Claude Haiku 4.5 vs Claude Sonnet 4.6 for Multilingual

What Multilingual demands: equivalent-quality output across non-English languages, robust handling of cultural nuance, faithful translations, consistent persona and formatting in target languages, safe refusals on harmful multilingual content, and the ability to operate on long multilingual contexts. Primary signals in our data: both Claude Haiku 4.5 and Claude Sonnet 4.6 score 5/5 on our Multilingual test (tied for 1st). Supporting capabilities that matter here include safety_calibration (refusal and permissive behavior across languages), faithfulness, persona_consistency, long_context, structured_output, and creative_problem_solving for idiomatic rewrites. In our testing Sonnet 4.6 has safety_calibration 5 vs Haiku 4.5's 2, and creative_problem_solving 5 vs 4 — these differences indicate Sonnet will better handle ambiguous or risky multilingual content and creative localization tasks. Sonnet also has documented external benchmark results (75.2% SWE-bench Verified and 85.8% AIME 2025 per Epoch AI) that supplement our internal scores; Haiku has no external scores in the payload, which is a relevant gap when you need third-party verification.

1. Safety-sensitive localization (legal, medical, compliance): Sonnet 4.6 — both models give equivalent-quality translations in our Multilingual test (5/5), but Sonnet's safety_calibration 5 vs Haiku's 2 reduces risk of unsafe or permissive outputs in non-English content. 2) Large multilingual document processing (books, long chat histories): Sonnet 4.6 — 1,000,000 context window vs Haiku's 200,000 helps preserve cross-document consistency and references. 3) Budget real-time multilingual chat (high throughput, lower latency): Claude Haiku 4.5 — matches Sonnet on Multilingual quality (5/5) but at lower per-mTok costs (input: 1 vs 3; output: 5 vs 15), making it the cost-efficient choice for high-volume conversational AI. 4) Idiomatic localization and creative rewriting in many languages: Sonnet 4.6 — creative_problem_solving 5 vs Haiku 4 supports better idiomatic adaptations. 5) When you want third-party verification: Sonnet 4.6 has SWE-bench Verified 75.2% and AIME 2025 85.8% (Epoch AI); Haiku has no external scores in the payload, so Sonnet is preferable if external benchmarks matter to stakeholders.

For Multilingual, choose Claude Haiku 4.5 if you need lower-cost, lower-latency multilingual inference and you can accept weaker safety calibration (input/output cost per mTok: 1/5). Choose Claude Sonnet 4.6 if you need the safest, most robust multilingual behavior, longer context, stronger creative localization, and third-party benchmark evidence (swebench_verified 75.2% and aime_2025 85.8% per Epoch AI), despite higher per-mTok costs (input/output cost per mTok: 3/15).