DeepSeek V3.1 vs GPT-5.4 Nano

Across our 12-test suite, GPT-5.4 Nano wins 5 benchmarks outright, DeepSeek V3.1 wins 2, and the remaining 5 are ties.

Where GPT-5.4 Nano wins:

• Strategic analysis: 5 vs 4. GPT-5.4 Nano ties for 1st among 54 models on nuanced tradeoff reasoning; DeepSeek V3.1 ranks 27th of 54. This is the widest practical gap — for financial modeling, risk assessment, or executive decision support, GPT-5.4 Nano is the clearer choice.
• Constrained rewriting: 4 vs 3. GPT-5.4 Nano ranks 6th of 53 on compression within hard character limits; DeepSeek V3.1 ranks 31st of 53. For copywriting, ad copy, and character-limited outputs, GPT-5.4 Nano is noticeably better in our testing.
• Tool calling: 4 vs 3. GPT-5.4 Nano ranks 18th of 54 on function selection, argument accuracy, and sequencing; DeepSeek V3.1 ranks 47th of 54 — near the bottom of the field. This is a significant gap for agentic workflows, API orchestration, and any LLM integration where reliable tool use is critical.
• Safety calibration: 3 vs 1. GPT-5.4 Nano ranks 10th of 55 (tied with one other model); DeepSeek V3.1 ranks 32nd of 55 with a score of 1 — well below the field median of 2. For production deployments where content safety and appropriate refusals matter (e.g., consumer-facing apps), this is a meaningful structural difference.
• Multilingual: 5 vs 4. GPT-5.4 Nano ties for 1st among 55 models; DeepSeek V3.1 ranks 36th of 55. For non-English user bases or global deployments, GPT-5.4 Nano has a clear edge in our testing.

Where DeepSeek V3.1 wins:

• Faithfulness: 5 vs 4. DeepSeek V3.1 ties for 1st among 55 models (with 32 others); GPT-5.4 Nano ranks 34th of 55. For RAG pipelines, summarization, and any task where sticking closely to source material is critical, DeepSeek V3.1 holds an advantage.
• Creative problem-solving: 5 vs 4. DeepSeek V3.1 ties for 1st among 54 models (with 7 others) — a smaller tie group, making this score more meaningful; GPT-5.4 Nano ranks 9th of 54. For ideation, non-obvious solution generation, and open-ended problem-solving, DeepSeek V3.1 scores higher.

Ties (5 benchmarks): Both models score identically on structured output (5/5, tied for 1st), long context (5/5, tied for 1st), persona consistency (5/5, tied for 1st), agentic planning (4/4, rank 16 of 54), and classification (3/3, rank 31 of 53). These represent areas of genuine parity.

External benchmark note: GPT-5.4 Nano scores 87.8% on AIME 2025 (Epoch AI), ranking 8th of 23 models with that score on record — above the field median of 83.9% for models tracked on that benchmark. DeepSeek V3.1 has no external benchmark scores in the payload. This places GPT-5.4 Nano among the stronger math reasoning models by that third-party measure.

DeepSeek V3.1 costs $0.15/MTok input and $0.75/MTok output. GPT-5.4 Nano costs $0.20/MTok input and $1.25/MTok output — 33% more expensive on input, 67% more on output. Output cost dominates most real workloads, so that gap compounds fast. At 1M output tokens/month, you pay $0.75 vs $1.25 — a $0.50 difference that's easy to ignore. At 10M output tokens/month, that's $7,500 vs $12,500 — a $5,000/month gap. At 100M output tokens/month, DeepSeek V3.1 saves $50,000/month over GPT-5.4 Nano. For cost-sensitive or high-throughput applications — bulk document processing, summarization pipelines, or chat products with large user bases — that differential justifies serious consideration. GPT-5.4 Nano's higher cost only makes sense if your workload specifically requires multimodal input (image and file support, which DeepSeek V3.1 does not offer per the payload), its safety calibration performance, or its stronger tool calling.

Choose GPT-5.4 Nano if:

• Your application depends on reliable tool calling (ranks 18th vs DeepSeek V3.1's 47th of 54 in our testing) — this gap is large enough to matter for agentic or API-driven workflows.
• You need safety calibration you can depend on in production (scores 3 vs 1 in our testing; ranks 10th vs 32nd of 55).
• Your users communicate in non-English languages and quality consistency across languages is required.
• You need multimodal input — GPT-5.4 Nano supports text, image, and file inputs; DeepSeek V3.1 is text-only per the payload.
• You need a 400,000-token context window or up to 128,000 max output tokens — GPT-5.4 Nano's context capacity is substantially larger than DeepSeek V3.1's 32,768-token window and 7,168 max output tokens.
• Structured strategic reasoning or constrained rewriting quality justifies the 67% output cost premium.

Choose DeepSeek V3.1 if:

• You're running high-volume output workloads and the $0.50/MTok output savings compounds meaningfully — that's $50,000/month saved at 100M output tokens.
• Your use case centers on faithfulness to source material — RAG systems, document Q&A, citation-based tasks — where DeepSeek V3.1 scores 5/5 and ranks among the top in our testing.
• You need advanced generation control: DeepSeek V3.1 supports a broader set of parameters including temperature, top-k, top-p, min-p, frequency/presence/repetition penalties, logprobs, and reasoning modes that GPT-5.4 Nano does not expose.
• Ideation and creative problem-solving are central to your product — DeepSeek V3.1 scores 5/5 vs GPT-5.4 Nano's 4/5 in our testing.
• Your context requirements fit within 32K tokens and you don't need image or file inputs.