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Note on Claude Models Occasionally Outputting Korean, Japanese, and Other Non-target Languages ​

Applies to: Developers Updated: 2026-07-02

This note explains the "language drift" phenomenon that occasionally occurs when using Claude models, its causes, and how to reduce such output.

Symptoms ​

When using the official Claude models, or calling Claude models through CloudRouter, the following occasionally occurs:

  • You ask for answers in Chinese, but the model suddenly mixes in Korean, Japanese, or English partway through
  • The first half is in Chinese, then the second half suddenly turns into Korean, Japanese, or another language
  • Language switching is more likely during code explanation, log analysis, long-context conversations, and multilingual material processing
  • The user did not actively request a language switch, yet the model's output language changed

Conclusion ​

This is not CloudRouter mixing in Korean, Japanese, or other-language content in the middle — it is the "language drift" phenomenon that occasionally occurs in the official Claude models themselves.

Large language models like Claude are trained on massive multilingual data. When generating content, the model does not first lock in one language and use it throughout; instead, it continually predicts the next most likely token based on the current context. When code, abbreviations, foreign terms, logs, or multilingual material appear in the context, or when there is a degree of sampling randomness, the model may slide from a Chinese context into English, Japanese, Korean, or other language spaces.

CloudRouter is primarily responsible for unified model access, routing, forwarding, billing, and stability assurance. It does not actively change Chinese content into Korean or Japanese, nor does it insert other-language content into model replies. CloudRouter keeps the model's returned content as-is, to avoid introducing new uncertainty by modifying the model's output without authorization.

Why language drift happens ​

1. The model's training data is itself a multilingual mix ​

Large models like Claude, GPT, and Gemini are all exposed to vast multilingual corpora, including Chinese, English, Japanese, Korean, code, web pages, papers, forums, documents, and more. These languages are not completely isolated; they coexist in the same model generation space.

When the model generates, it predicts the probability distribution of the next token based on context. So the model is not "only picking words from a Chinese vocabulary" — it is choosing from a huge multilingual candidate space.

2. Code, abbreviations, and variable names easily break language boundaries ​

A lot of language drift happens in the following scenarios:

  • Analyzing code or explaining error logs
  • Processing English variable names or API documentation
  • Contexts containing mixed Chinese-English content
  • User input containing common words like OK, error, token, model, response
  • Prompts containing multilingual examples

These look like "code" or "ordinary English words", but in the model's vector representation space they may relate to multiple language regions at once. If the probabilities of Chinese-, English-, Japanese-, and Korean-related tokens are very close, the model may — due to sampling randomness — pick another language direction.

3. The first drifting token affects subsequent output ​

Large models generate text token by token, moving forward. If at some step the model outputs a Japanese or Korean token, then at the next step it incorporates that token into the context, and the probability of continuing to output that language is further raised.

It's as if the model reaches a language fork during generation: it should continue down the Chinese path, but some token causes it to slip onto the Japanese or Korean path. Once drift begins, the next few sentences may keep going in that direction until a new semantic turning point lets it return to Chinese.

4. Sampling randomness amplifies this phenomenon ​

The model does not always pick only the highest-probability word; it selects among high-probability candidates according to its sampling strategy.

  • The lower the temperature, the more stable and conservative the output
  • The higher the temperature, the more divergent the output and the more likely language drift is
  • Long contexts, multilingual contexts, and code contexts further increase the probability of drift

How to reduce language drift ​

1. Use /config to set the language to Simplified Chinese ​

If you are using Claude Code or a Claude client that supports configuration, it's recommended to set the language to Simplified Chinese via /config. This can help the model establish a tendency toward Chinese output to some extent, reducing the probability of switching to other languages partway through.

Note that setting the language via /config can mitigate the problem but won't necessarily eliminate it 100%, because language drift can still be influenced by context, code content, and sampling strategy.

2. Explicitly require fixed Chinese output in the prompt ​

It's recommended to add explicit language constraints in the System Prompt or the user prompt:

text
You must always answer in Simplified Chinese. Unless the user explicitly
asks for a translation or to keep the original text, do not output English,
Japanese, Korean, or any other language. Even if the input contains code,
English terms, logs, or error messages, you must explain in Chinese.

A stronger-constraint version:

text
All answers must use Simplified Chinese. Code, commands, variable names,
function names, and error messages may keep their original text, but
explanations, analysis, summaries, and step descriptions must all be in
Chinese. Do not output English, Japanese, Korean, or any other language
outside of code blocks. If foreign-language content appears in the context,
you must translate or explain it in Chinese.

3. Lower the temperature ​

If you're calling the API, it's recommended to set the temperature lower, for example:

json
{
  "temperature": 0.2
}

For scenarios that need stable output — customer service, knowledge-base Q&A, code explanation, product descriptions, etc. — a high temperature is not recommended.

4. Wrap code and logs in code blocks where possible ​

If the input contains code, logs, errors, JSON, YAML, Shell commands, etc., it's recommended to place them all in code blocks:

text
Please explain the cause of the following error in Chinese:

​```bash
Error: connection timeout when requesting model endpoint
​```

This helps the model distinguish which content is raw material and which content is the body that needs to be explained in Chinese.

5. Re-anchor the language when drift occurs ​

If the model has already started outputting another language, you can simply append a line:

text
Please answer the previous message again in Simplified Chinese, and do not
use Japanese, Korean, or English.

If it happens repeatedly, it's recommended to start a new conversation and state the language requirement explicitly in the first prompt.

If the methods above still don't help and language drift occurs frequently, please contact our staff and we'll help investigate the specific cause.

Contact us ​