Claude Haiku 4.5 vs R1 0528 for Translation

What Translation demands: faithful, fluent multilingual output plus preservation of meaning, register, and locale-specific phrases. Primary measures on this task are our multilingual and faithfulness benchmarks — both models score 5/5 in our testing. Secondary capabilities that affect real-world translation: long_context (retrieval across long documents), constrained_rewriting (character-limit localization), safety_calibration (refusing harmful or unsafe translations), modality (image→text for screenshot/caption translation), structured_output (JSON or CSV localization artifacts), and cost. Claude Haiku 4.5 and R1 0528 tie on the task's primary measures (5/5 each), so choice depends on secondary strengths: Haiku has a larger context window (200,000 tokens) and image->text modality; R1 0528 has higher safety_calibration (4 vs 2), better constrained_rewriting (4 vs 3), lower output cost ($2.15 vs $5 per mTok), but its quirks require attention (may return empty responses on structured_output/constrained_rewriting for short prompts and needs high max completion tokens).

Where Claude Haiku 4.5 shines: • Translating app screenshots, menus, or images — Haiku supports text+image→text so you can feed screenshots directly. • Very large single-document localization (200,000-token context and 64,000 max output tokens) — fewer chunking steps for long manuals. Where R1 0528 shines: • High-volume, cost-sensitive localization pipelines — equivalent translation quality in our tests (5/5) but lower output cost ($2.15 per mTok vs $5). • Regulated content or safety-sensitive translations — R1 has safety_calibration 4 vs Haiku's 2 in our testing, reducing unsafe outputs. • UI string compression/localization — R1 scores 4 for constrained_rewriting vs Haiku's 3, making it better at tight character budgets. Caveat for developers: R1’s quirks note that it may return empty responses on structured_output and constrained_rewriting for short tasks and requires higher max completion tokens; plan prompts and token budgets accordingly.

For Translation, choose Claude Haiku 4.5 if you need image-to-text translation or the largest single-document context (200k tokens) and higher max output tokens. Choose R1 0528 if you want the same translation quality at lower cost ($2.15 vs $5 per output mTok), better safety calibration (4 vs 2), and stronger constrained_rewriting for tight UI/localization work — but budget for higher max completion tokens and watch its structured_output quirks.