R1 vs GPT-4o-mini

Summary of our 12-test head-to-head (scores are our 1–5 proxies unless noted):

• R1 wins: strategic_analysis (R1 5 vs GPT-4o-mini 2; R1 tied for 1st of 54 models), constrained_rewriting (R1 4 vs 3; R1 rank 6 of 53), creative_problem_solving (R1 5 vs 2; R1 tied for 1st), faithfulness (R1 5 vs 3; R1 tied for 1st of 55), persona_consistency (R1 5 vs 4; R1 tied for 1st), agentic_planning (R1 4 vs 3; R1 rank 16 of 54), multilingual (R1 5 vs 4; R1 tied for 1st of 55). These wins mean R1 is substantially better at nuanced tradeoff reasoning, maintaining character, multilingual parity, producing faithful outputs, and generating non‑obvious solutions — useful for complex analysis, long-form structured answers, and multi‑language products.
• GPT-4o-mini wins: classification (GPT-4o-mini 4 vs R1 2; GPT-4o-mini tied for 1st of 53) and safety_calibration (GPT-4o-mini 4 vs R1 1; GPT-4o-mini rank 6 of 55). Practically, GPT-4o-mini is the safer, more reliable choice for routing, content moderation, and classification tasks.
• Ties: structured_output (both 4; both rank 26 of 54), tool_calling (both 4; both rank 18 of 54), long_context (both 4; both rank 38 of 55). For JSON schema, function selection, and retrieval over large contexts both models perform similarly in our suite.
• External math benchmarks (Epoch AI): R1 scores 93.1% on MATH Level 5 vs GPT-4o-mini 52.6% (R1 rank 8 of 14 vs GPT-4o-mini rank 13 of 14). On AIME 2025 R1 scores 53.3% vs GPT-4o-mini 6.9% (R1 rank 17 of 23 vs GPT-4o-mini rank 21 of 23). These third‑party math results corroborate R1’s clear advantage on advanced math tasks. All statements above are from our testing and the Epoch AI scores where noted.

Prices in the payload are per mTok (1,000 tokens). For a realistic 1M tokens/month assuming a 50/50 split of input vs output tokens: R1 costs $1,600/month (input: $350 + output: $1,250) while GPT-4o-mini costs $375/month (input: $75 + output: $300). At 10M tokens/month those totals scale to $16,000 vs $3,750; at 100M tokens/month $160,000 vs $37,500. If you measure cost as 1M input + 1M output (double the tokens vs the 50/50 example), R1 is $3,200 vs GPT-4o-mini $750. Who should care: startups, high-volume APIs, and product teams with >1M tokens/mo will see immediate budget impact — GPT-4o-mini reduces token spend by ~76% versus R1 in these examples. Teams prioritizing maximum reasoning/math quality should budget for R1; teams optimizing latency/cost or safety-sensitive routing should prefer GPT-4o-mini.

Choose R1 if: you need best-in-class reasoning, advanced math (MATH Level 5 93.1% in Epoch AI), multilingual parity, or maximum faithfulness and creative problem solving for products where higher cost is acceptable. Choose GPT-4o-mini if: cost, safety calibration, and classification are priority constraints — it cuts token spend by ~76% at 1M tokens (50/50 split) and wins on safety and classification in our tests. If you need comparable tool calling, structured output, or long‑context retrieval at much lower cost, choose GPT-4o-mini.