DeepSeek V3.1 vs R1 0528

Head-to-head by our 12-test suite: R1 0528 wins on constrained_rewriting (4 vs 3), tool_calling (5 vs 3), classification (4 vs 3), safety_calibration (4 vs 1), agentic_planning (5 vs 4), and multilingual (5 vs 4). DeepSeek V3.1 wins structured_output (5 vs 4) and creative_problem_solving (5 vs 4). They tie on faithfulness (5/5), long_context (5/5), persona_consistency (5/5), and strategic_analysis (4/4).

Context and rankings: R1's tool_calling score (5) is tied for 1st out of 54 models on that test, while DeepSeek V3.1's 3 places it at rank 47 of 54 — a meaningful gap for workflows that pick functions and construct arguments. Safety calibration is another wide gap: R1 ranks 6th of 55 (score 4) versus DeepSeek V3.1's score 1 at rank 32, so R1 more reliably refuses harmful requests in our tests. For structured output, DeepSeek V3.1 scores 5 and is tied for 1st (JSON/schema compliance), while R1's 4 is mid-table (rank 26 of 54) — expect fewer schema fixes when using DeepSeek V3.1.

Other practical signals: both models score 5 on faithfulness and long_context (tied for 1st), so both handle source fidelity and very long contexts well in our tests. R1 includes external math results: it scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI), which supports its strong classification/structured reasoning performance; DeepSeek V3.1 has no external math entries in the payload. Overall, R1 excels where robust tool orchestration, safety, constrained rewriting, and multilingual classification matter; DeepSeek V3.1 shines for strict structured-output tasks and creative problem generation at a much lower price.

Per-token pricing (per 1K tokens): DeepSeek V3.1 input $0.15 / output $0.75. R1 0528 input $0.50 / output $2.15. Converted to per‑million tokens: DeepSeek V3.1 costs $150 per 1M input and $750 per 1M output; R1 0528 costs $500 per 1M input and $2,150 per 1M output. If you assume a 1:1 split of input:output tokens, cost per 1M-roundtrip = DeepSeek V3.1 ~$900 vs R1 0528 ~$2,650. At 10M (1:1) that's ~$9,000 vs ~$26,500; at 100M it's ~$90,000 vs ~$265,000. The practical takeaway: teams doing high-volume inference (10M+ tokens/month) will see large absolute savings with DeepSeek V3.1; teams prioritizing higher tool-calling accuracy, stronger safety calibration, or multilingual/classification quality may accept the 2.9x higher bill for R1 0528.

Choose DeepSeek V3.1 if: you need top-tier structured output (score 5, tied for 1st), creative problem solving (5), long-context fidelity, and you need to minimize inference cost (input $0.15/1K, output $0.75/1K). Choose R1 0528 if: your product depends on reliable tool calling, agentic planning, safety calibration, constrained rewriting, or multilingual/classification accuracy — R1 scores 5 on tool_calling and agentic_planning and 4 on safety (and posts strong external math scores per Epoch AI). If budget is tight at scale (10M+ tokens/month), favor DeepSeek V3.1; if correctness for tool-based pipelines matters more, accept R1's higher bill.