R1 0528 vs GPT-5.4

Summary of our 12-test suite: GPT-5.4 wins 3 tests (structured_output, strategic_analysis, safety_calibration); R1 0528 wins 2 (tool_calling, classification); 7 tests tie. Detailed walk-through: - Tool calling: R1 scores 5 vs GPT-5.4 4; R1 is tied for 1st on tool_calling (tied with 16 others), so expect more reliable function selection and argument accuracy in our tests. - Classification: R1 4 vs GPT-5.4 3; R1 is tied for 1st on classification, meaning better routing/categorization in practical flows. - Structured output: GPT-5.4 scores 5 vs R1 4; GPT-5.4 is tied for 1st on structured_output, indicating stronger JSON/schema compliance in our runs. - Strategic analysis: GPT-5.4 5 vs R1 4; GPT-5.4 ranks tied for 1st on strategic_analysis, so it handled nuanced tradeoffs and numeric reasoning better in our scenarios. - Safety calibration: GPT-5.4 5 vs R1 4; GPT-5.4 tied for 1st on safety_calibration, refusing harmful prompts while permitting legitimate ones more accurately in our tests. - Ties (both models equal): constrained_rewriting (4), creative_problem_solving (4), faithfulness (5), long_context (5), persona_consistency (5), agentic_planning (5), multilingual (5). External benchmarks (Epoch AI) add context: GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI) and 95.3% on AIME 2025 (Epoch AI, rank 2 of 12 and rank 3 of 23 respectively), indicating top-tier coding and olympiad-style math performance on those external tests. R1 0528 posts 96.6% on MATH Level 5 (Epoch AI) and 66.4% on AIME 2025 (Epoch AI, rank 5 of 14 and rank 16 of 23 respectively), showing exceptional MATH Level 5 results but weaker AIME performance. Operational quirks: R1’s quirks include empty responses on structured_output, constrained_rewriting, and agentic_planning unless high max-completion tokens are set, and it uses reasoning tokens that increase output consumption — important for production prompt engineering.

Per-token rates: R1 0528 charges $0.50 input / $2.15 output per mTok; GPT-5.4 charges $2.50 input / $15.00 output per mTok. If your workload is output-heavy (all tokens treated as output): for 1M tokens/month R1 costs $2,150 vs GPT-5.4 $15,000 (R1 saves $12,850). For 10M: R1 $21,500 vs GPT-5.4 $150,000. For 100M: R1 $215,000 vs GPT-5.4 $1,500,000. If tokens are split 50/50 input/output, net costs for 1M tokens are R1 $1,325 vs GPT-5.4 $8,750; for 10M: R1 $13,250 vs GPT-5.4 $87,500; for 100M: R1 $132,500 vs GPT-5.4 $875,000. Who should care: any high-volume consumer or SaaS product (10M+ tokens/month) will see large absolute dollar differences; enterprises needing multimodal, safety-first outputs may justify GPT-5.4’s higher spend, while startups and cost-sensitive pipelines should prefer R1 0528.

Choose R1 0528 if you need extreme cost efficiency, strong tool-calling, and classification (R1: tool_calling 5, classification 4) and can accommodate its quirks (set high max completion tokens and handle empty structured responses). Choose GPT-5.4 if you need highest-ranked strategic analysis, structured-output fidelity, and safety calibration (GPT-5.4: strategic_analysis 5, structured_output 5, safety_calibration 5), plus multimodal and massive context support — and you can afford substantially higher token costs.