• Messages
  • Managed Agents
  • Admin

Search...
⌘K
First steps
Intro to ClaudeQuickstart
Building with Claude
Features overviewUsing the Messages APIStop reasons and fallbackRefusals and fallbackFallback credit
Model capabilities
Extended thinkingAdaptive thinkingEffortTask budgets (beta)Fast mode (research preview)Structured outputsCitationsStreaming MessagesBatch processingSearch resultsStreaming refusalsMultilingual supportEmbeddings
Tools
OverviewHow tool use worksTutorial: Build a tool-using agentDefine toolsHandle tool callsParallel tool useTool Runner (SDK)Strict tool useTool use with prompt cachingServer toolsTroubleshootingWeb search toolWeb fetch toolCode execution toolAdvisor toolMemory toolBash toolComputer use toolText editor tool
Tool infrastructure
Tool referenceManage tool contextTool combinationsTool searchProgrammatic tool callingFine-grained tool streaming
Context management
Context windowsCompactionContext editingPrompt cachingMid-conversation system messagesBuild an orchestration modeCache diagnostics (beta)Token counting
Working with files
Files APIPDF supportImages and vision
Skills
OverviewQuickstartBest practicesSkills for enterpriseSkills in the API
MCP
Remote MCP serversMCP connector
Claude on cloud platforms
Amazon BedrockAmazon Bedrock (legacy)Claude Platform on AWSMicrosoft FoundryVertex AI

Log in
Multilingual support
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...

Solutions

  • AI agents
  • Code modernization
  • Coding
  • Customer support
  • Education
  • Financial services
  • Government
  • Life sciences

Partners

  • Claude on AWS
  • Google Cloud's Vertex AI

Learn

  • Blog
  • Courses
  • Use cases
  • Connectors
  • Customer stories
  • Engineering at Anthropic
  • Events
  • Powered by Claude
  • Service partners
  • Startups program

Company

  • Anthropic
  • Careers
  • Economic Futures
  • Research
  • News
  • Responsible Scaling Policy
  • Security and compliance
  • Transparency

Learn

  • Blog
  • Courses
  • Use cases
  • Connectors
  • Customer stories
  • Engineering at Anthropic
  • Events
  • Powered by Claude
  • Service partners
  • Startups program

Help and security

  • Availability
  • Status
  • Support
  • Discord

Terms and policies

  • Privacy policy
  • Responsible disclosure policy
  • Terms of service: Commercial
  • Terms of service: Consumer
  • Usage policy
Messages/Model 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.

Claude is capable in many languages beyond those benchmarked in the following table. Test with any languages relevant to your specific use cases.

Performance data

The following table shows zero-shot chain-of-thought evaluation scores for Claude models across languages, expressed as a percentage relative to English performance (100%):

LanguageClaude Opus 4.1 (deprecated)1Claude Sonnet 4.51Claude Haiku 4.51
English (baseline, fixed to 100%)100%100%100%
Spanish98.1%98.2%96.4%
Portuguese (Brazil)97.8%97.8%96.1%
Italian97.7%97.9%96.0%
French97.9%97.5%95.7%
Indonesian97.3%97.3%94.2%
German97.7%97.0%94.3%
Arabic97.1%97.2%92.5%
Chinese (Simplified)97.1%96.9%94.2%
Korean96.6%96.7%93.3%
Japanese96.9%96.8%93.5%
Hindi96.8%96.7%92.4%
Bengali95.7%95.4%90.4%
Swahili89.8%91.1%78.3%
Yoruba80.3%79.7%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.


Set the response language

Claude infers the response language from the conversation, but for production applications you should state the target language explicitly. The most reliable place to do this is the system prompt, which keeps the instruction stable across every turn of a conversation.

client = anthropic.Anthropic()

message = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    system="Always respond in French, regardless of the language the user writes in.",
    messages=[{"role": "user", "content": "How do I reset my password?"}],
)

print(message.content)

If your application lets users pick a language at runtime, interpolate that choice into the system prompt rather than relying on Claude to infer it from the user's message. To translate between two specific languages, name both: Translate the user's message from German to Korean. Respond with only the translation.


Best practices

When working with multilingual content:

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

Also follow the general guidance in Prompt engineering overview to further improve output quality.


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.

Next steps


Prompt engineering overview

Apply general prompting techniques to improve multilingual output quality.

Customer support agent

Build a localized support chatbot using a language-constrained system prompt.

Models overview

Compare model tiers to balance multilingual quality against cost and latency.

Define success criteria and build evaluations

Evaluate translation and localization quality before you ship.

Was this page helpful?

  • Overview
  • Performance data
  • Set the response language
  • Best practices
  • Language support considerations
  • Next steps