DeepSeek V3.1 vs DeepSeek V3.2

Overview: across our 12-test suite DeepSeek V3.2 wins 5 tests, DeepSeek V3.1 wins 1, and 6 tests tie. Detailed walk-through: - Strategic analysis: V3.2 scores 5 vs V3.1’s 4; V3.2 is tied for 1st of 54 models on strategic analysis (rank: tied for 1st with 25 others), so it’s the safer pick for numeric tradeoffs and multi-step reasoning. - Agentic planning: V3.2 5 vs V3.1 4 — V3.2 is tied for 1st of 54 (rank display: tied for 1st with 14 others), making it stronger at goal decomposition and failure recovery. - Constrained rewriting: V3.2 4 vs V3.1 3 — V3.2 ranks 6 of 53 vs V3.1 rank 31, so V3.2 will more reliably compress text into tight character limits. - Safety calibration: V3.2 2 vs V3.1 1 — V3.2 ranks 12 of 55 vs V3.1 rank 32, meaning V3.2 better balances refusal and permission on borderline requests. - Multilingual: V3.2 5 vs V3.1 4 — V3.2 is tied for 1st (top-tier) for non-English parity, so expect better cross-language quality. - Creative problem solving: V3.1 5 vs V3.2 4 — V3.1 is tied for 1st on this test (tied with 7 others), so it generates more non-obvious, feasible ideas in our testing. Ties (no clear winner but context matters): structured_output (both 5, tied for 1st), tool_calling (both 3, rank 47/54), faithfulness (both 5, tied for 1st), classification (both 3, rank 31/53), long_context (both 5, tied for 1st), persona_consistency (both 5, tied for 1st). Practical meaning: choose V3.2 for planning, multilingual, safety-sensitive, and compression tasks; choose V3.1 when your primary need is high-end creative ideation or specific prompt-mode long-context workflows (V3.1 max_output_tokens is 7,168 and context_window is 32,768 vs V3.2’s 163,840).

Costs (per 1,000 tokens = mTok) are: DeepSeek V3.1 input $0.15, output $0.75; DeepSeek V3.2 input $0.26, output $0.38. Example monthly budgets assuming a 50/50 input/output token split: for 1M tokens/month: V3.1 = $450, V3.2 = $320 (V3.1 is $130 more); for 10M: V3.1 = $4,500 vs V3.2 = $3,200 (difference $1,300); for 100M: V3.1 = $45,000 vs V3.2 = $32,000 (difference $13,000). If your workload is output-heavy (generation/chat), V3.1’s $0.75/mTok output makes it substantially more expensive (one million output tokens costs $750 on V3.1 vs $380 on V3.2). If your workload is input-heavy (e.g., large embeddings or classifiers), V3.2’s higher input price ($0.26 vs $0.15) narrows the gap but V3.2 still often costs less overall for balanced or generation-heavy flows. Teams with millions of monthly tokens or consumer-facing chat apps should care most about these differences.

Choose DeepSeek V3.1 if: - You need top-tier creative problem solving (score 5 vs 4) and tighter control over prompt modes (V3.1 supports a two-phase long-context prompt template and a 7,168 max output token limit). - Your app is small-scale or you can tolerate higher output costs for better ideation. Choose DeepSeek V3.2 if: - You prioritize strategic analysis, agentic planning, constrained rewriting, safety calibration, or multilingual quality (V3.2 wins those 5 tests). - You operate at scale and care about cost-efficiency for generation (V3.2 output $0.38 vs V3.1 $0.75/mTok) and need a very large context window (163,840 tokens).