DeepSeek V3.2 vs GPT-5 Nano

Overview: across our 12-test suite DeepSeek V3.2 wins 6 tests, GPT-5 Nano wins 2, and 4 tests tie. Details by test (score A = DeepSeek, B = GPT-5 Nano):

• Strategic analysis: DeepSeek 5 vs GPT-5 Nano 4. DeepSeek is tied for 1st ("tied for 1st with 25 other models out of 54 tested"), GPT-5 Nano ranks 27 of 54. Practical effect: DeepSeek handles nuanced tradeoff reasoning and numeric tradeoffs better in real tasks.
• Constrained rewriting: DeepSeek 4 vs GPT-5 Nano 3. DeepSeek ranks 6 of 53; GPT-5 Nano ranks 31. DeepSeek is better when you must compress content to strict limits.
• Creative problem solving: DeepSeek 4 vs GPT-5 Nano 3. DeepSeek ranks 9 of 54 vs GPT-5 Nano 30. DeepSeek generates more specific, feasible ideas in our tests.
• Faithfulness: DeepSeek 5 vs GPT-5 Nano 4. DeepSeek tied for 1st with 32 others (out of 55); GPT-5 Nano ranks 34 of 55. DeepSeek is less prone to deviating from source material in our testing.
• Persona consistency: DeepSeek 5 vs GPT-5 Nano 4. DeepSeek tied for 1st; GPT-5 Nano ranks 38 of 53. DeepSeek better preserves character and resists injection.
• Agentic planning: DeepSeek 5 vs GPT-5 Nano 4. DeepSeek tied for 1st; GPT-5 Nano ranks 16 of 54. DeepSeek better at goal decomposition and recovery in our scenarios.
• Tool calling: DeepSeek 3 vs GPT-5 Nano 4. GPT-5 Nano ranks 18 of 54 vs DeepSeek at 47 of 54. In practice GPT-5 Nano selects functions, arguments, and sequencing more accurately for developer tool flows.
• Safety calibration: DeepSeek 2 vs GPT-5 Nano 4. GPT-5 Nano ranks 6 of 55 vs DeepSeek 12 of 55. GPT-5 Nano refused harmful prompts more appropriately in our tests.
• Structured output: tie 5/5. Both tied for 1st (tied with 24 others). Both are reliable for strict JSON/schema outputs.
• Classification: tie 3/3. Both rank 31 of 53 (20 models share this score). Similar routing accuracy.
• Long context: tie 5/5. Both tied for 1st (many models share top score). Both handle 30K+ token retrieval well in our suite.
• Multilingual: tie 5/5. Both tied for 1st. Comparable multilingual quality in our tests. External math benchmarks (Epoch AI): GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (as provided in the payload). This reinforces GPT-5 Nano’s advantage on advanced math tasks in third-party measures. Bottom line: DeepSeek outperforms on higher-level reasoning, faithfulness, persona, and agentic planning; GPT-5 Nano wins developer-facing tool-calling, safety, and external math measures.

Costs in the payload are per mTok (1,000 tokens). DeepSeek V3.2: input $0.26 + output $0.38 = $0.64 per mTok. GPT-5 Nano: input $0.05 + output $0.40 = $0.45 per mTok. At 1M tokens/month (1,000 mTok) DeepSeek ≈ $640 vs GPT-5 Nano ≈ $450 (difference $190). At 10M: DeepSeek ≈ $6,400 vs GPT-5 Nano ≈ $4,500 (difference $1,900). At 100M: DeepSeek ≈ $64,000 vs GPT-5 Nano ≈ $45,000 (difference $19,000). Who should care: high-volume apps, data-heavy SaaS, and inference-heavy backends will feel the $1.9K–$19K gaps; low-volume prototypes and per-request interactive apps will be less sensitive. Note: GPT-5 Nano’s cheaper input cost (0.05 vs 0.26) drives most savings despite a slightly higher output rate (0.40 vs 0.38).

Choose DeepSeek V3.2 if you need: high-fidelity strategic analysis, strict faithfulness to sources, strong persona consistency, constrained rewriting, or agentic planning (it wins 6 of 12 internal benchmarks and ranks tied-for-1st in multiple reasoning and fidelity tests). Choose GPT-5 Nano if you need: better tool-calling for developer integrations, stronger safety calibration, multimodal inputs (text+image+file->text), or lower input costs at scale (it wins tool_calling and safety, and costs ~$0.45/mTok vs DeepSeek’s ~$0.64/mTok). If budget at high volume is critical, GPT-5 Nano saves $190 per 1M tokens; if top-tier reasoning and faithfulness matter more than that delta, pick DeepSeek.