R1 0528 vs GPT-5.4 Nano

Summary (our 12-test suite): R1 0528 wins 5 tests, GPT-5.4 Nano wins 2, and 5 tests tie. R1 wins: tool_calling (R1 5 vs Nano 4) — R1 is tied for 1st on tool_calling ("tied for 1st with 16 other models out of 54 tested"), meaning it selects functions, arguments, and sequencing more reliably in our tasks; faithfulness (5 vs 4) — R1 is tied for 1st on faithfulness, so it sticks to source material with fewer hallucinations; classification (4 vs 3) — R1 is tied for 1st ("tied for 1st with 29 others"), so routing and categorization are more accurate in our tests; safety_calibration (4 vs 3) — R1 ranks higher (rank 6 of 55) and refuses harmful prompts more appropriately in our tests; agentic_planning (5 vs 4) — R1 is tied for 1st, giving stronger goal decomposition and recovery. GPT-5.4 Nano wins structured_output (5 vs 4) — Nano is tied for 1st on structured_output, so JSON/schema adherence is stronger for integrations that demand strict format compliance; and strategic_analysis (5 vs 4) — Nano is tied for 1st, showing better nuanced tradeoff reasoning in numeric scenarios. Ties: constrained_rewriting (4/4), creative_problem_solving (4/4), long_context (5/5), persona_consistency (5/5), multilingual (5/5) — both models match top-tier performance on these tasks in our tests. External benchmarks (Epoch AI) as supplementary signals: R1 0528 scores 96.6% on MATH Level 5 (Epoch AI), indicating very strong high-level math performance in that external test; GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), outperforming R1 on that specific math olympiad measure (R1: 66.4%). Practical takeaways: pick R1 for reliable tool-calling, faithfulness and classification; pick GPT-5.4 Nano for structured-output pipelines and strategic numeric reasoning. Note R1 0528 has implementation quirks in our testing: it can return empty responses on structured_output, constrained_rewriting, and agentic_planning and uses reasoning tokens that consume output budget on short tasks — these affect integration and cost.

Prices in the payload are per mTok: R1 0528 input $0.50 / mTok and output $2.15 / mTok; GPT-5.4 Nano input $0.20 / mTok and output $1.25 / mTok. If you count 1 mTok as 1,000 tokens, combined input+output cost per 1,000 tokens is $2.65 for R1 0528 and $1.45 for GPT-5.4 Nano. That scales to: 1M tokens ≈ $2,650 (R1) vs $1,450 (Nano) — a $1,200 monthly gap; 10M tokens ≈ $26,500 vs $14,500 — a $12,000 gap; 100M tokens ≈ $265,000 vs $145,000 — a $120,000 gap. Who should care: high-volume deployments, streaming APIs, and cost-sensitive startups will feel these differences immediately; small-volume prototypes or task-specific pipelines may prefer R1 0528’s higher accuracy despite the premium.

Choose R1 0528 if: you need top-tier tool calling, faithfulness, classification, or agentic planning in production integrations and you can absorb higher cost and R1’s quirks (empty responses on some structured outputs; reasoning tokens consume output budget). Specific cases: orchestration engines that call external functions, classification/routing services, and math-heavy pipelines (MATH Level 5: 96.6% Epoch AI). Choose GPT-5.4 Nano if: you need the lowest cost per token, multimodal inputs (text+image+file), strict structured-output compliance, or superior strategic/numeric reasoning (structured_output 5/5, strategic_analysis 5/5). Specific cases: high-volume chat or inference, image+text processing, or strict JSON schema outputs where cost efficiency matters.