Claude Haiku 4.5 vs Devstral 2 2512 for Translation
Winner: Claude Haiku 4.5. In our testing Claude Haiku 4.5 scores 5.0 on the Translation task vs Devstral 2 2512's 4.5, and ranks 1/52 vs 28/52. Both models tie at multilingual (5 vs 5), but Claude Haiku 4.5 outperforms on faithfulness (5 vs 4) and persona consistency (5 vs 4), which matter most for accurate, tone-preserving translations. Devstral 2 2512 is stronger at structured output (5 vs 4) and constrained rewriting (5 vs 3), so it is preferable when strict JSON schemas or tight character limits are primary constraints. Cost note: Claude Haiku 4.5 uses input/output rates of $1 / $5 per mTok, while Devstral 2 2512 is cheaper at $0.40 / $2 per mTok.
anthropic
Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
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mistral
Devstral 2 2512
Benchmark Scores
External Benchmarks
Pricing
Input
$0.400/MTok
Output
$2.00/MTok
modelpicker.net
Task Analysis
What Translation demands: high multilingual competence, strict faithfulness to source meaning, tone/persona preservation, handling long documents, and sometimes strict structured output (e.g., localized JSON). Our primary measures for this task are the multilingual and faithfulness tests. No external benchmark is provided for Translation in the payload, so our taskScore is the primary signal: Claude Haiku 4.5 scores 5.0 vs Devstral 2 2512 at 4.5. Supporting internal metrics explain why: both models score 5 on multilingual and 5 on long_context (able to handle long documents), but Claude Haiku 4.5 scores 5 in faithfulness and 5 in persona_consistency (better literal accuracy and tone maintenance). Devstral 2 2512 scores 5 in structured_output and 5 in constrained_rewriting (better for strict schema adherence and tight character budgets). Other relevant differences: tool_calling favors Claude Haiku 4.5 (5 vs 4) for integrated workflows, while Devstral is materially cheaper per mTok (input $0.40/output $2 vs Haiku $1/$5), which matters for high-volume localization.
Practical Examples
- Legal contract translation (faithfulness-critical): Choose Claude Haiku 4.5 — faithfulness 5 vs 4 and persona_consistency 5 vs 4 reduce risk of mistranslation of obligations and tone; taskRank 1/52 vs 28/52. 2) Product UI localization with JSON output: Choose Devstral 2 2512 — structured_output 5 vs 4 and constrained_rewriting 5 vs 3 make it better at producing compact, schema-compliant locale files. 3) Long-document batch translation (manual + API pipeline): Both models handle long context (both score 5), but Claude Haiku 4.5's stronger tool_calling (5 vs 4) and faithfulness favor workflows that call validation or QA tools. 4) Cost-sensitive, high-volume localization: Devstral 2 2512 is cheaper (input $0.40 / output $2 per mTok) versus Claude Haiku 4.5 (input $1 / output $5 per mTok), so Devstral can cut operating cost when strict fidelity is less critical.
Bottom Line
For Translation, choose Claude Haiku 4.5 if you need the most accurate, tone-preserving translations and top-ranked faithfulness (scores: Translation 5.0, faithfulness 5, persona_consistency 5). Choose Devstral 2 2512 if you need strict structured outputs or tight-character rewrites (structured_output 5, constrained_rewriting 5) or if per-mTok cost is the primary constraint (Devstral input $0.40 / output $2 vs Haiku input $1 / output $5).
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.
For translation tasks, we supplement our benchmark suite with WMT/FLORES scores from Epoch AI, an independent research organization.