Claude Opus 4.7 vs R1

Summary of wins (in our testing): Claude Opus 4.7 wins tool calling, classification, long context, safety calibration, and agentic planning. R1 wins multilingual. They tie on structured output, strategic analysis, constrained rewriting, creative problem solving, faithfulness, and persona consistency. Details and implications: 1) Tool calling — Opus: 5 (tied for 1st with 17 others out of 55 tested); R1: 4 (rank 19 of 55). In practice Opus's 5/5 means more accurate function selection, argument formatting and sequencing in agentic flows. 2) Long context — Opus: 5 (tied for 1st with 37 others of 56); R1: 4 (rank 39 of 56). Opus is materially better for retrieval or summarization across 30K+ token contexts. 3) Agentic planning — Opus: 5 (tied for 1st with 15 others of 55); R1: 4 (rank 17). Opus demonstrates stronger goal decomposition and failure recovery in our planning tests. 4) Safety calibration — Opus: 3 (rank 10 of 56); R1: 1 (rank 33 of 56). Opus refuses harmful requests more reliably while allowing legitimate ones; R1 scored low here in our suite. 5) Classification — Opus: 3 (rank 31 of 54) vs R1: 2 (rank 52 of 54) — Opus is better for routing and labeling pipelines. 6) Multilingual — Opus: 4 (rank 36 of 56) vs R1: 5 (tied for 1st with 34 others of 56) — R1 is stronger for non-English parity. 7) Ties — both models score the same on structured output (4, rank 26/55), strategic analysis (5, tied 1st), constrained rewriting (4, rank 6/55), creative problem solving (5, tied 1st), faithfulness (5, tied 1st) and persona consistency (5, tied 1st). These ties indicate comparable reliability on JSON/schema formatting, nuanced tradeoff reasoning, compressed rewriting, idea generation, sticking to sources, and maintaining a persona. 8) External math benchmarks (supplementary): R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 — both according to Epoch AI — which supports R1's strong formal-math capability on those external tests.

Costs shown assume a 50/50 split of input vs output tokens. Claude Opus 4.7 charges $5 per million input and $25 per million output, yielding about $15.00 per 1M tokens under a 50/50 mix. R1 charges $0.70 per million input and $2.50 per million output, yielding about $1.60 per 1M tokens. At 1M tokens/month the bill is ~$15.00 (Opus) vs $1.60 (R1); at 10M it's ~$150 vs $16; at 100M it's ~$1,500 vs $160. That ~10x gap matters for high-volume APIs, chat fleets, or embedded agents where token volume drives monthly spend; teams with strict cost budgets or large-scale inference should favor R1, while teams prioritizing best-in-class tool calling, long-context, and safety may accept Opus's higher cost.

Choose Claude Opus 4.7 if you need top-tier tool calling, robust long-context retrieval, stronger agentic planning and better safety calibration for production agents or multi-step workflows (it won 5 decisive benchmarks in our tests). Choose R1 if you must minimize inference cost at scale (about $1.60 vs $15 per 1M tokens under a 50/50 input/output split), need the best multilingual parity, or want strong external math results (MATH Level 5: 93.1% per Epoch AI).