Claude Haiku 4.5 vs Claude Sonnet 4.6 for Faithfulness

What Faithfulness demands: Faithfulness requires the model to stick to source material without adding unsupported facts. Key capabilities are: safety_calibration (refusing or qualifying unsupported claims), long_context handling (so the model can reference long source documents accurately), tool_calling and structured_output (to retrieve and return exact data and follow schemas), and strong classification/persona_consistency to avoid drift. In our testing both models achieved a 5/5 faithfulness score, so the headline result is a tie. Because there is no single externalBenchmark flagged as primary in the payload, we treat our internal faithfulness score as the direct measure and use other internal metrics and available third-party scores as supporting evidence. Sonnet’s safety_calibration = 5 vs Haiku’s = 2, Sonnet’s context_window = 1,000,000 tokens vs Haiku’s = 200,000 tokens, and Sonnet includes SWE-bench Verified 75.2% and AIME 2025 85.8% (Epoch AI) as supplementary signals of robustness — all of which matter for real-world faithfulness when source length, refusal behavior, or multi-step tool usage are involved. Tool_calling and structured_output are equal (tool_calling 5, structured_output 4), so for short, well-scoped tasks both models match on basic factual adherence.

1. Long legal or technical synthesis: Sonnet 4.6 is superior. Its 1,000,000-token context window plus safety_calibration 5 reduce the chance of omission or invented facts when synthesizing or citing long source documents. 2) Multi-step agent that fetches facts: Sonnet edges ahead because safety_calibration 5 and tool_calling 5 together lower hallucination risk during retrieval and function sequencing. 3) Short customer-support factual answers: Claude Haiku 4.5 performs identically on faithfulness (5/5) while costing less (input_cost_per_mtok 1 vs 3, output_cost_per_mtok 5 vs 15) and offering lower latency — good for high-volume, short-context deployments. 4) Codebase reasoning where external verification matters: Sonnet’s supplementary external scores (SWE-bench Verified 75.2% and AIME 2025 85.8%, Epoch AI) provide extra confidence for complex correctness-sensitive tasks; Haiku lacks those reported external scores in the payload. 5) Schema-bound exports: both models share structured_output = 4 and tool_calling = 5, so either will meet JSON/schema fidelity needs for constrained outputs at small-to-medium scale.

For Faithfulness, choose Claude Haiku 4.5 if you need lower-cost, lower-latency faithful answers on short or well-scoped inputs and want the same 5/5 faithfulness rating at lower compute cost. Choose Claude Sonnet 4.6 if your workflows involve long source documents, safety-sensitive refusal behavior, or multi-step tool-driven retrieval — Sonnet pairs the 5/5 faithfulness score with safety_calibration 5 (vs Haiku’s 2), a 1,000,000-token context window (vs 200,000), and supporting external test scores (SWE-bench Verified 75.2% and AIME 2025 85.8% per Epoch AI).