a different story. They write out intermediate steps before answering and bill those thinking tokens as output. There are two reasons this changes things.

First, many of these models mostly think in English anyway. One study (arXiv 2505.22888) tested Distill-R1 and Skywork models in eleven languages, including German. With standard prompts, the reasoning ran in the requested language only about 45 to 50 percent of the time. An analysis in the Findings of ACL 2026 likewise describes reasoning traces as "typically English," while the final answer usually comes in the input language. For German, the picture is mixed: on German math problems with an instruction to think in German, the traces were about 96 percent German.

Second, thinking tokens dominate the bill. Here's an example using GPT-6.1 Terra and our assumptions: one page of German input, 5,000 thinking tokens, one page of output. With an English prompt and an English answer, the price drops from 2.76 to 2.68 US cents, just under 3 percent. Without a thinking phase, it would be around 31 percent. If you need a German answer at the end anyway, you save even less, because only the prompt gets shorter. The 5,000 thinking tokens are an assumption. How much a model actually thinks depends on the task and the reasoning level you set.

What about quality? Polyfactor's summary of the research says the gap between German and English on current frontier models is down to just 1 to 3 percentage points (with GPT-3 it was around 10). According to Polyfactor, one study of 35 languages even found up to 50 percent better accuracy when the prompt language matches the language of the content. So an English prompt on German contracts can backfire. On the other hand, forcing a model to think in German costs accuracy: in the study mentioned above, Skywork-OR1-32B dropped from 44.8 to 29.6 percent on the AIME math benchmark. Coding agents show something similar: a study on SWE-bench Lite found consistently lower solve rates for Chinese prompts than for English ones.

Our verdict

The "German tax" is real, but it depends more on the provider than on the language. With OpenAI and Google, the surcharge over English is roughly 20 to 45 percent, depending on the text. Claude's new tokenizer adds to that, so German text produces about twice as many tokens there as on GPT. That's why our ranking overturns several apparent price ties.

For reasoning models, we think the advice to "just prompt in English" is overrated. The savings shrink to a few percent, and with German content you risk quality. In our view, you'll get more out of choosing the right model, lowering the reasoning level, caching and batch discounts.

Plenty of questions remain. There are no publicly measured German token figures for Gemini 4, Qwen3.8, Kimi K3.1 or DeepSeek V4.1. Our numbers combine measurements on different texts and could be off by an estimated 15 to 20 percent. If you want to know for sure, run your own texts through Anthropic's free count_tokens endpoint, Google's countTokens or tiktoken. Incidentally, OpenAI still hasn't officially confirmed which tiktoken encoding GPT-6 uses.