DeepSeek V3.2 vs GPT-5.4

Summary of head-to-head results in our 12-test suite: GPT-5.4 wins 2 tests (tool_calling, safety_calibration); DeepSeek V3.2 wins none; the other 10 tests are ties. Detailed breakdown: - Tool calling: GPT-5.4 scores 4 vs DeepSeek 3. Rankings show GPT-5.4 at rank 18 of 54 vs DeepSeek rank 47 of 54, so GPT-5.4 is substantially better at function selection, argument accuracy and sequencing in our testing. - Safety calibration: GPT-5.4 scores 5 vs DeepSeek 2; GPT-5.4 is tied for 1st of 55 models while DeepSeek sits at rank 12 of 55 — this matters for apps that must refuse harmful requests reliably. - Structured output: both score 5 and are tied for 1st (tied with 24 others), meaning both models are strong at JSON/schema compliance in our tests. - Long context: both score 5 and tie for 1st (tied with 36 others), so both handle retrieval at 30K+ tokens well in our scenarios; note the context_window values in the payload differ (DeepSeek 163,840 vs GPT-5.4 1,050,000) which affects absolute context budgets. - Faithfulness, strategic_analysis, agentic_planning, persona_consistency, multilingual: all ties with top-tier ranks (many tied for 1st), indicating comparable performance on staying true to source content, nuanced tradeoff reasoning, goal decomposition and non-English output. - Constrained_rewriting and creative_problem_solving: both score 4 and rank similarly (constrained_rewriting rank 6 of 53; creative_problem_solving rank 9 of 54), so both generate feasible, specific ideas and handle tight-length constraints. - Classification: both score 3 and occupy the same mid-rank (rank 31 of 53), implying similar routing/categorization accuracy. External benchmarks (supplementary): GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI) — rank 2 of 12 — and 95.3% on AIME 2025 (Epoch AI) — rank 3 of 23. DeepSeek has no external SWE-bench/AIME scores in the payload; these third-party results support GPT-5.4’s stronger coding/math performance in our comparison.

DeepSeek V3.2 input = $0.26/mTok, output = $0.38/mTok. GPT-5.4 input = $2.50/mTok, output = $15.00/mTok. Per 1M tokens (1,000 mTok): DeepSeek = $260 input, $380 output, $640 total (if you count equal input+output). GPT-5.4 = $2,500 input, $15,000 output, $17,500 total per 1M in+out. At 10M tokens/month multiply by 10 (DeepSeek ≈ $6,400 vs GPT-5.4 ≈ $175,000). At 100M tokens/month multiply by 100 (DeepSeek ≈ $64,000 vs GPT-5.4 ≈ $1,750,000). Who should care: high-volume SaaS, search/indexing, and consumer apps doing millions of tokens/month will see massive savings with DeepSeek; teams that need highest-ranked tool-calling and safety guarantees may justify GPT-5.4’s much higher spend for smaller-scale or mission-critical apps.

Choose DeepSeek V3.2 if you: - Need the lowest operating cost at scale (DeepSeek output $0.38/mTok vs GPT-5.4 $15/mTok). - Run high-context retrieval or long conversations where cost and strong structured-output/faithfulness matter. - Require parity with GPT-5.4 on creative problem solving, constrained rewriting, strategic analysis, multilingual output, and structured outputs. Choose GPT-5.4 if you: - Must prioritize tool-calling correctness and safety calibration (GPT-5.4 wins tool_calling and safety_calibration in our tests). - Rely on external coding/math benchmarks (GPT-5.4: 76.9% on SWE-bench Verified and 95.3% on AIME 2025, Epoch AI). - Are building safety-critical agents or integrations where higher per-token cost is acceptable for better tool orchestration and refusal behavior.