Claude Haiku 4.5 vs Devstral 2 2512 for Multilingual

What Multilingual demands: parity of quality across non-English languages, preserving nuance/meaning, consistent tone, correct formatting for local conventions, and reliable refusal/accept behaviour when content is sensitive. External benchmarks are not available for this comparison, so our primary evidence is our internal test results. On the Multilingual test both Claude Haiku 4.5 and Devstral 2 2512 score 5/5 and tie for rank 1 of 52 in our testing. Supporting capabilities explain real-world differences: Claude Haiku 4.5 scores higher on faithfulness (5 vs 4), classification (4 vs 3), tool_calling (5 vs 4), and persona_consistency (5 vs 4), which indicate better preservation of source meaning, more accurate routing/categorization of multilingual input, stronger function selection for language-specific tooling, and steadier tone across translations. Devstral 2 2512 scores higher on structured_output (5 vs 4) and constrained_rewriting (5 vs 3), useful when strict schema compliance or tight-length translations are required. Also weigh engineering factors: Devstral has a larger context window (262,144 vs 200,000 tokens) and lower input/output costs (input $0.4 / output $2 per mTok vs Claude’s input $1 / output $5 per mTok). Use these proxies together — the Multilingual test shows parity on raw language ability, but supporting benchmarks push our recommendation toward Haiku for fidelity-sensitive multilingual applications and toward Devstral for schema-heavy or cost-sensitive deployments.

1. Global customer support routing: Claude Haiku 4.5 — higher classification (4 vs 3) and faithfulness (5 vs 4) mean more accurate language detection, intent routing, and fewer meaning-changing errors when triaging tickets in multiple languages. 2) Medical or legal translation drafts: Claude Haiku 4.5 — faithfulness 5 vs 4 and persona_consistency 5 vs 4 reduce risk of misinterpretation across languages. 3) High-volume localized CSV/JSON exports: Devstral 2 2512 — structured_output 5 vs 4 and constrained_rewriting 5 vs 3 favor exact schema adherence and tight-length outputs; also cheaper at input $0.4 / output $2 per mTok for cost-sensitive pipelines. 4) Long bilingual document processing: Devstral 2 2512 — larger context window (262,144 vs 200,000) helps when working with very long source documents, while both models scored 5/5 on Multilingual in our tests. 5) Multimodal multilingual workflows: Claude Haiku 4.5 supports text+image->text modality (Devstral is text->text), useful if you need OCR or image context in non-English languages.

For Multilingual, choose Claude Haiku 4.5 if you need the highest fidelity, better classification/routing, stronger tool calling, and steadier persona across languages (Haiku wins 7 tests vs 2 in our 12-test suite). Choose Devstral 2 2512 if you need exact structured outputs or constrained-length multilingual output, larger context (262,144 tokens), or lower per‑token cost (input $0.4 / output $2 vs Claude’s $1 / $5). Note: both models score 5/5 on our Multilingual test and tie for rank 1 of 52 — the decision is driven by these supporting strengths and cost/context trade-offs.