Claude Sonnet 4.6 vs R1 0528 for Faithfulness

Faithfulness requires the model to stick to source material without hallucinating, preserve factual claims and structure, and produce verifiable, reproducible output. Capabilities that matter: safety_calibration (avoiding invented or risky content), structured_output (JSON/format compliance so consumers can verify fields), long_context (keeping source context at 30K+ tokens), tool_calling (accurate function/argument selection when augmenting with retrieval or verification) and persona_consistency (to avoid injecting inconsistent facts). Primary evidence: in our testing both models score 5/5 on the Faithfulness task and are tied for 1st of 52. Supporting evidence: Claude Sonnet 4.6 has safety_calibration 5 versus R1 0528's 4, which reduces the chance of permissive or invented answers in edge cases. Both models score 5 on tool_calling and long_context and 4 on structured_output, but R1 0528's quirks state it can return empty responses on structured_output and that its reasoning tokens consume output budget on short tasks — behaviors that can undermine faithfulness in automated pipelines. Supplementary external points: Claude Sonnet 4.6 posts 75.2% on SWE-bench Verified (Epoch AI) in our data; R1 0528 posts 96.6% on MATH Level 5 (Epoch AI) and 66.4% on AIME 2025 (Epoch AI). Those external scores are task-specific and are supplementary, not replacements for our internal faithfulness measures.

1. Regulatory summary pipeline (must preserve exact language and citations): Choose Claude Sonnet 4.6. Both models score 5/5 on faithfulness, but Sonnet's safety_calibration 5 vs R1's 4 and lack of structured_output quirks reduce risk of hallucinated clauses or missing fields. 2) Low-cost, high-volume extraction from clean sources: Choose R1 0528 if budget matters — input/output cost per mTok is lower (R1 input $0.50/mTok, output $2.15/mTok vs Sonnet input $3/mTok, output $15/mTok). R1 matches Sonnet's 5/5 faithfulness in our tests but may return empty structured outputs on some prompts, so include validation. 3) Long-context fact-checking across 30K+ tokens: Both models score 5 for long_context and 5/5 faithfulness, so Sonnet is safer for strict fidelity (better safety_calibration) while R1 is a cost-efficient alternative if you add a validation layer to catch empty structured outputs. 4) Tool-augmented retrieval verification: Both score 5 on tool_calling in our tests, so either can be used; prefer Claude Sonnet 4.6 when the retrieval outputs will be programmatically relied on without additional human review.

For Faithfulness, choose Claude Sonnet 4.6 if you need the safest, most reliable output in structured or regulated pipelines (it scores 5/5 on faithfulness and has safety_calibration 5 vs R1's 4). Choose R1 0528 if you must minimize cost (R1 input $0.50/mTok, output $2.15/mTok) and can tolerate or programmatically detect its documented quirks (empty structured outputs, reasoning-token budget constraints).