R1 0528 vs o4 Mini
R1 0528 is the better pick for the most common production use case: lower cost and stronger wins on agentic planning, safety calibration, and constrained rewriting. o4 Mini beats R1 on structured output and strategic analysis (and posts higher math scores on external tests), so choose it when JSON compliance, strategic tradeoffs, or multimodal inputs matter.
deepseek
R1 0528
Benchmark Scores
External Benchmarks
Pricing
Input
$0.500/MTok
Output
$2.15/MTok
modelpicker.net
openai
o4 Mini
Benchmark Scores
External Benchmarks
Pricing
Input
$1.10/MTok
Output
$4.40/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite, R1 0528 wins 3 benchmarks (constrained_rewriting, safety_calibration, agentic_planning), o4 Mini wins 2 (structured_output, strategic_analysis), and 7 tests tie. Detailed comparisons (our testing):
- Constrained rewriting: R1 4 vs o4 Mini 3 — R1 ranks 6 of 53 (tied groups noted), meaning R1 is noticeably better at compression into hard character limits in our tests.
- Safety calibration: R1 4 vs o4 Mini 1 — R1 ranks 6 of 55 while o4 Mini ranks 32 of 55, so R1 refuses harmful requests and permits legitimate ones more reliably in our evaluation.
- Agentic planning: R1 5 vs o4 Mini 4 — R1 is tied for 1st (strong goal decomposition and failure recovery in our tests) while o4 Mini is rank 16, so R1 is the better agentic planner in typical agent workflows.
- Structured output: R1 4 vs o4 Mini 5 — o4 Mini ranks tied for 1st on structured_output (JSON/schema compliance), while R1 ranks 26 of 54; pick o4 Mini when strict schema adherence matters.
- Strategic analysis: R1 4 vs o4 Mini 5 — o4 Mini is tied for 1st on nuanced tradeoff reasoning; R1 sits midpack (rank 27), so o4 Mini gives clearer numeric tradeoffs in our tests.
- Tool calling & faithfulness & long context & persona & multilingual & classification & creative problem solving: ties — both models scored equally in our suite (e.g., tool_calling 5/5 and tied for 1st). External math benchmarks (Epoch AI): on MATH Level 5 o4 Mini 97.8% vs R1 96.6% (o4 Mini edges R1); on AIME 2025 o4 Mini 81.7% vs R1 66.4% (o4 Mini leads by a clear margin). We report our internal 1–5 scores and rankings alongside these Epoch AI percentages as supplementary context.
Pricing Analysis
Per the payload, R1 0528 charges $0.50 input + $2.15 output = $2.65 per mTok; o4 Mini charges $1.10 input + $4.40 output = $5.50 per mTok. At 1M tokens (1,000 mTok) that’s $2,650 (R1) vs $5,500 (o4 Mini). At 10M tokens: $26,500 vs $55,000. At 100M tokens: $265,000 vs $550,000. The priceRatio in the payload is ~0.49, so R1 runs at roughly half the per-mTok spend of o4 Mini. High-volume deployments, SaaS billing teams, and startups with tight margins should care about this gap; at 10M+ tokens/month the difference becomes tens of thousands of dollars monthly.
Real-World Cost Comparison
Bottom Line
Choose R1 0528 if: you need a lower-cost production model (about $2.65/mTok), prioritize safety calibration, agentic planning, tool calling, long-context and multilingual parity, and can accommodate R1’s quirks (see payload). Specific: internal agents, high-volume chat/assistant fleets, or safety-sensitive workflows. Choose o4 Mini if: you need strict structured output/JSON compliance, stronger strategic analysis, multimodal inputs (text+image+file->text), or top external math performance (MATH Level 5 97.8% and AIME 2025 81.7% per Epoch AI). Specific: applications requiring reliable schema output, document+image ingestion, or math-heavy reasoning.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.