R1 0528 vs GPT-4.1 Nano

Overview — in our 12-test suite R1 0528 wins 9 categories, GPT‑4.1 Nano wins 1, and 2 are ties. Wins: strategic_analysis (R1 4 vs Nano 2), creative_problem_solving (4 vs 2), tool_calling (5 vs 4), classification (4 vs 3), long_context (5 vs 4), safety_calibration (4 vs 2), persona_consistency (5 vs 4), agentic_planning (5 vs 4), and multilingual (5 vs 4). GPT‑4.1 Nano wins structured_output (5 vs R1’s 4); constrained_rewriting and faithfulness tie (both 4/5 respectively). Specifics and task meaning: - Tool calling: R1 scores 5 and is “tied for 1st with 16 others out of 54” (rankingsA.tool_calling); Nano scores 4 and ranks 18/54. That means R1 is more reliable at function selection, argument accuracy, and sequencing in our tests — valuable for agents. - Long context: R1 scores 5 (tied for 1st with 36 others out of 55) vs Nano 4 (rank 38/55). Despite GPT‑4.1 Nano having a larger context_window (1,047,576 vs R1’s 163,840), R1 performed better on our 30K+ retrieval tests, which matters for document search and extended-session assistants. - Safety and alignment: R1 safety_calibration 4 (rank 6/55) vs Nano 2 (rank 12/55); R1 refuses harmful requests more reliably in our suite. - Structured output: Nano 5 (tied for 1st with 24 others) vs R1 4 — GPT‑4.1 Nano is better at strict JSON/schema compliance in our tests. - Math/external benchmarks (Epoch AI): on MATH Level 5 R1 scores 96.6% vs GPT‑4.1 Nano 70% (Epoch AI); on AIME 2025 R1 66.4% vs Nano 28.9% (Epoch AI). These external results, attributed to Epoch AI, explain R1’s superiority on quantitative tasks in our evaluation. - Ties and nuances: constrained_rewriting ties at 4 each, and faithfulness is 5 for both; both models resist hallucination on source-faithful tasks equally in our tests. In short: R1 trades higher cost for substantially better tool use, long-context retrieval, agentic planning, multilingual and math performance; GPT‑4.1 Nano is the low-cost winner for strict structured outputs and multimodal inputs.

Per the payload, R1 0528 input = $0.50/million tokens and output = $2.15/million tokens; GPT-4.1 Nano input = $0.10 and output = $0.40. Using a simple 50/50 input/output split: at 1M total tokens/month R1 costs $1.325 vs GPT‑4.1 Nano $0.25; at 10M R1 $13.25 vs GPT $2.50; at 100M R1 $132.50 vs GPT $25.00. If your workload is output-heavy (long generated responses), R1’s $2.15/mTok output rate amplifies the gap. The ~5.375x priceRatio means high-volume SaaS, chat, or API businesses should prefer GPT‑4.1 Nano for cost control; teams that need R1’s higher benchmark accuracy should budget accordingly for the higher per-token bill.

Choose R1 0528 if you need high accuracy on agentic workflows, tool calling, long-context retrieval, safety calibration, multilingual output, or math-heavy tasks — and you can absorb higher per-token costs (input $0.50/mTok, output $2.15/mTok). Choose GPT‑4.1 Nano if you need the lowest cost and latency with multimodal input support (text+image+file -> text), strict schema/JSON outputs, or you’re running high-volume production traffic where the ~5.4× price gap matters (input $0.10/mTok, output $0.40/mTok).