multilingual-support changedbuild-with-claude/multilingual-support
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4141
4242The following table shows zero-shot chain-of-thought evaluation scores for Claude models across languages, expressed as a percentage relative to English performance (100%):
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44| Language | Claude Sonnet 4.5 (deprecated)1 | Claude Haiku 4.51 |
45| --------------------------------- | ------------------------------- | ----------------- |
46| English (baseline, fixed to 100%) | 100% | 100% |
47| Spanish | 98.2% | 96.4% |
48| Portuguese (Brazil) | 97.8% | 96.1% |
49| Italian | 97.9% | 96.0% |
50| French | 97.5% | 95.7% |
51| Indonesian | 97.3% | 94.2% |
52| German | 97.0% | 94.3% |
53| Arabic | 97.2% | 92.5% |
54| Chinese (Simplified) | 96.9% | 94.2% |
55| Korean | 96.7% | 93.3% |
56| Japanese | 96.8% | 93.5% |
57| Hindi | 96.7% | 92.4% |
58| Bengali | 95.4% | 90.4% |
59| Swahili | 91.1% | 78.3% |
60| Yoruba | 79.7% | 52.7% |
44| Language | Claude Sonnet 4.5 (deprecated)<sup>1</sup> | Claude Haiku 4.5<sup>1</sup> |
45| --------------------------------- | ------------------------------------------ | ---------------------------- |
46| English (baseline, fixed to 100%) | 100% | 100% |
47| Spanish | 98.2% | 96.4% |
48| Portuguese (Brazil) | 97.8% | 96.1% |
49| Italian | 97.9% | 96.0% |
50| French | 97.5% | 95.7% |
51| Indonesian | 97.3% | 94.2% |
52| German | 97.0% | 94.3% |
53| Arabic | 97.2% | 92.5% |
54| Chinese (Simplified) | 96.9% | 94.2% |
55| Korean | 96.7% | 93.3% |
56| Japanese | 96.8% | 93.5% |
57| Hindi | 96.7% | 92.4% |
58| Bengali | 95.4% | 90.4% |
59| Swahili | 91.1% | 78.3% |
60| Yoruba | 79.7% | 52.7% |
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621 With [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking).
62<sup>1</sup> With [extended thinking](https://platform.claude.com/docs/en/build-with-claude/extended-thinking).
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6464<Note>
6565 These metrics are based on [MMLU (Massive Multitask Language Understanding)](https://en.wikipedia.org/wiki/MMLU) English test sets that were translated into 14 additional languages by professional human translators, as documented in [OpenAI’s simple-evals repository](https://github.com/openai/simple-evals/blob/main/multilingual_mmlu_benchmark_results.md). The use of human translators for this evaluation ensures high-quality translations, particularly important for languages with fewer digital resources.
No line in this hunk matches that.