R1 0528 vs GPT-5.4 for Faithfulness

Faithfulness demands that an AI stick to source material without inventing facts, preserve exact structure when required, and refuse or qualify answers when information is missing. Capabilities that matter: - Context window size: larger windows reduce dropped context and out‑of‑scope extrapolation (GPT-5.4: 1,050,000 vs R1 0528: 163,840). - Structured output compliance: JSON/schema fidelity prevents format‑induced errors (GPT-5.4 structured_output 5 vs R1 4). - Safety calibration: correct refusal/qualification prevents confident hallucinations in unsupported areas (GPT-5.4 safety_calibration 5 vs R1 4). - Tool calling and argument accuracy: correct function selection and precise arguments avoid downstream incorrect behavior (R1 0528 tool_calling 5 vs GPT-5.4 4). - Reasoning behavior and quirks: R1 0528 is a reasoning_model and uses reasoning tokens (which consume output budget) and has a quirk that it returns empty responses on structured_output; those behaviors affect faithfulness in short or strict formatting tasks. All faithfulness claims above are drawn from our internal benchmarks and the models' reported scores.

1. Long, safety‑sensitive research summary: GPT-5.4 is preferable — both models scored 5/5 on faithfulness in our tests, but GPT-5.4’s safety_calibration (5 vs 4) and 1,050,000 token context window reduce hallucination risk when checking and quoting long sources. 2) Strict JSON ingestion pipeline: GPT-5.4 scores structured_output 5 vs R1 4 in our testing, so it more reliably meets schema requirements; note R1 0528 declares a quirk that can return empty responses on structured_output, which can break pipelines. 3) Orchestrating precise API calls or multi‑step tool sequences: R1 0528 shines (tool_calling 5 vs GPT‑5.4 4) — in our tests it selects and sequences functions with higher argument accuracy, which prevents downstream errors even if its structured output score is lower. 4) Cost‑sensitive production: R1 0528 is far cheaper per the listed rates (output_cost_per_mtok $2.15 vs GPT-5.4 $15.00), so for high‑volume faithful extraction where tool calling is critical and strict JSON schemas are not, R1 may be the better operational choice.

For Faithfulness, choose R1 0528 if you need the lowest cost and the best tool_calling accuracy for multi‑step, API‑driven workflows (R1 output_cost_per_mtok $2.15 vs GPT-5.4 $15.00; tool_calling 5 vs 4). Choose GPT-5.4 if you require the strongest safety refusals and schema fidelity across very long contexts (faithfulness tied 5/5, but GPT-5.4 has safety_calibration 5 vs 4, structured_output 5 vs 4, and a 1,050,000 token context window).