Claude Sonnet 4.6 vs R1

Overview: across our 12-test suite Sonnet (modelA) wins 5 tests, R1 wins 1, and 6 tests tie (see win/loss/tie breakdown in the payload). Detailed walk-through by test: - Tool calling: Sonnet 5 vs R1 4. Sonnet is tied for 1st of 54 models (tied with 16 others); R1 ranks 18 of 54. In practice Sonnet will select and sequence functions more accurately and produce better argument payloads for tool-based workflows. - Classification: Sonnet 4 vs R1 2; Sonnet is tied for 1st of 53 (29 others share the top score). Expect fewer misroutes and better intent classification with Sonnet. - Long context: Sonnet 5 vs R1 4; Sonnet tied for 1st of 55 (36 ties) and has a 1,000,000-token context window vs R1’s 64,000. For large-document retrieval, codebases, or long chat histories Sonnet maintains higher retrieval fidelity. - Safety calibration: Sonnet 5 vs R1 1; Sonnet is tied for 1st of 55 (4 others share top score). Sonnet refuses harmful prompts more reliably while allowing legitimate ones. - Agentic planning: Sonnet 5 vs R1 4; Sonnet is tied for 1st of 54 (14 ties). Sonnet better decomposes goals and proposes robust recovery steps. - Constrained rewriting: Sonnet 3 vs R1 4; R1 wins here and ranks 6 of 53 (25 share that score). If you need aggressive compression into hard character limits, R1 is stronger. - Ties (no clear winner): structured_output (both 4), strategic_analysis (both 5), creative_problem_solving (both 5), faithfulness (both 5), persona_consistency (both 5), multilingual (both 5). These ties indicate comparable performance on JSON/schema compliance, nuanced reasoning, creativity, fidelity to source, character maintenance, and multilingual parity. External benchmarks (Epoch AI): Sonnet scores 75.2% on SWE-bench Verified (Epoch AI) and ranks 4 of 12 in that subset — a concrete indicator of strong coding problem resolution in third‑party testing. Sonnet also scores 85.8% on AIME 2025 (Epoch AI), ranking 10 of 23. R1 posts 93.1% on MATH Level 5 (Epoch AI), ranking 8 of 14, but scores 53.3% on AIME 2025 (Epoch AI), ranking 17 of 23. These external results corroborate that R1 is relatively stronger on certain math benchmarks while Sonnet holds an edge on coding SWE-bench and contest-style math like AIME in our data.

Raw per‑token prices in the payload: Claude Sonnet 4.6 charges $3.00 / m-input-token and $15.00 / m-output-token. R1 charges $0.70 / m-input-token and $2.50 / m-output-token. Practical examples: assume a balanced 50/50 split of input vs output tokens. Per 1 million total tokens: Sonnet = 0.5*(3+15) = $9.00; R1 = 0.5*(0.7+2.5) = $1.60. Per 10M tokens: Sonnet $90; R1 $16. Per 100M tokens: Sonnet $900; R1 $160. If your workload is output-heavy (e.g., generation-dominant) the gap widens: 1M output-only = Sonnet $15 vs R1 $2.50; 100M output-only = Sonnet $1,500 vs R1 $250. Who should care: high-volume SaaS, searchable chat, and agent fleets will save materially on R1; teams that need Sonnet’s tool-calling, long-context, or safety guarantees may find the higher cost justified for lower error / fewer manual interventions.

Choose Claude Sonnet 4.6 if: - You build agents, tool-driven workflows, or need robust function-calling and argument accuracy (Sonnet 5 vs R1 4 on tool_calling; Sonnet tied for 1st). - You work with very long contexts (Sonnet 5, 1,000,000-token window vs R1 64k). - Safety calibration and faithfulness matter (Sonnet 5 vs R1 1 on safety; both 5 on faithfulness but Sonnet ranks top on safety). Choose R1 if: - You are price-sensitive or run very high token volumes (R1 costs roughly $1.60 per 1M tokens balanced vs Sonnet $9 per 1M in a 50/50 split). - Your priority is constrained rewriting/compression (R1 4 vs Sonnet 3) or some high-level math workloads (R1 93.1% on MATH Level 5, Epoch AI). - You can accept smaller context windows and the need to manage R1’s stated quirks (reasoning tokens, min_max_completion_tokens).