DeepSeek V3.1 vs R1

Overview: R1 wins 4 benchmarks (strategic_analysis, constrained_rewriting, tool_calling, multilingual); DeepSeek V3.1 wins 3 (structured_output, classification, long_context); 5 benchmarks tie. Detailed walk-through: - Structured output: DeepSeek V3.1 = 5 vs R1 = 4. DeepSeek V3.1 is tied for 1st with 24 others on structured_output, so expect more reliable JSON/schema adherence in production pipelines. - Classification: DeepSeek V3.1 = 3 vs R1 = 2; DeepSeek V3.1 ranks 31 of 53 whereas R1 ranks 51 of 53, so routing and simple label tasks favor DeepSeek V3.1. - Long context: DeepSeek V3.1 = 5 vs R1 = 4; DeepSeek V3.1 is tied for 1st on long_context, making it stronger for retrieval and instructions across 30K+ tokens. - Strategic analysis: R1 = 5 vs DeepSeek V3.1 = 4; R1 is tied for 1st on strategic_analysis, so multi-step numerical tradeoffs and nuanced planning favor R1. - Constrained rewriting: R1 = 4 vs DeepSeek V3.1 = 3; R1 ranks 6 of 53 in this test, indicating it compresses/rewrites within hard limits better. - Tool calling: R1 = 4 vs DeepSeek V3.1 = 3; R1 ranks 18 of 54 vs DeepSeek V3.1 at 47 of 54 — R1 is measurably better at function selection, argument accuracy and sequencing. - Multilingual: R1 = 5 vs DeepSeek V3.1 = 4; R1 is tied for 1st in multilingual quality, so non-English parity favors R1. - Ties: creative_problem_solving (5/5 each), faithfulness (5/5 each, both tied for 1st), safety_calibration (1/1, both low), persona_consistency (5/5 each), agentic_planning (4/4 each). External benchmarks: R1 posts 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI) — these third-party scores support R1’s edge on tougher math/technical reasoning. Practical meaning: prefer R1 when you need better strategic reasoning, function/tool orchestration, constrained rewriting, or multilingual parity; prefer DeepSeek V3.1 for large-context retrieval, guaranteed structured output, and cheaper per-token compute.

Prices (per mTok): DeepSeek V3.1 input $0.15, output $0.75; R1 input $0.70, output $2.50. Assuming a 50/50 split of input/output tokens, monthly totals: for 1M tokens DeepSeek V3.1 = $450 vs R1 = $1,600; for 10M tokens DeepSeek V3.1 = $4,500 vs R1 = $16,000; for 100M tokens DeepSeek V3.1 = $45,000 vs R1 = $160,000. The absolute gap grows linearly, so startups, high-volume SaaS, and consumer apps should care: at 100M tokens/month R1 costs $115,000 more per month under the 50/50 assumption. Choose DeepSeek V3.1 when token cost is a primary constraint; choose R1 when the extra capability (see benchmarks) justified the higher spend.

Choose DeepSeek V3.1 if: - You need low-cost inference at scale (input $0.15 / output $0.75 per mTok). - Your workload relies on long-context retrieval (5/5) and strict structured outputs (5/5). - You need better classification/routing for pipelines. Choose R1 if: - You prioritize strategic analysis, tool calling, constrained rewriting, or multilingual output (R1 wins those benchmarks). - You need stronger performance on difficult math/technical tasks (R1: 93.1% on MATH Level 5, 53.3% on AIME 2025, Epoch AI). - You can justify the higher cost for better tool orchestration and nuanced reasoning.