R1 0528 vs GPT-4o-mini

Across our 12-test suite, R1 0528 wins 9 categories, GPT‑4o‑mini wins none, and three are ties. Key head-to-heads (scores out of 5 unless noted):

• Long context: R1 5 vs GPT‑4o‑mini 4 — R1 is tied for 1st of 55 models on long_context (tied with 36 others). This matters for retrieval and document-level tasks at 30K+ tokens.
• Tool calling: R1 5 vs GPT‑4o‑mini 4 — R1 is tied for 1st of 54 on tool_calling; GPT‑4o‑mini ranks 18/54. Expect R1 to select and sequence functions more accurately in our tests.
• Agentic planning: R1 5 vs GPT‑4o‑mini 3 — R1 tied for 1st of 54 on agentic_planning; GPT‑4o‑mini ranks 42/54, so R1 better decomposes goals and recovers from failures in our scenarios.
• Faithfulness: R1 5 vs GPT‑4o‑mini 3 — R1 tied for 1st of 55; GPT‑4o‑mini ranks 52/55. In our testing R1 sticks to source material more reliably.
• Persona consistency & multilingual: R1 5 vs GPT‑4o‑mini 4 — R1 ties for 1st on persona_consistency and multilingual (high ranks), so it preserves character and non‑English parity better in our tests.
• Creative problem solving & constrained rewriting: R1 4 vs GPT‑4o‑mini 2/3 — R1 outperforms on novel, feasible ideas and compression tasks in our suite.
• Classification & safety_calibration & structured_output: tied at 4 vs 4 — both models performed equally on JSON/schema tasks, routing/classification, and safety refusals in our testing. External math benchmarks (Epoch AI): on MATH Level 5 (Epoch AI) R1 scores 96.6% vs GPT‑4o‑mini 52.6%; R1 ranks 5/14 vs GPT‑4o‑mini 13/14. On AIME 2025 (Epoch AI) R1 66.4% vs GPT‑4o‑mini 6.9% (R1 ranks 16/23, GPT‑4o‑mini 21/23). These external results explain why R1 is markedly stronger on math/competition tasks in practice. Caveats: R1 has operational quirks in the payload — it returns empty responses on structured_output, constrained_rewriting, and agentic_planning in some conditions, uses reasoning tokens that consume output budget on short tasks, and requires high max completion tokens (min_max_completion_tokens: 1000). Factor these into integration and cost planning.

Token pricing (per 1k tokens): R1 0528 input $0.50, output $2.15; GPT‑4o‑mini input $0.15, output $0.60. That makes R1's output tokens 2.15/0.60 = 3.583x more expensive (priceRatio 3.5833 in the payload). Practical costs (per model):

• Per 1M tokens: R1 input $500, output $2,150, combined 1M in + 1M out = $2,650. GPT‑4o‑mini input $150, output $600, combined = $750.
• Per 10M tokens: R1 combined = $26,500; GPT combined = $7,500.
• Per 100M tokens: R1 combined = $265,000; GPT combined = $75,000. Who should care: teams generating large volumes of output tokens (chatbots that synthesize long replies, document generation, or vector store re-renders) will see R1 drive much higher monthly spend; price-sensitive startups and consumer apps should prefer GPT‑4o‑mini to control costs. R1’s higher price can be justified if its superior benchmark performance (long context, tool calling, math) materially improves product quality.

Choose R1 0528 if you need best-in-suite long-context retrieval, tool calling, agentic planning, faithfulness, multilingual parity, or high math performance (R1 wins 9 of 12 tests and scores 96.6% on MATH Level 5, Epoch AI). Accept higher token costs and accommodate R1’s quirks (empty responses on some structured tasks, high min completion tokens). Choose GPT‑4o‑mini if you need multimodal input (text+image+file), much lower token cost (output $0.60 vs R1 $2.15 per 1k), a large max_output_tokens (16,384), or are building high-volume consumer features where price dominates. GPT‑4o‑mini ties R1 on classification, structured output, and safety calibration in our tests.