R1 0528 vs GPT-5.4 for Math

What Math requires: precise symbolic and numeric reasoning, multi-step proof tracing, faithful intermediate steps, optional tool access (calculators, CAS) for heavy computation, and reliable structured outputs for graders or downstream systems. Because an authoritative external benchmark is available, we treat MATH Level 5 (Epoch AI) as the primary measure: R1 0528 scores 96.6% on that benchmark (Epoch AI) — the best direct signal for contest-style, higher-difficulty math in this payload. Supporting internal metrics explain why: R1 0528 scores 5/5 on tool_calling, 5/5 on faithfulness, and 5/5 on long_context in our tests, which align with robust stepwise reasoning and working-memory needs. GPT-5.4 lacks a MATH Level 5 score in the payload, but it posts 95.3% on AIME 2025 (Epoch AI) and wins internally on structured_output (5/5) and strategic_analysis (5/5). Important caveats: R1 0528’s quirks include empty responses on structured_output and uses 'reasoning tokens' that consume output budget and require high max completion tokens — this affects short-format structured tasks. Price, context window, and I/O costs also matter: R1 0528 has a 163,840-token window and much lower per-mtok costs (input 0.5, output 2.15) compared with GPT-5.4’s 1,050,000+ token window and higher costs (input 2.5, output 15).

1. High-difficulty contest problem set (MATH Level 5 style): R1 0528 shines — it scored 96.6% on MATH Level 5 (Epoch AI) in our data, so expect strong correctness and stepwise solutions for competition problems. 2) AIME-style timed multiple-answer problems: GPT-5.4 shows a clear advantage on AIME 2025 (Epoch AI) at 95.3% vs R1 0528’s 66.4% — choose GPT-5.4 for AIME-specific preparation or formats similar to that benchmark. 3) Grader-facing JSON or strict schema output (automated scoring pipelines): GPT-5.4 is stronger — structured_output 5/5 in our tests and R1 0528 has a quirk that returns empty responses on structured_output. 4) Long, multi-step proofs or chain-of-thought requiring large working memory: R1 0528’s long_context 5/5 and faithfulness 5/5 are advantageous, but account for R1’s 'reasoning tokens' consuming output budget (set high max completion tokens). 5) Cost-sensitive bulk problem generation: R1 0528 is much cheaper per mtok (input 0.5, output 2.15) than GPT-5.4 (input 2.5, output 15), so for large-scale datasets R1 reduces compute spend while retaining high MATH Level 5 performance.

For Math, choose R1 0528 if you need top MATH Level 5 performance (96.6% on Epoch AI), lower per-token cost, strong tool calling, and long-context stepwise solutions — but avoid R1 when you require strict structured_output JSON (it returns empty on structured_output) or you cannot allocate large completion budgets. Choose GPT-5.4 if your primary need is strict schema-compliant output, strategic analysis with structured JSON (structured_output 5/5), or AIME-style performance (95.3% on AIME 2025, Epoch AI); be prepared for substantially higher per-token costs and very large context windows.