R1 vs DeepSeek V3.2

We compare the two on our 12-test suite (scores are 1–5 unless noted). Wins/ties are based on those scores in our testing. - Classification: R1 2 vs V3.2 3 — DeepSeek V3.2 wins; R1 ranks poorly (rank 51 of 53) while V3.2 sits mid-table (rank 31 of 53). This matters for routing/categorization systems. - Multilingual: R1 5 vs V3.2 5 — tie; both tied for 1st with many models, so expect equivalent non-English quality in our tests. - Constrained rewriting: R1 4 vs V3.2 4 — tie; both rank 6 of 53, so both handle tight character limits similarly. - Long context: R1 4 vs V3.2 5 — DeepSeek V3.2 wins; V3.2 is tied for 1st on long-context in our rankings while R1 sits at rank 38 of 55. For retrieval and 30K+ token workflows, V3.2 is clearly stronger. - Persona consistency: R1 5 vs V3.2 5 — tie; both tied for 1st, so character/chat stability is equivalent. - Structured output: R1 4 vs V3.2 5 — V3.2 wins and is tied for 1st; R1 ranks 26 of 54. If you need reliable JSON/schema outputs, V3.2 is safer in our tests. - Tool calling: R1 4 vs V3.2 3 — R1 wins; R1 ranks 18 of 54 while V3.2 ranks 47 of 54, so R1 is better at correct function selection and arguments in our tool-calling tests. - Strategic analysis: R1 5 vs V3.2 5 — tie; both tied for 1st in our tests, so both handle nuanced tradeoff reasoning well. - Safety calibration: R1 1 vs V3.2 2 — V3.2 wins; V3.2 ranks 12 of 55 vs R1 at 32 of 55, so V3.2 is measurably better at refusing harmful prompts while permitting legitimate ones. - Creative problem solving: R1 5 vs V3.2 4 — R1 wins; R1 ties for 1st, while V3.2 ranks 9th. For non-obvious, high-quality idea generation, R1 leads. - Faithfulness: R1 5 vs V3.2 5 — tie; both tied for 1st, so both stick to source material in our tests. - Agentic planning: R1 4 vs V3.2 5 — V3.2 wins and is tied for 1st; R1 is solid (rank 16) but V3.2 better decomposes goals and plans recovery in our tests. Additional math/competition signals: R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI); those external results are present for R1 in the payload and can inform math-heavy use cases. DeepSeek V3.2 has no math-level scores in the provided payload. Finally, context window and token limits matter: R1 has a 64,000 token window and max_output_tokens 16,000; DeepSeek V3.2 reports a 163,840 token window — this aligns with V3.2’s long-context strength.

Costs are material. Per the payload, R1 input is $0.70/mTok and output $2.50/mTok; DeepSeek V3.2 input is $0.26/mTok and output $0.38/mTok (priceRatio = 6.5789). Using a 50/50 input/output token split: for 1M tokens/month (500k input + 500k output) R1 costs $1,600 (0.7×500 + 2.5×500 = $350 + $1,250), DeepSeek V3.2 costs $320 (0.26×500 + 0.38×500 = $130 + $190). At 10M tokens/month multiply those totals by 10 (R1 $16,000 vs V3.2 $3,200). At 100M tokens/month: R1 $160,000 vs V3.2 $32,000. Teams with heavy output workloads (e.g., long responses, summarization) should care most—R1’s $2.50/mTok output rate drives the largest part of the gap. Small-scale prototyping or niche cases where R1’s wins matter may justify the premium; most production uses will be far more cost-effective on DeepSeek V3.2.

Choose DeepSeek V3.2 if you need large-context retrieval, reliable structured/JSON outputs, stronger agentic planning, better safety calibration, and far lower per-token cost (V3.2 wins 5 of 12 benchmarks in our testing). Choose R1 if your priority is the best creative problem-solving and function/tool-calling accuracy and you’re willing to pay a premium—R1 wins those tests but costs ~6.58× more per the payload. If you’re operating at scale (millions of tokens/month), DeepSeek V3.2 will likely save the team tens of thousands to hundreds of thousands of dollars.