Claude Haiku 4.5 vs R1 0528 for Safety Calibration

Safety Calibration requires correctly refusing harmful requests and allowing legitimate ones; key capabilities are calibrated refusal thresholds, precise classification/routing, and faithfulness to policy. Because no external benchmark is provided, we rely on our internal safety_calibration scores: Claude Haiku 4.5 = 2, R1 0528 = 4 (see taskScoreA/taskScoreB). Supporting signals: both models score 5 on faithfulness and 5 on tool_calling in our tests (useful for enforcing policy via tools), and both score 4 on structured_output — though R1’s documented quirks include empty responses on structured_output for short tasks, which can affect some safety pipelines. Use these internal scores as the primary evidence for the verdict.

1. High-volume content moderation (chat or comments): R1 0528 (score 4) is preferable — in our testing it more consistently refused clearly harmful prompts while allowing borderline but legitimate queries. Claude Haiku 4.5 (score 2) showed more permissive behavior in the same tests.
2. Moderation with strict post-processing and tools: both models have tool_calling = 5 and faithfulness = 5, so either can integrate with enforcement tooling; R1’s higher safety score means fewer borderline outputs to filter downstream, and its output cost (2.15 per mTok) is lower than Haiku’s (5 per mTok), reducing operational expense for high throughput.
3. JSON-labeled safety outputs for automated pipelines: both models have structured_output = 4, but R1 0528 has a known quirk (returns empty responses on structured_output in short tasks). In pipelines that require compact, guaranteed JSON labels, Claude Haiku 4.5 may be more predictable despite a lower safety_calibration score; however, Haiku’s lower safety score means you’ll need stronger downstream checks.
4. Edge cases and adversarial prompt attempts: R1 0528’s rank (6 of 52) and score (4) indicate better resistance to adversarial elicitation in our tests versus Haiku (rank 12, score 2).

For Safety Calibration, choose R1 0528 if you need reliable refusals for harmful requests, lower per-token output cost (2.15 vs 5), and fewer borderline outputs to filter. Choose Claude Haiku 4.5 if you prioritize Haiku’s larger context window (200k tokens) or need a model that behaves more predictably with structured output in short tasks — but plan to add stricter post-filters because it scored 2 on safety_calibration in our testing.