R1 0528 vs GPT-5 Mini

We ran our 12-test suite and report wins/ties from our testing. R1 0528 wins: tool_calling (R1 5 vs GPT-5 Mini 3) — R1 is tied for 1st on tool_calling (tied with 16 others, rank display: tied for 1st) while GPT-5 Mini ranks 47/54; safety_calibration (R1 4 vs GPT-5 Mini 3) — R1 ranks 6/55, GPT-5 Mini ranks 10/55; agentic_planning (R1 5 vs GPT-5 Mini 4) — R1 tied for 1st, GPT-5 Mini rank 16/54. GPT-5 Mini wins: structured_output (GPT-5 Mini 5 vs R1 4) — GPT-5 Mini tied for 1st (tied with 24 others), R1 ranks 26/54; strategic_analysis (GPT-5 Mini 5 vs R1 4) — GPT-5 Mini tied for 1st, R1 ranks 27/54. The following tests tie (same numeric score in our testing): constrained_rewriting (4/4), creative_problem_solving (4/4), faithfulness (5/5), classification (4/4), long_context (5/5), persona_consistency (5/5), multilingual (5/5). Practical implications: R1's 5/5 on tool_calling and agentic_planning means it selects and sequences functions more reliably in our tests — critical for multi-step tool-driven agents and automation. GPT-5 Mini's 5/5 structured_output and 5/5 strategic_analysis mean it better follows strict JSON schemas and handles nuanced tradeoff reasoning in our tests — important for APIs that demand exact formats and for financial/analytical prompts. Note quirks: R1 0528 has a documented behavior of returning empty responses on structured_output, constrained_rewriting, and agentic_planning in some short tasks and uses reasoning tokens that consume output budget; test results reflect functionality but you must account for this quirk in production. On third-party math benchmarks (Epoch AI): MATH Level 5 — R1 96.6% vs GPT-5 Mini 97.8% (Epoch AI); AIME 2025 — R1 66.4% vs GPT-5 Mini 86.7% (Epoch AI); GPT-5 Mini also reports 64.7% on SWE-bench Verified (Epoch AI). These external scores supplement our internal results and show GPT-5 Mini leads on higher-difficulty contest math (AIME) and slightly on MATH Level 5 in Epoch AI data.

Using the payload prices (R1 0528: input $0.50/mTok, output $2.15/mTok; GPT-5 Mini: input $0.25/mTok, output $2.00/mTok) the combined per-mTok cost is $2.65 for R1 and $2.25 for GPT-5 Mini. Assuming 1,000,000 tokens = 1,000 mTok, monthly cost would be: R1 = $2,650; GPT-5 Mini = $2,250 (difference $400). At 10M tokens (10,000 mTok): R1 = $26,500; GPT-5 Mini = $22,500 (diff $4,000). At 100M tokens (100,000 mTok): R1 = $265,000; GPT-5 Mini = $225,000 (diff $40,000). Who should care: any team doing high-volume retrieval or embedding-style workloads (large input sizes) should note R1's input price is double GPT-5 Mini's ($0.50 vs $0.25/mTok). Conversely, if output tokens dominate cost, the gap is smaller (R1 output $2.15 vs GPT-5 Mini $2.00).

Choose R1 0528 if: you build agentic systems or multi-step tool chains where our tests show R1's tool_calling (5/5) and agentic_planning (5/5) advantages and stronger safety calibration (4/5) matter — accept higher input costs and account for R1's structured_output quirk. Choose GPT-5 Mini if: you need strict schema compliance, nuanced strategic analysis, or lower token costs for high-volume general purpose tasks — GPT-5 Mini scored 5/5 on structured_output and strategic_analysis in our testing and has lower input/output prices (input $0.25/mTok, output $2.00/mTok).