Claude Haiku 4.5 vs Claude Opus 4.6 for Faithfulness

Faithfulness demands that an AI stick to source material, avoid inventing facts, and correctly attribute or refuse requests when the source is missing or ambiguous. Key capabilities that matter: safety_calibration (refuse or correct unsupported claims), long_context (recall source material across large documents), structured_output (accurate, schema-compliant extraction), tool_calling (fetch and verify source evidence), and persona_consistency/classification (stay aligned to source voice and route to correct data). In our testing, both Claude Opus 4.6 and Claude Haiku 4.5 achieved top faithfulness scores (5/5, tied). Because there is no single PRIMARY externalBenchmark flagged in the payload, we base the primary verdict on our internal task score and supporting internal metrics. Opus’s 5/5 safety_calibration vs Haiku’s 2/5 is the decisive internal signal for lower hallucination risk and better refusal behavior. Additionally, Claude Opus 4.6 posts external third-party results in the payload—78.7% on SWE-bench Verified and 94.4% on AIME 2025 (Epoch AI)—which we cite as supplementary evidence of robustness in related verification-heavy tasks. Tool_calling (both 5/5), long_context (both 5/5), and structured_output (both 4/5) show both models are capable at the mechanical aspects of staying faithful; safety_calibration is where Opus clearly outperforms Haiku in our tests.

1. High-stakes policy or legal summary: Opus 4.6 — In our testing Opus’s 5/5 safety_calibration reduces the chance of confident but unsupported assertions; its 1,000,000-token context window also helps when summarizing long statutes. Expect higher cost: input 5 and output 25 per mTok. 2) Long technical document extraction where schema matters: Tie — both models score 5/5 faithfulness, 5/5 tool_calling, and 4/5 structured_output in our tests, so either will reliably extract cited lines; choose Haiku if cost/speed is critical. 3) Customer-facing knowledge base where refusal behavior matters (ambiguous queries, outdated source): Opus 4.6 — Opus’s stronger safety_calibration (5 vs 2) means it is more likely in our testing to flag missing evidence or refuse to invent details. 4) Bulk post-processing of PDFs for research where budget dominates: Claude Haiku 4.5 — same 5/5 faithfulness and a much lower cost profile (input 1, output 5 per mTok) and still a 200,000-token context window, making it the pragmatic choice when volume and latency matter. 5) Code or math verification workflows: Opus 4.6 has supplemental external scores included in the payload—78.7% on SWE-bench Verified and 94.4% on AIME 2025 (Epoch AI)—which we treat as supporting evidence for stronger verification performance in related tasks.

For Faithfulness, choose Claude Haiku 4.5 if you need a lower‑cost, high‑throughput option that still scores 5/5 on faithfulness in our testing and offers excellent tool_calling and long_context support. Choose Claude Opus 4.6 if you need the safest refusal and lowest hallucination risk in high‑stakes, verification, or ambiguous‑source workflows—Opus adds a 5 vs 2 safety_calibration advantage in our tests and is supported by 78.7% on SWE-bench Verified and 94.4% on AIME 2025 (Epoch AI).