Claude Sonnet 4.6 vs R1 0528 for Safety Calibration

What Safety Calibration demands: the ability to refuse harmful or disallowed requests while permitting legitimate ones with minimal false positives and clear, safe alternatives. Key capabilities that matter: accurate refusal detection, nuanced justification for refusals, faithfulness (to avoid hallucinated safety claims), reliable tool calling and structured outputs for automated enforcement, and robust long-context handling when safety rules depend on prior conversation. In our data there is no external benchmark for this task (externalBenchmark is null), so the primary signal is our taskScore: Claude Sonnet 4.6 = 5, R1 0528 = 4. Supporting evidence: both models score 5 on faithfulness and 5 on tool_calling, which helps implement enforcement flows, but R1 0528's documented quirks (it returns empty responses on structured_output and constrained_rewriting and uses separate reasoning tokens) can undermine automated safety pipelines that rely on structured refusals or short outputs. Claude's high scores on agentic_planning (5) and long_context (5) further support complex, reproducible safety gating across extended dialogs.

1. High-assurance moderation pipeline: Claude Sonnet 4.6 (5/5) — refuses clearly harmful prompts, provides concise safe explanations, and supports long-context policy checks across a 1,000,000-token window. Use Claude when you need consistent, auditable refusals. 2) Cost-sensitive moderation at scale: R1 0528 (4/5) — good refusal behavior for many cases and lower costs (input 0.5 ¢/mtok, output 2.15 ¢/mtok) but watch for gaps: its quirks can return empty structured outputs, breaking automated JSON-based refusal logs. 3) Tool-integrated enforcement: both models have tool_calling=5 and faithfulness=5, so they can select enforcement actions reliably; Claude's lack of empty-structured-output quirks (per payload) makes it more robust for systems that expect machine-readable refusal records. 4) Edge cases and adversarial prompts: Claude's top safety_calibration score and tied leadership on related axes (agentic_planning, persona_consistency) indicate fewer false negatives on adversarial attempts; R1 may require additional wrapper checks or higher engineering effort to match that behavior.

For Safety Calibration, choose Claude Sonnet 4.6 if you need the strongest, most consistent refusal behavior and robust long-context safety checks (5/5 vs 4/5; ranks 1 vs 6 of 52). Choose R1 0528 if budget and lower per-token cost (input 0.5¢/mtok, output 2.15¢/mtok) are the priority and you can accept its 4/5 safety score plus engineering workarounds for its structured_output quirks.