Claude Opus 4.6 vs R1 0528

Summary of our 12-test suite (scores from our testing): Claude Opus 4.6 wins 3 tests, R1 0528 wins 2, and they tie on 7. Details: - Claude Opus 4.6 wins strategic_analysis (5 vs 4) — in our ranking Opus is tied for 1st of 54 (tied with 25 others); this implies better nuanced tradeoff reasoning and real‑number analysis for planning and business decisions. - Opus also wins creative_problem_solving (5 vs 4) and ranks tied for 1st, indicating stronger generation of non‑obvious feasible ideas. - Opus wins safety_calibration (5 vs 4) and is tied for 1st of 55, meaning it more reliably refuses harmful prompts while allowing legitimate ones in our tests. - R1 0528 wins constrained_rewriting (4 vs 3) and classification (4 vs 3); R1 ranks 6th of 53 on constrained_rewriting and tied for 1st on classification, so it handles tight character compression and routing tasks better in our runs. - They tie on tool_calling (both 5), agentic_planning (both 5), faithfulness (both 5), long_context (both 5), persona_consistency (both 5), multilingual (both 5), and structured_output (both 4). Ties on tool_calling and agentic_planning indicate comparable ability to select functions, sequence calls, and decompose goals. - External benchmarks: Beyond our internal tests, Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) and ranks 1 of 12 on that metric, and scores 94.4% on AIME 2025 (rank 4 of 23) in the payload. R1 0528 scores 96.6% on MATH Level 5 (Epoch AI) — rank 5 of 14 — but only 66.4% on AIME 2025 (rank 16 of 23). These external numbers show Opus leading on real-world coding verification (SWE-bench) and advanced contest math (AIME), while R1 has a strong MATH Level 5 result. - Important product note from the payload: R1 0528’s quirks include returning empty responses on structured_output and that it uses reasoning tokens which consume output budget; teams relying on strict structured JSON outputs should validate R1’s behavior in their integration.

Costs use the payload per‑mTok prices and assume a 50/50 split between input and output tokens. Claude Opus 4.6 charges $5 input and $25 output per mTok: at 1M tokens (1,000 mTok) that’s $2,500 input + $12,500 output = $15,000/month. At 10M tokens = $150,000/month; at 100M = $1,500,000/month. R1 0528 charges $0.50 input and $2.15 output per mTok: at 1M tokens = $250 + $1,075 = $1,325/month; at 10M = $13,250; at 100M = $132,500. The price ratio in the payload is 11.63×. Who should care: startups, consumer apps, and high‑volume pipelines will see six‑figure to seven‑figure monthly differences and should favor R1 0528 for cost control. Teams needing top-tier strategic reasoning, safety, or agentic workflows should budget for Opus 4.6’s higher operating cost.

Choose Claude Opus 4.6 if you need best-in-class strategic reasoning, safety calibration, long-context agent workflows, and SWE-bench coding robustness and you can absorb higher runtime costs (Opus is ~11.6× pricier per the payload). Choose R1 0528 if you are cost‑sensitive at scale, need strong constrained rewriting and classification, or want a competitive math performance (MATH Level 5 = 96.6% in our data) while saving substantially on monthly token spend.