R1 vs GPT-4.1 Mini

Summary of our 12-test suite (scores are from our testing):

• R1 wins: strategic_analysis (R1 5 vs GPT-4.1 Mini 4) — R1 is tied for 1st on strategic analysis in our rankings ("tied for 1st with 25 other models out of 54 tested"). This matters for tasks that need nuanced tradeoff reasoning and numeric tradeoffs. creative_problem_solving (R1 5 vs 3) — R1 is tied for 1st on creative problem solving, so it generates more non-obvious, feasible ideas in our tests. faithfulness (R1 5 vs 4) — R1 ties for 1st on faithfulness, meaning it sticks to source material better in our evaluation.
• GPT-4.1 Mini wins: classification (GPT-4.1 Mini 3 vs R1 2) — GPT-4.1 Mini ranks substantially higher (rank 31 vs R1 rank 51 of 53), so it’s better at routing and categorization in our tests. long_context (GPT-4.1 Mini 5 vs R1 4) — GPT-4.1 Mini is tied for 1st on long context in our ranking (tied for 1st with 36 others), reflecting superior retrieval accuracy at 30K+ token scales in our testing. safety_calibration (GPT-4.1 Mini 2 vs R1 1) — GPT-4.1 Mini’s safety score and rank (rank 12 of 55) show it refuses harmful prompts more appropriately in our suite.
• Ties (no clear winner in our testing): structured_output (both 4), constrained_rewriting (both 4), tool_calling (both 4), persona_consistency (both 5), agentic_planning (both 4), multilingual (both 5). For these tasks the models performed equivalently in our benchmarks.
• Math/competition: R1 scored 93.1% vs GPT-4.1 Mini 87.3% on math_level_5 and 53.3 vs 44.7 on aime_2025 in our testing; R1 ranks 8th vs GPT-4.1 Mini 9th on math_level_5, and 17th vs 18th on AIME 2025 — small but measurable edge for R1 on hard math in our suite. Context and real-task meaning: R1’s strengths (top ranks in strategic analysis, creative problem solving, and faithfulness) indicate it’s better for in-depth reasoning, product strategy write-ups, ideation, and fidelity-sensitive summarization in our tests. GPT-4.1 Mini’s wins in long_context, classification, and safety calibration make it a better fit for document retrieval across huge contexts (it also has a 1,047,576-token context window in the payload), production classification/routing pipelines, and workloads where safer refusals are important. All benchmark claims above are from our testing.

Per the payload prices: R1 charges $0.70 input and $2.50 output per mTok while GPT-4.1 Mini charges $0.40 input and $1.60 output per mTok (price ratio 1.5625). Interpreting mTok as the same billing unit in the payload, output-only cost examples: per 1M output tokens R1 ≈ $2,500 vs GPT-4.1 Mini ≈ $1,600; per 10M: R1 ≈ $25,000 vs GPT-4.1 Mini ≈ $16,000; per 100M: R1 ≈ $250,000 vs GPT-4.1 Mini ≈ $160,000. If your app sends roughly equal input and output tokens, add input costs: per 1M total tokens (50/50 split) R1 ≈ $3,200 vs GPT-4.1 Mini ≈ $2,000; per 10M: R1 ≈ $32,000 vs GPT-4.1 Mini ≈ $20,000; per 100M: R1 ≈ $320,000 vs GPT-4.1 Mini ≈ $200,000. Bottom line: high-volume, price-sensitive deployments should care about GPT-4.1 Mini’s ~1.56× cost advantage; teams that need R1’s edge in reasoning, creativity, or faithfulness must budget the higher spend.

Choose R1 if: you prioritize top-tier strategic reasoning, creative problem solving, and faithfulness (R1 scores 5 vs GPT-4.1 Mini 4/3 on those tests in our suite), and you can absorb higher token costs (R1 output $2.50 per mTok). Use cases: research analysis, idea-generation workshops, high-fidelity summarization, or math-heavy assistants (R1 math_level_5 93.1 vs 87.3).
Choose GPT-4.1 Mini if: you need a lower-cost, multimodal model with huge context and better safety/classification in our tests (long_context 5 vs R1 4; safety_calibration 2 vs R1 1), or you’re building large-scale document retrieval, classifier/routing pipelines, or image+file inputs (GPT-4.1 Mini modality includes text+image+file→text). Use cases: enterprise search over very long docs, high-volume production inference, and moderated customer-facing assistants where cost and safety matter.