Claude Haiku 4.5 vs Claude Opus 4.7 for Faithfulness

Faithfulness demands that an LLM stick to source material without hallucinating; it depends on accurate retrieval from long context, correct function/tool selection, precise structured output, robust refusal behavior, and reliable classification/routing when sources conflict. In our testing both Claude Haiku 4.5 and Claude Opus 4.7 receive the top faithfulness score (5/5), so the direct fidelity signal is a tie. To explain practical differences we look at related proxy benchmarks: both models score 5 on tool calling and 5 on long-context (supporting reliable extraction from large documents) and 4 on structured output (JSON/schema adherence). Differences that affect faithfulness in real workloads: Opus scores 4 on constrained rewriting (better at strict compression), while Haiku scores 3 there; Haiku scores higher on multilingual (5 vs 4) and classification (4 vs 3), which help preserve source meaning across languages and accurate routing. Safety calibration is 3 for Opus vs 2 for Haiku — relevant when faithfulness includes safe refusals. Because no external benchmark is provided, our internal 1–5 proxies are the primary evidence for this task.

• Low-cost production summarization of long documents: Choose Claude Haiku 4.5 — both models scored 5/5 for faithfulness and 5 for long-context, but Haiku costs $1 per million input tokens and $5 per million output tokens vs Opus at $5/$25, so Haiku gives identical fidelity at roughly 80% lower output cost.
• Multilingual compliance reports where sticking to source wording matters: Claude Haiku 4.5 is preferable (multilingual 5 vs Opus 4), reducing risk of mistranslation-induced hallucination.
• Strict character-limit transformations (e.g., headlines inside hard limits): Claude Opus 4.7 shines because constrained rewriting is 4 vs Haiku’s 3 — Opus is better at compression-within-limits while maintaining fidelity.
• Complex creative rewrites that still must remain factual: Opus has higher creative problem solving (5 vs Haiku 4) and slightly better safety calibration (3 vs 2), which can help when you need imaginative reformulation without inventing facts.
• Classification-driven source selection (automated routing to canonical sources): Haiku’s classification score (4 vs Opus 3) favors fewer misroutes and thus fewer downstream hallucinations.

For Faithfulness, choose Claude Haiku 4.5 if you need the same top-tier fidelity at much lower cost, plus stronger multilingual and classification support (Haiku is $1 input / $5 output per million tokens vs Opus $5 / $25). Choose Claude Opus 4.7 if your workload requires better constrained rewriting or higher creative problem-solving robustness (Opus: constrained rewriting 4 vs Haiku 3; creative problem solving 5 vs 4) and you can absorb the higher token costs.