DeepSeek V3.1 vs GPT-5 Nano

Overview (our 12-test suite): they tie on 6 tests, DeepSeek V3.1 wins 3, GPT-5 Nano wins 3. Specifics (scores are our internal 1–5 measures unless otherwise noted):

• Faithfulness: DeepSeek V3.1 5 vs GPT-5 Nano 4. In our testing DeepSeek is tied for 1st on faithfulness ("tied for 1st with 32 other models out of 55 tested"); GPT-5 Nano ranks 34/55. This indicates DeepSeek is less likely to deviate from source material in factual tasks.
• Creative problem solving: DeepSeek V3.1 5 vs GPT-5 Nano 3. DeepSeek is tied for 1st (creative problem solving), so it's stronger at producing non-obvious, feasible ideas in our tests.
• Persona consistency: DeepSeek V3.1 5 vs GPT-5 Nano 4. DeepSeek ties for 1st on persona consistency; expect better role-holding and resistance to injection in character-driven chat.
• Tool calling: DeepSeek V3.1 3 vs GPT-5 Nano 4. GPT-5 Nano ranks 18/54 on tool calling (DeepSeek ranks 47/54), so GPT-5 Nano is measurably better at selecting functions, sequencing calls, and populating arguments in our tool-calling tests—important for agentic developer workflows.
• Safety calibration: DeepSeek V3.1 1 vs GPT-5 Nano 4. GPT-5 Nano ranks 6/55 on safety calibration versus DeepSeek at rank 32; GPT-5 Nano better balances refusals and permits in risky prompts in our testing.
• Multilingual: DeepSeek V3.1 4 vs GPT-5 Nano 5. GPT-5 Nano ties for 1st on multilingual quality; expect stronger non-English parity.
• Ties (equal scores): structured_output 5/5 (both tied for 1st), long_context 5/5 (both tied for 1st), strategic_analysis 4/4, constrained_rewriting 3/3, classification 3/3, agentic_planning 4/4. Structured_output and long_context ties mean both models handle schema compliance and 30K+ retrieval accuracy well in our suite.
• External math benchmarks (supplementary): GPT-5 Nano scores 95.2% on MATH Level 5 and 81.1% on AIME 2025 (Epoch AI). These are third-party measures and supplement our internal results; they suggest strong math performance for GPT-5 Nano but are a separate signal from our 1–5 tests. Operational implications: pick DeepSeek V3.1 when factual fidelity, creative ideation, or character consistency are priority. Pick GPT-5 Nano when you need safer refusals, reliable tool integration, broad multilingual support, multimodal inputs (text+image+file->text), or much lower per-token cost. Note context windows: DeepSeek V3.1 has a 32,768 token window; GPT-5 Nano supports 400,000 tokens—relevant for huge-document or multi-file contexts.

Costs shown are per 1,000 tokens (mTok) in the payload. Assuming equal input and output token volume (1M input + 1M output = 1,000 mTok each): DeepSeek V3.1 costs $0.15 (input) + $0.75 (output) = $0.90 per mTok, or $900 for 1M in+out tokens. GPT-5 Nano costs $0.05 + $0.40 = $0.45 per mTok, or $450 for the same 1M in+out. At 10M in+out tokens the totals are $9,000 vs $4,500; at 100M they are $90,000 vs $45,000. The payload also reports a priceRatio of 1.875. Large-volume services or price-sensitive integrations should prefer GPT-5 Nano; teams that prioritize the higher faithfulness and creative output of DeepSeek must budget roughly 2x the per-token spend under equal I/O assumptions.

Choose DeepSeek V3.1 if you need top-tier faithfulness, creative problem solving, or persona consistency in chat and are willing to pay ~2x per-token under equal input/output volumes. Choose GPT-5 Nano if you need better tool-calling, stronger safety calibration, first-rate multilingual quality, multimodal inputs, or a much lower cost for high-volume production; GPT-5 Nano also shows strong external math scores (MATH Level 5 95.2%, AIME 2025 81.1% per Epoch AI).