Claude Opus 4.6 vs R1

Across our 12-test suite, Claude Opus 4.6 wins 5 benchmarks, R1 wins 1, and 6 are ties (see win/loss/tie). Detailed comparisons (score out of 5 unless noted):

• Tool calling: Opus 4.6 = 5 vs R1 = 4. Opus ties for 1st (tied with 16 others out of 54) — this matters for systems that select and sequence functions and pass accurate arguments. Expect fewer tool-integration errors with Opus in our tests.
• Long context: Opus 4.6 = 5 vs R1 = 4. Opus is tied for 1st (36 others of 55) on 30K+ retrieval accuracy, so it handles very long documents better in our runs.
• Safety calibration: Opus 4.6 = 5 vs R1 = 1. Opus tied for 1st on safety (with 4 others of 55); R1 ranks 32 of 55. For content-moderation and refusal behavior, Opus is markedly safer in our testing.
• Agentic planning: Opus 4.6 = 5 vs R1 = 4. Opus tied for 1st (with 14 others of 54); better at goal decomposition and failure recovery in our tests.
• Classification: Opus 4.6 = 3 vs R1 = 2. Opus ranks 31 of 53, R1 ranks 51 of 53 — Opus is substantially better for routing/categorization tasks in practice.
• Constrained rewriting: R1 = 4 vs Opus 4.6 = 3. R1 ranks 6 of 53 here (Opus 31 of 53); R1 is the clear choice when you must compress text into hard character limits without losing meaning.
• Ties (both models scored the same): structured_output (4), strategic_analysis (5), creative_problem_solving (5), faithfulness (5), persona_consistency (5), multilingual (5). For these tasks, both models performed equivalently in our suite; note structured_output ranks 26 of 54 for each. Supplementary external benchmarks (Epoch AI): Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI) — rank 1 of 12 in our data — supporting its coding strength. On AIME 2025 (Epoch AI) Opus 4.6 scores 94.4% (rank 4 of 23) while R1 scores 53.3% (rank 17 of 23). Conversely, R1 scores 93.1% on MATH Level 5 (Epoch AI), showing R1’s strength on that specific math set (rank 8 of 14). These external numbers are supplementary to our 1–5 internal scores and help explain the models' task specializations.

The payload lists Claude Opus 4.6 at $5 input / $25 output per mTok and R1 at $0.7 input / $2.5 output per mTok (price ratio 10x). Using those per‑mTok rates and assuming 1 mTok = 1,000 tokens: output-only monthly costs are: for 1M tokens — Opus: $25,000 vs R1: $2,500; for 10M — Opus: $250,000 vs R1: $25,000; for 100M — Opus: $2,500,000 vs R1: $250,000. If you count input + output equally (round trips, input = output), combined monthly costs become: 1M tokens — Opus $30,000 vs R1 $3,200; 10M — Opus $300,000 vs R1 $32,000; 100M — Opus $3,000,000 vs R1 $320,000. The takeaway: high-volume API customers and startups should care — R1 reduces token spend by ~90% relative to Opus on token pricing in this payload. Choose Opus when the performance gains (tool calling, long context, safety) justify those multi‑thousand to multi‑million dollar differences; choose R1 when per‑token cost is the dominant decision factor.

Choose Claude Opus 4.6 if you need: production-grade agent workflows, robust tool calling, large context handling (30K+), and strict safety calibration — it wins those tests in our suite (tool_calling 5, long_context 5, safety_calibration 5) and also tops SWE-bench Verified at 78.7% (Epoch AI). Choose R1 if you need: a dramatically lower cost per token (R1 $2.5/output mTok vs Opus $25) or you prioritize constrained rewriting and some competition-style math tasks — R1 wins constrained_rewriting (4 vs 3) and scores 93.1% on MATH Level 5 (Epoch AI). If you’re cost-sensitive at scale, prefer R1; if accuracy/safety in agentic workflows matters and you can absorb the higher spend, prefer Opus 4.6.