Claude Haiku 4.5 vs Devstral 2 2512 for Faithfulness

What Faithfulness demands: produce outputs that stick to source material, avoid invented facts, and preserve factual relationships when summarizing, citing, or transforming content. Capabilities that matter most: reliable long-context retrieval (to access the source), tool calling or grounding (to fetch/verbatim source), persona_consistency (to avoid injected claims), and structured_output (to enforce exact formats). External benchmarks are not present for this comparison, so our internal faithfulness test is the primary measure. In our testing Claude Haiku 4.5 scores 5/5 on the Faithfulness task (taskScoreA: 5) while Devstral 2 2512 scores 4/5 (taskScoreB: 4). Supporting diagnostics: Haiku leads on tool_calling (5 vs 4) and persona_consistency (5 vs 4), both relevant to refusing to hallucinate or invent claims; long_context is equal (5 vs 5), so retrieval distance is not the differentiator. Devstral’s structured_output is stronger (5 vs 4), meaning it will better follow strict JSON/schema constraints even when its content faithfulness is slightly weaker.

1. Multi-source summarization for legal excerpts: Choose Claude Haiku 4.5. In our tests Haiku’s faithfulness score (5) plus tool_calling 5 mean it's less likely to introduce unsupported claims when synthesizing multiple long documents. 2) API-driven citation generation (strict JSON schema): Choose Devstral 2 2512 when exact format matters. Devstral’s structured_output 5 helps produce schema-compliant citations, but expect a slightly higher risk of minor hallucinations (faithfulness 4 vs Haiku’s 5). 3) Long-context fact retrieval (30K+ tokens): Both models score long_context 5, so either can retrieve distant evidence; prefer Haiku if the priority is factual fidelity (5 vs 4). 4) Cost-sensitive faithful pipelines: Devstral is cheaper (input_cost_per_mtok 0.4, output_cost_per_mtok 2) versus Haiku (input_cost_per_mtok 1, output_cost_per_mtok 5). If you can tolerate one fewer faithfulness point to save on inference cost, Devstral is the practical choice.

For Faithfulness, choose Claude Haiku 4.5 if you need the highest fidelity to source material (5/5 in our tests, ranked 1st). Choose Devstral 2 2512 if you need strict structured output and lower inference cost (structured_output 5, input_cost_per_mtok 0.4 / output_cost_per_mtok 2) and can accept a slightly lower faithfulness score (4/5).