R1 0528 vs GPT-4.1

Across our 12-test suite R1 0528 wins 3 tests, GPT-4.1 wins 2 tests, and 7 tests tie. Details: - Creative problem solving: R1 4 vs GPT-4.1 3 (R1 rank 9 of 54 vs GPT rank 30) — expect R1 to produce more feasible, non-obvious ideas in our prompts. - Safety calibration: R1 4 vs GPT-4.1 1 (R1 rank 6 of 55 vs GPT rank 32) — R1 refused harmful requests more reliably in our tests. - Agentic planning: R1 5 vs GPT-4.1 4 (R1 tied for 1st; GPT rank 16) — R1 better at decomposition and failure recovery in our agent-style tasks. - Strategic analysis: GPT-4.1 5 vs R1 4 (GPT tied for 1st; R1 rank 27) — GPT-4.1 handles nuanced tradeoffs and numeric reasoning better in our scenarios. - Constrained rewriting: GPT-4.1 5 vs R1 4 (GPT tied for 1st; R1 rank 6) — GPT-4.1 is stronger when tight character limits and exact compressions matter. Ties (structured_output, tool_calling, faithfulness, classification, long_context, persona_consistency, multilingual) mean both models produced equivalent scores on those tasks in our tests — e.g., both scored 5 for long_context and persona_consistency and both tied for top ranks on tool_calling. External benchmarks (Epoch AI) supplement this picture: on MATH Level 5 R1 scores 96.6% vs GPT-4.1 83.0%; on AIME 2025 R1 66.4% vs GPT-4.1 38.3% (Epoch AI). GPT-4.1 reports 48.5% on SWE-bench Verified (Epoch AI); R1 has no SWE-bench value in the payload. Practical context: R1 shines for agentic workflows, safer refusals, creative tasks, and higher math performance in our tests; GPT-4.1 shines for strategic tradeoff reasoning and ultra-precise constrained rewriting, and adds multimodal I/O and a much larger context window (1,047,576 vs R1's 163,840 tokens). Note R1 quirks from the payload: it "returns empty responses on structured_output, constrained_rewriting, and agentic_planning" and "uses reasoning tokens" which can affect short-task output budgets — test these paths before production.

Pricing in the payload is per mTok. Using a 50/50 input/output token split as a practical example, R1 0528 (input $0.50 / output $2.15 per mTok) costs $1,325 per 1M total tokens; GPT-4.1 (input $2.00 / output $8.00 per mTok) costs $5,000 per 1M total tokens. Scale impact: at 10M tokens/month R1 ≈ $13,250 vs GPT-4.1 ≈ $50,000; at 100M tokens/month R1 ≈ $132,500 vs GPT-4.1 ≈ $500,000. Who should care: any high-volume app, startups with tight margins, or teams embedding models for heavy automation — the roughly 3.8x cost gap on a 50/50 traffic mix makes R1 materially cheaper for scale.

Choose R1 0528 if: you operate at scale and need a dramatically lower cost per token (R1 input $0.50 / output $2.15 per mTok), require top agentic planning, tool calling, safer refusals, strong creative/problem-solving, or superior MATH Level 5 and AIME performance in our tests. Choose GPT-4.1 if: you need the best strategic analysis and constrained rewriting from our suite, multimodal I/O (text+image+file->text), or a far larger context window (1,047,576 tokens) and are willing to pay roughly 3.8x more per 50/50 token mix for those capabilities.