DeepSeek V3.2 vs GPT-5.4 Nano

Across our 12-test suite most categories are ties (8 ties). DeepSeek V3.2 wins faithfulness (score 5 vs 4) — ranks tied for 1st of 55 with 32 others — meaning it sticks to source material more reliably in our testing. DeepSeek also wins agentic planning (5 vs 4), tied for 1st (display: "tied for 1st with 14 other models out of 54 tested"), which shows stronger goal decomposition and failure recovery in practice. GPT-5.4 Nano wins tool_calling (4 vs 3): GPT ranks 18 of 54 (tied with 28) vs DeepSeek rank 47 of 54, so GPT is measurably better at selecting and sequencing function calls and arguments in our tool-calling scenarios. GPT also wins safety_calibration (3 vs 2) — rank 10 of 55 vs DeepSeek rank 12 — indicating slightly better refusal/allow behavior. The two models tie on structured_output (both 5, tied for 1st), strategic_analysis (both 5, tied for 1st), constrained_rewriting (both 4, rank 6), creative_problem_solving (both 4, rank 9), classification (both 3, rank 31), long_context (both 5, tied for 1st), persona_consistency (both 5, tied for 1st), and multilingual (both 5, tied for 1st). Notably, GPT-5.4 Nano reports an AIME 2025 score of 87.8% (Epoch AI) and ranks 8 of 23 on that external math benchmark — a supplemental signal of its numeric/problem-solving performance on that dataset; DeepSeek has no external AIME score in the payload. In short: DeepSeek offers higher faithfulness and planning in our benchmarks; GPT-5.4 Nano is stronger at tool-calling and marginally safer, with added multimodal input support.

Per-million-token pricing (input+output assumed equal volumes): DeepSeek V3.2 charges $0.26 input + $0.38 output = $0.64 per 1M input+output tokens. GPT-5.4 Nano charges $0.20 input + $1.25 output = $1.45 per 1M input+output tokens. At scale this gap matters: for 1M tokens/month the difference is $0.81; for 10M it's $8.10; for 100M it's $81.00 (DeepSeek: $64 vs GPT-5.4 Nano: $145 for 100M). Teams with high output volume (long responses, many user replies) or tight margins should care — startups, large chat providers, and high-throughput APIs will see substantial savings with DeepSeek V3.2. If you need image/file inputs or tool-heavy multimodal flows, the higher GPT-5.4 Nano cost may be justified.

Choose DeepSeek V3.2 if you prioritize factual fidelity, robust agentic planning, or want lower per-token output costs — ideal for knowledge-heavy assistants, long-context retrieval apps, and high-volume text-only deployments. Choose GPT-5.4 Nano if you need tool-calling accuracy, multimodal (text+image+file) inputs, or slightly better safety calibration — ideal for apps that orchestrate external functions, ingest images/files, or require tighter refusal behavior despite higher output cost.