DeepSeek V3.1 vs GPT-4.1 Nano

Summary of our 12-test suite (scores shown are from our testing):

• DeepSeek V3.1 wins strategic_analysis (4 vs 2). In practice this means better nuanced tradeoff reasoning with numbers — useful for financial or optimization prompts (ranks 27/54 for DeepSeek).

• DeepSeek wins creative_problem_solving (5 vs 2). This indicates stronger generation of non-obvious, feasible ideas in our tests (DeepSeek ties for 1st with other top models).

• DeepSeek wins long_context (5 vs 4). Retrieval and accuracy at 30K+ tokens are better on DeepSeek (DeepSeek is tied for 1st of 55 tested; GPT-4.1 Nano ranks 38/55), so multi-document summarization and large-context instructions favor DeepSeek.

• DeepSeek wins persona_consistency (5 vs 4). It resists injection and keeps character more reliably in our runs (DeepSeek tied for 1st).

• GPT-4.1 Nano wins constrained_rewriting (4 vs 3). If you must compress text under hard character limits, GPT-4.1 Nano performed better in our compression tests (rank 6 of 53).

• GPT-4.1 Nano wins tool_calling (4 vs 3). It selects functions, arguments, and sequencing more accurately in our tool-calling scenarios (GPT ranks 18 of 54; DeepSeek ranks 47 of 54).

• GPT-4.1 Nano wins safety_calibration (2 vs 1). In our safety tests GPT-4.1 Nano refused more harmful prompts appropriately (GPT rank 12/55 vs DeepSeek rank 32/55).

• Ties: structured_output (both 5), faithfulness (both 5), classification (both 3), agentic_planning (both 4), multilingual (both 4). For JSON schema and sticking to source material both models are equivalent in our testing.

Additional third-party data: GPT-4.1 Nano scores 70% on MATH Level 5 and 28.9% on AIME 2025 (Epoch AI). DeepSeek V3.1 has no external math scores in the payload. Use these Epoch AI numbers as supplementary evidence for math performance when relevant.

What this means for real tasks: choose DeepSeek for long-doc summarization, multi-step reasoning across large contexts, and creative ideation. Choose GPT-4.1 Nano when you need cheaper, faster inference, better constrained rewriting, stronger function/tool selection, or slightly better safety calibration.

Raw token costs (per 1k tokens): DeepSeek V3.1 input $0.15 / output $0.75; GPT-4.1 Nano input $0.10 / output $0.40. For 1M input tokens: DeepSeek $150 vs GPT $100. For 1M output tokens: DeepSeek $750 vs GPT $400. If you produce 1M input + 1M output tokens/month the bill is $900 (DeepSeek) vs $500 (GPT) — DeepSeek costs $400 more. Scale to 10M+10M: $9,000 vs $5,000. Scale to 100M+100M: $90,000 vs $50,000. The cost gap hits teams that generate large output volumes (long responses, high-throughput APIs). Organizations with strict budgets or latency/cost constraints should favor GPT-4.1 Nano; teams prioritizing higher long-context and creative accuracy should budget for DeepSeek V3.1’s ~1.875x price ratio.

Choose DeepSeek V3.1 if you need: large-context retrieval and fidelity (long-context score 5), high-quality creative problem-solving (score 5), and strict persona consistency — invest the higher token cost for improved reasoning and creativity. Choose GPT-4.1 Nano if you need: lower-cost at scale (input $0.10/output $0.40 per 1k), better constrained rewriting (4) and tool-calling (4), or if safety calibration and throughput matter more than top-end creative reasoning.