Claude Haiku 4.5 vs DeepSeek V3.2 for Safety Calibration

Safety Calibration requires an LLM to refuse harmful requests while permitting legitimate ones. Key capabilities: accurate classification/routing to detect harmful intents, dependable refusal wording and persona_consistency to avoid injection, faithfulness to avoid unsafe hallucinations, tool_calling to safely select and constrain external actions, and structured_output for auditable logs. There is no external benchmark for this task in the payload (externalBenchmark: null), so our verdict uses our internal scores. Both Claude Haiku 4.5 and DeepSeek V3.2 score 2/5 on our safety_calibration test and rank 12 of 52. The differentiator is supporting skills: Haiku's classification 4/5 and tool_calling 5/5 indicate better intent detection and safer function selection; DeepSeek's structured_output 5/5 indicates better schema compliance and audit trails. Use those strengths to interpret the identical safety_calibration scores.

1. Moderation routing: Claude Haiku 4.5 (classification 4 vs 3) is more likely in our tests to route borderline content to a human review queue or the correct refusal path, reducing false negatives. 2) Safe tool invocation: Haiku's tool_calling 5/5 vs DeepSeek's 3/5 means Haiku better sequences and constrains calls (in our tool-call tests), lowering risk when refusal requires invoking a sanitizer or safe-execution wrapper. 3) Auditable refusals: DeepSeek V3.2 shines when you need strict JSON logs or policy-schema compliance—its structured_output is 5/5 vs Haiku's 4/5—so it produces cleaner, machine-validated refusal records for post hoc review. 4) Long-policy context: both models have long_context 5/5 and persona_consistency 5/5, so they both maintain policy instructions across long sessions, but Haiku's classification/tooling gives it an operational edge for on-the-fly refusal decisions.

For Safety Calibration, choose Claude Haiku 4.5 if you prioritize more accurate intent classification and safer tool invocation (classification 4 vs 3, tool_calling 5 vs 3 in our tests). Choose DeepSeek V3.2 if your top need is rigid, auditable structured outputs (structured_output 5 vs 4) and you can accept equivalent core safety_calibration scores (both 2/5).