Capabilities

Multilingual support

Claude excels at tasks across multiple languages, maintaining strong cross-lingual performance relative to English.

Overview

Claude demonstrates robust multilingual capabilities, with particularly strong performance in zero-shot tasks across languages. The model maintains consistent relative performance across both widely-spoken and lower-resource languages, making it a reliable choice for multilingual applications.

Note that Claude is capable in many languages beyond those benchmarked below. We encourage testing with any languages relevant to your specific use cases.

Performance data

Below are the zero-shot chain-of-thought evaluation scores for Claude models across different languages, shown as a percent relative to English performance (100%):

Language	Claude Opus 4.1¹	Claude Opus 4¹	Claude Sonnet 4.5¹	Claude Sonnet 4¹	Claude Haiku 4.5¹
English (baseline, fixed to 100%)	100%	100%	100%	100%	100%
Spanish	98.1%	98.0%	98.2%	97.5%	96.4%
Portuguese (Brazil)	97.8%	97.3%	97.8%	97.2%	96.1%
Italian	97.7%	97.5%	97.9%	97.3%	96.0%
French	97.9%	97.7%	97.5%	97.1%	95.7%
Indonesian	97.3%	97.2%	97.3%	96.2%	94.2%
German	97.7%	97.1%	97.0%	94.7%	94.3%
Arabic	97.1%	96.9%	97.2%	96.1%	92.5%
Chinese (Simplified)	97.1%	96.7%	96.9%	95.9%	94.2%
Korean	96.6%	96.4%	96.7%	95.9%	93.3%
Japanese	96.9%	96.2%	96.8%	95.6%	93.5%
Hindi	96.8%	96.7%	96.7%	95.8%	92.4%
Bengali	95.7%	95.2%	95.4%	94.4%	90.4%
Swahili	89.8%	89.5%	91.1%	87.1%	78.3%
Yoruba	80.3%	78.9%	79.7%	76.4%	52.7%

¹ With extended thinking.

These metrics are based on MMLU (Massive Multitask Language Understanding) English test sets that were translated into 14 additional languages by professional human translators, as documented in OpenAI's simple-evals repository. The use of human translators for this evaluation ensures high-quality translations, particularly important for languages with fewer digital resources.

Best practices

When working with multilingual content:

Provide clear language context: While Claude can detect the target language automatically, explicitly stating the desired input/output language improves reliability. For enhanced fluency, you can prompt Claude to use "idiomatic speech as if it were a native speaker."
Use native scripts: Submit text in its native script rather than transliteration for optimal results
Consider cultural context: Effective communication often requires cultural and regional awareness beyond pure translation

We also suggest following our general prompt engineering guidelines to better improve Claude's performance.

Language support considerations

Claude processes input and generates output in most world languages that use standard Unicode characters
Performance varies by language, with particularly strong capabilities in widely-spoken languages
Even in languages with fewer digital resources, Claude maintains meaningful capabilities

Prompt Engineering Guide

Master the art of prompt crafting to get the most out of Claude.

Prompt Library

Find a wide range of pre-crafted prompts for various tasks and industries. Perfect for inspiration or quick starts.

Overview

Note that Claude is capable in many languages beyond those benchmarked below. We encourage testing with any languages relevant to your specific use cases.

Performance data

Below are the zero-shot chain-of-thought evaluation scores for Claude models across different languages, shown as a percent relative to English performance (100%):

Language	Claude Opus 4.1¹	Claude Opus 4¹	Claude Sonnet 4.5¹	Claude Sonnet 4¹	Claude Haiku 4.5¹
English (baseline, fixed to 100%)	100%	100%	100%	100%	100%
Spanish	98.1%	98.0%	98.2%	97.5%	96.4%
Portuguese (Brazil)	97.8%	97.3%	97.8%	97.2%	96.1%
Italian	97.7%	97.5%	97.9%	97.3%	96.0%
French	97.9%	97.7%	97.5%	97.1%	95.7%
Indonesian	97.3%	97.2%	97.3%	96.2%	94.2%
German	97.7%	97.1%	97.0%	94.7%	94.3%
Arabic	97.1%	96.9%	97.2%	96.1%	92.5%
Chinese (Simplified)	97.1%	96.7%	96.9%	95.9%	94.2%
Korean	96.6%	96.4%	96.7%	95.9%	93.3%
Japanese	96.9%	96.2%	96.8%	95.6%	93.5%
Hindi	96.8%	96.7%	96.7%	95.8%	92.4%
Bengali	95.7%	95.2%	95.4%	94.4%	90.4%
Swahili	89.8%	89.5%	91.1%	87.1%	78.3%
Yoruba	80.3%	78.9%	79.7%	76.4%	52.7%

Best practices

When working with multilingual content:

Provide clear language context: While Claude can detect the target language automatically, explicitly stating the desired input/output language improves reliability. For enhanced fluency, you can prompt Claude to use "idiomatic speech as if it were a native speaker."

Use native scripts: Submit text in its native script rather than transliteration for optimal results

Consider cultural context: Effective communication often requires cultural and regional awareness beyond pure translation

We also suggest following our general prompt engineering guidelines to better improve Claude's performance.