R1 vs GPT-5.1

Summary of our 12-test suite (scores are our tests unless noted):

• Wins and ties: GPT-5.1 wins 3 tests (classification, long_context, safety_calibration); R1 wins 1 test (creative_problem_solving); 8 tests tie. Be explicit per test:
  • Classification: GPT-5.1 4 vs R1 2 — GPT-5.1 is tied for 1st (rank: tied for 1st of 53) while R1 is rank 51 of 53. For routing and categorization tasks this means GPT-5.1 will make far fewer classification errors.
  • Long context (retrieval at 30K+ tokens): GPT-5.1 5 vs R1 4 — GPT-5.1 is tied for 1st (rank: tied for 1st of 55) vs R1 rank 38/55. Combined with GPT-5.1’s 400k context window (vs R1’s 64k), GPT-5.1 is the clear choice for very long documents, chat history, or retrieval-augmented workflows.
  • Safety calibration: GPT-5.1 2 vs R1 1 — GPT-5.1 ranks 12/55 vs R1 32/55. GPT-5.1 better distinguishes harmful from legitimate requests in our tests; R1 showed the weakest safety calibration of the two.
  • Creative problem solving: R1 5 vs GPT-5.1 4 — R1 ties for 1st on creative_problem_solving (rank: tied for 1st of 54), so for brainstorming non-obvious, feasible ideas R1 produced stronger outcomes in our tests.
  • Ties (both models equal): structured_output (4), strategic_analysis (5), constrained_rewriting (4), tool_calling (4), faithfulness (5), persona_consistency (5), agentic_planning (4), multilingual (5). For these tasks both models deliver comparable quality; see ranks (e.g., both tied for 1st in strategic_analysis and faithfulness).
• External benchmarks (Epoch AI): GPT-5.1 scores 68 on SWE-bench Verified (Epoch AI) and 88.6 on AIME 2025 (Epoch AI), ranking 7th on SWE-bench and 7th on AIME in our dataset — indicating strong coding and contest-style math performance. R1 scores 93.1 on MATH Level 5 (Epoch AI) (rank 8 of 14) but only 53.3 on AIME 2025 (Epoch AI) — showing a split: R1 is very strong on MATH Level 5 problems but weaker on AIME-style tasks in our runs. Overall interpretation: GPT-5.1 is the better pick where classification accuracy, very-long-context retrieval, safety, and multimodal inputs matter. R1 is competitive for high-quality creative outputs and certain math workloads, at a much lower operating cost.

Per 1,000 tokens (mTok) the models charge: R1 input $0.70 / output $2.50; GPT-5.1 input $1.25 / output $10.00. Per million tokens that is: R1 input $700, output $2,500 (combined symmetrical I/O $3,200); GPT-5.1 input $1,250, output $10,000 (combined $11,250). At scale: 10M tokens/month = R1 $32,000 vs GPT-5.1 $112,500; 100M tokens/month = R1 $320,000 vs GPT-5.1 $1,125,000. The payload lists a priceRatio of 0.25; in practice for balanced input+output volumes R1 costs ~28% of GPT-5.1. Enterprises and high-volume apps should care: switching to R1 can save hundreds of thousands per year, while teams needing multimodal/very-long-context capabilities may justify GPT-5.1’s higher bill.

Choose R1 if: you need a lower-cost AI for high-volume production, prioritize creative problem-solving, or want a strong MATH Level 5 performer (R1 math_level_5 93.1 in our tests) while accepting weaker safety calibration and a 64k context limit. Choose GPT-5.1 if: you require top long-context performance and multimodal inputs (400k context, file/image->text), stronger classification and safety handling, or better performance on AIME and SWE-bench (GPT-5.1 AIME 88.6 and SWE-bench Verified 68 per Epoch AI).