Claude Sonnet 4.6 vs GPT-5.4 for Safety Calibration
Winner: Claude Sonnet 4.6. In our testing both Claude Sonnet 4.6 and GPT-5.4 score 5/5 on Safety Calibration and share the top rank (1 of 52). We pick Claude Sonnet 4.6 by a narrow margin because it scores higher on tool_calling (5 vs 4) and classification (4 vs 3), capabilities that directly support correct refusal/permit routing and triage in safety-sensitive flows. GPT-5.4 ties on core safety but leads on structured_output (5 vs 4) and constrained_rewriting (4 vs 3), which favors strict output formatting and compact disclaimers.
anthropic
Claude Sonnet 4.6
Benchmark Scores
External Benchmarks
Pricing
Input
$3.00/MTok
Output
$15.00/MTok
modelpicker.net
openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
No external benchmark is provided for Safety Calibration, so our verdict relies on internal testing. Safety Calibration requires reliably refusing harmful requests while permitting legitimate ones; that demands (a) accurate classification/routing of borderline prompts, (b) faithful adherence to refusal policies, (c) correct tool selection or escalation when enforcement APIs are needed, and (d) precise formatted outputs for automated policy enforcement. In our tests both models score 5/5 on safety_calibration and share the top rank (1 of 52). Supporting indicators differ: Claude Sonnet 4.6 scores 5 on tool_calling and 4 on classification (vs GPT-5.4's 4 and 3 respectively), which strengthens Sonnet's ability to call moderation/escalation tools and triage ambiguous requests. GPT-5.4 scores 5 on structured_output and 4 on constrained_rewriting (vs Sonnet's 4 and 3), favoring strict JSON/format compliance and compact policy messaging. Both models score 5 on faithfulness and persona_consistency, which helps avoid unsafe hallucinated justifications for refusals.
Practical Examples
- Automated moderation pipeline: Sonnet 4.6 (tool_calling 5 vs 4) is better at deciding when to call a moderation API or escalate to human review, reducing false permits in borderline cases. 2) Triage of ambiguous prompts: Sonnet's higher classification (4 vs 3) helps distinguish intent and choose refusal vs permit more reliably in our tests. 3) Policy-as-code enforcement: GPT-5.4 (structured_output 5 vs 4) is stronger when your enforcement layer requires exact JSON responses (allowed/denied/action fields) for automated ingestion. 4) UI-limited disclaimers: GPT-5.4's constrained_rewriting (4 vs 3) is preferable when you must compress nuanced refusals into tight character limits while preserving policy intent. 5) Cost/context tradeoff: Sonnet input cost is 3¢/mTok vs GPT-5.4 at 2.5¢/mTok; output cost is 15¢/mTok for both — factor this in for high-throughput enforcement logs.
Bottom Line
For Safety Calibration, choose Claude Sonnet 4.6 if you prioritize safer decisioning, tool-driven escalation, and better prompt triage (tool_calling 5, classification 4). Choose GPT-5.4 if you need rock-solid, machine-readable policy outputs and concise refusal messaging (structured_output 5, constrained_rewriting 4) or slightly lower input cost (2.5¢ vs 3¢/mTok). Both score 5/5 on Safety Calibration in our testing and share the top rank, so pick based on which supporting capability (tooling/triage vs strict formatting/compression) matters for your integration.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.