R1 vs GPT-5.4 Mini

Overview: GPT-5.4 Mini wins four benchmarks (structured_output, classification, long_context, safety_calibration); R1 wins one (creative_problem_solving); seven tests tie. Detailed walk-through: - Structured output: GPT-5.4 Mini scores 5 vs R1's 4. GPT-5.4 Mini is tied for 1st (tied with 24 others out of 54) versus R1 at rank 26 of 54 — this matters when you need strict JSON/schema compliance for integrations. - Classification: GPT-5.4 Mini 4 vs R1 2; GPT-5.4 Mini is tied for 1st (tied with 29 others) while R1 ranks 51 of 53 — choose GPT-5.4 Mini for routing, tagging, or automated decisioning. - Long context: GPT-5.4 Mini 5 vs R1 4; GPT-5.4 Mini is tied for 1st (tied with 36 others out of 55) while R1 ranks 38 of 55 — GPT-5.4 Mini will be more reliable on retrieval and reasoning across 30K+ token contexts. - Safety calibration: GPT-5.4 Mini 2 vs R1 1; GPT-5.4 Mini ranks 12 of 55 vs R1 at 32 of 55 — GPT-5.4 Mini better balances refusal of harmful requests and allowing legitimate ones. - Creative problem solving: R1 5 vs GPT-5.4 Mini 4; R1 is tied for 1st (tied with 7 others) while GPT-5.4 Mini ranks 9 of 54 — R1 produces more non-obvious, feasible ideas in our tests. - Ties/showing parity: strategic_analysis (both 5, tied for 1st), constrained_rewriting (both 4, rank 6), tool_calling (both 4, rank 18), faithfulness (both 5, tied for 1st), persona_consistency (both 5, tied for 1st), agentic_planning (both 4, rank 16), multilingual (both 5, tied for 1st). These ties indicate similar performance on reasoning, faithfulness, persona, and multilingual tasks. Supplementary external math results for R1: on MATH Level 5 (Epoch AI) R1 scores 93.1% (rank 8 of 14) and on AIME 2025 (Epoch AI) it scores 53.3% (rank 17 of 23) — helpful if you evaluate math/competition-style capability specifically.

Per-model list rates (per mTOK): R1 input $0.70, output $2.50; GPT-5.4 Mini input $0.75, output $4.50. The payload's priceRatio (0.5556) means R1 costs ~55.6% of GPT-5.4 Mini on a per-mTOKEN basis. Using mTOK = 1,000 tokens and assuming a 50/50 split of input/output tokens: - 1M tokens total: R1 ≈ $1,600; GPT-5.4 Mini ≈ $2,625. - 10M tokens: R1 ≈ $16,000; GPT-5.4 Mini ≈ $26,250. - 100M tokens: R1 ≈ $160,000; GPT-5.4 Mini ≈ $262,500. If workloads are output-heavy (e.g., 80% output tokens), the GPT-5.4 Mini premium grows (1M tokens, 80/20 output/input: R1 ≈ $2,140; GPT-5.4 Mini ≈ $3,750). Who should care: high-volume product teams and API-heavy businesses — at tens of millions of tokens the absolute dollar gap becomes material; hobbyists and low-volume users will feel it less but still benefit from R1's lower rates.

Choose GPT-5.4 Mini if you need: strict structured outputs/JSON, top-tier classification/routing, robust long-context retrieval (30K+ tokens), or stronger safety calibration — it wins those benchmarks and ranks at or near 1st. Choose R1 if you need: a lower-cost model (≈55.6% of GPT-5.4 Mini per-mTOK), better creative problem-solving, and strong faithfulness/strategic reasoning; ideal for high-throughput, cost-sensitive creative assistants and teams who can accommodate R1's quirks (reasoning tokens, high min completion tokens).