R1 vs GPT-5.4 Nano

Summary of our 12-test comparison (scores are from our testing unless otherwise noted). Wins and ties: GPT-5.4 Nano wins structured_output (5 vs R1 4), classification (3 vs R1 2), long_context (5 vs R1 4), and safety_calibration (3 vs R1 1). R1 wins creative_problem_solving (5 vs Nano 4) and faithfulness (5 vs Nano 4). Tests that tied: strategic_analysis (5/5), constrained_rewriting (4/4), tool_calling (4/4), persona_consistency (5/5), agentic_planning (4/4), multilingual (5/5). Details and context: - Classification: GPT-5.4 Nano 3 vs R1 2; R1 sits near the bottom (rank 51 of 53) while Nano is in the midpack (rank 31 of 53) — expect better routing/categorization behavior from Nano in production. - Long context: Nano 5 vs R1 4; Nano is tied for 1st (tied with 36 others out of 55) while R1 ranks lower (rank 38 of 55). For retrieval and documents >30K tokens, Nano is more reliable in our tests. - Structured output: Nano 5 vs R1 4; Nano ties for 1st on schema adherence (rank 1 of 54) — better for strict JSON/formatting tasks. - Safety calibration: Nano 3 vs R1 1; Nano ranks ~10th of 55 while R1 ranks 32nd — Nano refuses harmful prompts more appropriately in our testing. - Creative problem solving & faithfulness: R1 5/5 vs Nano 4/5; R1 ties for top ranks on creative_problem_solving and ties for 1st on faithfulness (tied with many models), indicating stronger idea generation and sticking to source material. - Tool calling & agentic planning: both models score 4 and tie on these tasks; expect similar capability selecting functions and basic goal decomposition. External benchmarks (Epoch AI): R1 scores 93.1% on MATH Level 5 (Epoch AI) and 53.3% on AIME 2025 (Epoch AI); GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI). Use those external numbers as supplementary signals to the 1–5 internal tests.

Token pricing (per mTok): R1 input $0.70, output $2.50; GPT-5.4 Nano input $0.20, output $1.25 — a 2x priceRatio in the payload. Per million tokens (1,000 mTok) that means: input R1 $700 vs Nano $200; output R1 $2,500 vs Nano $1,250. If you assume a 50/50 split of input/output tokens, per million total tokens cost = R1 $1,600 (0.5M in + 0.5M out) vs Nano $725. Scaling: at 10M tokens/month (50/50) R1 ≈ $16,000 vs Nano ≈ $7,250; at 100M tokens/month R1 ≈ $160,000 vs Nano ≈ $72,500. Who should care: any high-volume app (search, large-scale assistants, automated summarization) will save roughly 50% on token bills with GPT-5.4 Nano; teams with small-scale, high-value prompts that prioritize idea novelty or strict faithfulness may accept R1's higher bill.

Choose GPT-5.4 Nano if: you need production-ready long-context understanding, strict structured output, better safety calibration, and much lower token costs — e.g., document Q&A over 30K tokens, high-volume chat, schema-driven APIs. Choose R1 if: you prioritize creative_problem_solving, strict faithfulness to source content, or higher MATH Level 5 performance (R1 93.1% on Epoch AI MATH Level 5) and can absorb ~2x token costs and weaker safety calibration.