R1 0528 vs GPT-4.1 Mini

Summary: R1 0528 wins 6 internal benchmarks (creative_problem_solving 4 vs 3, tool_calling 5 vs 4, faithfulness 5 vs 4, classification 4 vs 3, safety_calibration 4 vs 2, agentic_planning 5 vs 4). Six benchmarks tie (structured_output, strategic_analysis, constrained_rewriting, long_context, persona_consistency, multilingual). Notable specifics: - Tool calling: R1 scores 5 vs GPT-4.1 Mini's 4; R1 is tied for 1st of 54 models (tied with 16 others) while GPT-4.1 Mini ranks 18th of 54 — this matters for function selection, argument accuracy, and sequencing in agent workflows. - Faithfulness & Classification: R1 scores 5/4 and is tied for 1st in both classification and faithfulness (classification tied for 1st among 53), while GPT-4.1 Mini sits midpack (classification rank 31 of 53; faithfulness rank 34 of 55) — expect fewer hallucinations and better routing with R1 in our tests. - Safety calibration: R1 4 vs GPT-4.1 Mini 2; R1 ranks 6 of 55 (4 models share this) vs GPT-4.1 Mini rank 12 — R1 was substantially better at correct refusals vs permissive outputs in our suite. - Creative problem solving: R1 4 vs 3, ranking R1 at 9 of 54 vs GPT-4.1 Mini at 30 — R1 produced more non-obvious, feasible ideas in our tests. - Long context & persona & multilingual: both scored 5 and tie for 1st in our dataset (long_context tied for 1st with 36 others) — GPT-4.1 Mini's 1,047,576-token window and R1's 163,840-token window both performed well on retrieval and coherence at high token counts. - External math benchmarks: On MATH Level 5 (Epoch AI) R1 scores 96.6% vs GPT-4.1 Mini 87.3%; on AIME 2025 R1 66.4% vs GPT-4.1 Mini 44.7% — these external results reinforce R1's advantage on hard math and reasoning tasks. Caveats: R1 has implementation quirks — it uses reasoning tokens that consume output budget on short tasks and the model may return empty responses for structured_output, constrained_rewriting, and agentic_planning unless high max_completion_tokens are set. We observed that despite high scores, those quirks require engineering workarounds.

Pricing per mtok (payload units): R1 0528 input $0.50 / mtok and output $2.15 / mtok; GPT-4.1 Mini input $0.40 / mtok and output $1.60 / mtok. Using mtok = 1,000 tokens, a 50/50 split of input/output yields per-million-token costs: R1 = $1,325 / 1M tokens; GPT-4.1 Mini = $1,000 / 1M tokens. At scale the gap is linear: 1M tokens => $325 more with R1, 10M => $3,250 more, 100M => $32,500 more per month. Teams doing high-volume inference (10M+/month) or operating on slim unit-economics should prefer GPT-4.1 Mini for cost savings; teams who need R1's higher tool-calling, safety, or MATH-level accuracy should budget the ~34.4% premium (price ratio 1.34375).

Choose R1 0528 if you need top-ranked tool calling, stronger safety calibration, higher faithfulness and better hard-math performance (MATH Level 5: 96.6% vs 87.3%) and you can accept ~34% higher per-token cost and manage R1's quirks (reasoning tokens and empty structured outputs). Choose GPT-4.1 Mini if you need multimodal I/O (text+image+file), the largest context window (1,047,576 tokens) and lower cost — it saves $325 per 1M tokens on a 50/50 input/output split and ties on long-context, persona consistency, and multilingual tests.