R1 0528 vs o3

Summary of head-to-head results in our 12-test suite: R1 0528 wins 3 benchmarks, o3 wins 2, and 7 are ties. In our testing: - R1 wins classification (R1 4 vs o3 3), meaning more accurate routing/categorization in workflows. - R1 wins long_context (5 vs 4), which matters for retrieval and tasks with 30K+ token context. - R1 wins safety_calibration (4 vs 1), so R1 more reliably refuses harmful prompts while permitting legitimate requests. - o3 wins structured_output (R1 4 vs o3 5), so o3 is better at strict JSON/schema compliance and format adherence. - o3 also wins strategic_analysis (R1 4 vs o3 5), which shows up in nuanced tradeoff reasoning and numeric decision tasks. The remaining tests are ties: constrained_rewriting (4/4), creative_problem_solving (4/4), tool_calling (5/5), faithfulness (5/5), persona_consistency (5/5), agentic_planning (5/5), and multilingual (5/5) — these indicate comparable performance on instruction-following, tool sequencing, and multilingual output. Rankings context: R1 is tied for 1st in persona_consistency, faithfulness, long_context, tool_calling, agentic_planning and multilingual in our rankings, and R1 holds rank 5 of 14 on math_level_5 (96.6% on Epoch AI). o3 is tied for 1st on strategic_analysis and structured_output in our ranking sets and scores 97.8% on math_level_5 and 83.9% on AIME 2025 according to Epoch AI (third-party). Note an important R1 quirk from the payload: R1 returns empty responses on structured_output, constrained_rewriting, and agentic_planning in some cases and uses reasoning tokens that consume output budget on short tasks — this can materially impact JSON schema and short-output workflows despite R1's solid numeric scores.

Per 1k tokens: R1 0528 costs $0.50 (input) and $2.15 (output); o3 costs $2 (input) and $8 (output). Using a simple 50/50 input/output split as a realistic baseline, combined cost per 1k tokens is $2.65 for R1 and $10.00 for o3. Monthly examples at that split: 1M tokens → R1 ≈ $2,650 vs o3 ≈ $10,000; 10M → R1 ≈ $26,500 vs o3 ≈ $100,000; 100M → R1 ≈ $265,000 vs o3 ≈ $1,000,000. The cost gap matters for any high-volume deployment (teams running millions of tokens/month) or consumer-facing apps with many users. R1 is the clear choice when budget is a top constraint; o3 is justifiable only when its specific wins (structured output, strategic analysis, or superior external math/AIME scores) deliver measurable value that offsets the ~3.8x higher per-token bill.

Choose R1 0528 if: you need a much lower-cost engine for high-volume use (R1 combined ≈ $2.65/1k vs o3 $10/1k at a 50/50 split), you prioritize long-context retrieval, stronger safety calibration, or better classification. Choose o3 if: you require best-in-class structured-output/JSON fidelity or top-tier performance on harder math/olympiad tasks (o3: math_level_5 97.8% and AIME 2025 83.9% per Epoch AI), and you can absorb ~3.8x higher per-token spend for those gains.