Claude Sonnet 4.6 vs GPT-5.4 for Faithfulness

Faithfulness demands the model stick to source material, avoid hallucination, accurately cite or reproduce facts, and produce verifiable outputs. Key capabilities: tool calling (accurate function selection and arguments), structured output (schema adherence), classification/routing (correctly map source items), safety calibration (refuse unsupported claims), and long-context handling (retain source across many tokens). Because no external benchmark is present in the payload, our primary evidence is our internal testing: both models achieve the top faithfulness score of 5/5 in our 12-test suite. Supporting differences explain practical strengths: Claude Sonnet 4.6 has tool_calling = 5 and classification = 4, which help keep outputs aligned when integrating retrieval or external tools. GPT-5.4 has structured_output = 5, which helps when strict JSON or precise format fidelity is the priority. Both have safety_calibration = 5 and long_context = 5, so both resist making unsupported claims and retain long sources well.

1. Multi-step retrieval + citation workflow: Claude Sonnet 4.6 (faithfulness 5; tool_calling 5) is preferable — its higher tool_calling score (5 vs 4) suggests better function selection and argument accuracy when you chain retrieval or knowledge tools, reducing hallucinated claims. 2) API that requires strict JSON schemas for downstream parsing: GPT-5.4 is preferable (structured_output 5 vs Sonnet 4) because it scored higher on format adherence in our testing, lowering downstream parsing errors. 3) Large-document summarization with source fidelity: both models score faithfulness 5 and long_context 5, so either will retain and reproduce source material across 30K+ token contexts. 4) Classification-driven routing or fact-check gating: Claude Sonnet 4.6’s classification = 4 vs GPT-5.4’s 3 gives Claude an edge when the system must decide how to route or label source fragments before generating an answer. 5) Cost-sensitive deployments: GPT-5.4 has a slightly lower input cost (2.5 vs 3 per mTok); output cost is equal (15 per mTok). If identical faithfulness is required and budgets matter, this input-cost delta can influence choice.

For Faithfulness, choose Claude Sonnet 4.6 if your workflow relies on tool calling, retrieval chains, or better classification (tool_calling 5; classification 4). Choose GPT-5.4 if you need strict, machine-readable output formats and schema adherence (structured_output 5). Both models scored 5/5 for Faithfulness in our tests; pick based on the supporting strengths and input-cost differences.