DeepSeek V3.2 vs GPT-4.1 Mini

Head-to-head (our 12-test suite): DeepSeek V3.2 wins five benchmarks in our testing: structured_output (5 vs 4), strategic_analysis (5 vs 4), creative_problem_solving (4 vs 3), faithfulness (5 vs 4), and agentic_planning (5 vs 4). GPT-4.1 Mini wins tool_calling (4 vs 3). The remaining six tests tie: constrained_rewriting (4), classification (3), long_context (5), safety_calibration (2), persona_consistency (5), and multilingual (5). Context and ranks:

• Structured output: DeepSeek scores 5 and is tied for 1st ("tied for 1st with 24 other models"), while GPT-4.1 Mini scores 4 (rank 26 of 54). That indicates DeepSeek is notably stronger at strict JSON/schema compliance in our tests.
• Strategic analysis & agentic planning: DeepSeek scores 5 (tied for 1st), GPT-4.1 Mini scores 4 (rank 27 and 16 respectively). For nuanced tradeoffs and goal decomposition, DeepSeek held the top-tier rank in our suite.
• Faithfulness: DeepSeek 5 (tied for 1st) vs GPT-4.1 Mini 4 (rank 34). In practice this means DeepSeek is more likely to stick to source material and avoid hallucination on the tasks we ran.
• Creative problem solving: DeepSeek 4 (rank 9) vs GPT-4.1 Mini 3 (rank 30) — DeepSeek generated more feasible, non-obvious ideas in our prompts.
• Tool calling: GPT-4.1 Mini 4 (rank 18) vs DeepSeek 3 (rank 47). If your workflows rely on function selection, precise argument formation, and sequencing, GPT-4.1 Mini performed better in that specific area.
• Ties: both models matched on long-context (5, tied for 1st), persona consistency (5), multilingual (5), constrained rewriting (4), classification (3), and safety calibration (2). For long documents, multi-language output, and persona retention, both models are equivalent in our tests. External benchmarks: GPT-4.1 Mini posts 87.3% on MATH Level 5 and 44.7% on AIME 2025 (Epoch AI) per the payload; DeepSeek V3.2 has no external math scores in the payload. Use those external figures when math-competition performance is a deciding factor.

Per the payload, DeepSeek V3.2 charges input $0.26/mTok and output $0.38/mTok; GPT-4.1 Mini charges input $0.40/mTok and output $1.60/mTok. That makes DeepSeek output 0.38/1.60 = 0.2375 (23.75%) of GPT-4.1 Mini's output cost (payload priceRatio 0.2375). To give practical totals we convert tokens → mTok using 1 mTok = 1,000 tokens (explicit conversion assumption):

• 1M tokens = 1,000 mTok → DeepSeek: (0.26+0.38)*1,000 = $640; GPT-4.1 Mini: (0.40+1.60)*1,000 = $2,000. Savings: $1,360/month.
• 10M tokens = 10,000 mTok → DeepSeek: $6,400; GPT-4.1 Mini: $20,000. Savings: $13,600/month.
• 100M tokens = 100,000 mTok → DeepSeek: $64,000; GPT-4.1 Mini: $200,000. Savings: $136,000/month. Who should care: startups, high-volume API users, and production systems that generate large output volumes will see substantial savings with DeepSeek V3.2. Teams that prioritize tool orchestration or rely on GPT-4.1 Mini’s external math benchmark strengths may justify the higher cost for niche workloads.

Choose DeepSeek V3.2 if you need top-tier structured output, strong faithfulness, strategic reasoning or creative problem solving at a much lower price (output $0.38/mTok). Ideal for APIs that produce high-volume, schema-driven responses, multilingual systems, or apps that require long contexts. Choose GPT-4.1 Mini if your primary need is reliable tool calling/function orchestration or you rely on the external math benchmarks (MATH Level 5 87.3%, AIME 2025 44.7% by Epoch AI); accept the higher per-token cost for those specific strengths.