R1 vs o4 Mini

Overview: across our 12-test suite the models split wins 4 (o4 Mini) vs 2 (R1) with 6 ties. Below we compare each test with scores and rank context from our testing.

• tool_calling: o4 Mini 5 vs R1 4. o4 Mini wins and ranks "tied for 1st with 16 others" (rank 1 of 54); R1 ranks 18 of 54. For real tasks this means o4 Mini selects and sequences functions and arguments more accurately in our tool-calling scenarios.

• structured_output: o4 Mini 5 vs R1 4. o4 Mini wins and is "tied for 1st with 24 others"; R1 is mid-pack (rank 26 of 54). For JSON/schema outputs, o4 Mini adheres to formats more reliably.

• classification: o4 Mini 4 vs R1 2. o4 Mini is tied for 1st (rank 1 of 53); R1 is near the bottom (display: "rank 51 of 53"). R1 is weak for routing/labeling tasks in our tests; o4 Mini is far better for accurate categorization.

• long_context: o4 Mini 5 vs R1 4. o4 Mini wins and is tied for 1st (rank 1 of 55); R1 is lower (rank 38 of 55). Concretely, o4 Mini handles retrieval/QA across 30K+ tokens better in our scenarios. The models' context windows reflect this: o4 Mini 200,000 vs R1 64,000 tokens.

• constrained_rewriting: R1 4 vs o4 Mini 3. R1 wins (R1 rank display: "rank 6 of 53" shared among many); o4 Mini is lower (rank 31). R1 performs better when outputs must be compressed into hard character/length limits.

• creative_problem_solving: R1 5 vs o4 Mini 4. R1 wins and is tied for top (display: "tied for 1st with 7 others"); o4 Mini is strong but a notch lower. Expect R1 to generate more non-obvious, feasible ideas in our creative tasks.

• strategic_analysis: tie, both 5. Both models tie for top (both show "tied for 1st with 25 others"). For nuanced tradeoffs our tests show parity.

• faithfulness: tie, both 5 and tied for 1st. Both stick to source material in our faithfulness tests.

• persona_consistency: tie, both 5 and tied for 1st. Both maintain character across prompts in our tests.

• agentic_planning: tie, both 4 (R1 displayed rank 16 of 54; o4 Mini rank 16). Both perform similarly on decomposition and failure recovery.

• multilingual: tie, both 5 (tied for 1st). Both produce equivalent-quality non-English outputs in our tests.

• safety_calibration: tie, both 1 (both rank display: "rank 32 of 55"). Both models scored poorly on safety calibration in our suite and show similar refusal/permissiveness behavior.

External math benchmarks (Epoch AI): On MATH Level 5 (Epoch AI) o4 Mini scores 97.8% vs R1 93.1% (o4 Mini rank 2 of 14; R1 rank 8). On AIME 2025 (Epoch AI) o4 Mini 81.7% vs R1 53.3% (o4 Mini rank 13 of 23; R1 rank 17). We reference Epoch AI for these external measures. These math results corroborate o4 Mini's advantage on reasoning/math-heavy tasks in our tests.

Costs (per mTok from payload): R1 input $0.70, output $2.50; o4 Mini input $1.10, output $4.40. Per 1M tokens (1,000 mTok): R1 = $700 input / $2,500 output; o4 Mini = $1,100 input / $4,400 output. With a 50/50 input/output split (practical mixed usage) per 1M tokens: R1 ≈ $1,600, o4 Mini ≈ $2,750. At 10M tokens/month: R1 ≈ $16,000 vs o4 Mini ≈ $27,500. At 100M tokens/month: R1 ≈ $160,000 vs o4 Mini ≈ $275,000. R1 therefore runs at ~57% of o4 Mini's per-token cost (priceRatio 0.568). Teams with large volume (10M+ tokens/month), thin margins, or prototypes should care about the gap; teams prioritizing tool integration, long-context, or top-tier classification will likely accept o4 Mini's higher cost.

Choose o4 Mini if: you need the best tool-calling, structured-output, classification, or very long-context performance (o4 Mini wins those tests and offers a 200k context window), and you can absorb higher per-token costs (output $4.40/mtok). Choose R1 if: cost efficiency matters (R1 output $2.50/mtok; ~57% of o4 Mini's cost) and you prioritize creative problem solving or tight constrained rewriting where R1 outscored o4 Mini. If you need top math/reasoning accuracy (Epoch AI math tests), o4 Mini is demonstrably stronger.