R1 0528 vs DeepSeek V3.2

All benchmark statements below refer to results in our testing across the 12-test suite. Head-to-head wins: R1 0528 wins tool_calling, classification, and safety_calibration; DeepSeek V3.2 wins structured_output and strategic_analysis; the remaining tests tie. Specifics:

• Tool calling: R1 0528 scores 5 vs DeepSeek V3.2's 3 in our tests, and R1 is tied for 1st (rank 1 of 54) — this signals stronger function selection, argument accuracy, and sequencing for agentic workflows.
• Classification: R1 0528 scores 4 vs 3; R1 is tied for 1st in classification (tied with 29 others out of 53) — expect more reliable routing and tagging in pipelines.
• Safety calibration: R1 0528 scores 4 vs 2 for DeepSeek V3.2; R1 ranks 6 of 55 (ranked with 3 others) — R1 refuses harmful requests more appropriately in our tests.
• Structured output (JSON/schema): DeepSeek V3.2 scores 5 vs R1 0528's 4 and is tied for 1st (tied with 24 others) — DeepSeek V3.2 is the safer pick when strict schema adherence matters.
• Strategic analysis: DeepSeek V3.2 scores 5 vs R1 0528's 4 and is tied for 1st (tied with 25 others) — better for nuanced tradeoff calculations and numeric reasoning in our tests.
• Ties: constrained_rewriting (4/4), creative_problem_solving (4/4), faithfulness (5/5), long_context (5/5), persona_consistency (5/5), agentic_planning (5/5), multilingual (5/5) — both models perform identically on these tasks in our suite.
• Math/olympiad: R1 0528 scores 96.6 on math_level_5 (rank 5 of 14) and 66.4 on aime_2025 (rank 16 of 23) in our testing — DeepSeek V3.2 has no published scores for these external-style math tests in the payload. Operational constraints: R1 0528 has notable quirks in the payload — it 'returns empty responses on structured_output, constrained_rewriting, and agentic_planning' and 'uses reasoning tokens' that consume output budget on short tasks. Those quirks affect real tasks requiring short, strict JSON outputs or short-chain reasoning despite high tool_calling scores.

Per the payload, R1 0528 charges $0.50 per mTok input and $2.15 per mTok output; DeepSeek V3.2 charges $0.26 per mTok input and $0.38 per mTok output. Interpreting mTok as the billing unit listed, cost examples (assuming equal input/output token split):

• 1M tokens/mo (50% input, 50% output): R1 0528 ≈ $1,325; DeepSeek V3.2 ≈ $320.
• 10M tokens/mo: R1 0528 ≈ $13,250; DeepSeek V3.2 ≈ $3,200.
• 100M tokens/mo: R1 0528 ≈ $132,500; DeepSeek V3.2 ≈ $32,000. If you bill only output tokens, 1M output tokens cost $2,150 on R1 0528 vs $380 on DeepSeek V3.2. The absolute gap matters for high-volume products, multi-tenant SaaS, or any application where inference cost dominates; small-scale experimentation or highly specialized safety/agentic needs may justify R1 0528's premium.

Choose R1 0528 if: you need best-in-class tool calling, stronger classification, and tighter safety behavior in agentic workflows or math-heavy tasks and you can absorb the higher cost and the model's quirks (empty-on-structured-output, large min completion tokens). Choose DeepSeek V3.2 if: you need strict structured-output (JSON/schema) compliance, top-ranked strategic analysis, or a cost-efficient production model — it delivers the same long-context, persona, multilingual, and agentic-planning scores at a fraction of the price.